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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | # Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker Almost 6 months ago to the day, EleutherAI released GPT-J 6B, an open-source alternative to OpenAIs GPT-3. GPT-J 6B is the 6 billion parameter successor to EleutherAIs GPT-NEO family, a family of transformer-based language models based on the GPT architecture for text generation. EleutherAI's primary goal is to train a model that is equivalent in size to GPT-3 and make it available to the public under an open license. Over the last 6 months, GPT-J gained a lot of interest from Researchers, Data Scientists, and even Software Developers, but it remained very challenging to deploy GPT-J into production for real-world use cases and products. There are some hosted solutions to use GPT-J for production workloads, like the Hugging Face Inference API, or for experimenting using EleutherAIs 6b playground, but fewer examples on how to easily deploy it into your own environment. In this blog post, you will learn how to easily deploy GPT-J using Amazon SageMaker and the Hugging Face Inference Toolkit with a few lines of code for scalable, reliable, and secure real-time inference using a regular size GPU instance with NVIDIA T4 (~500$/m). But before we get into it, I want to explain why deploying GPT-J into production is challenging. --- | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | Default request This is an example of a default request using greedy search. Inference-time after the first request: 3s python predictor.predict({ "inputs": "Can you please let us know more details about your " }) | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | Background The weights of the 6 billion parameter model represent a ~24GB memory footprint. To load it in float32, one would need at least 2x model size CPU RAM: 1x for initial weights and another 1x to load the checkpoint. So for GPT-J it would require at least 48GB of CPU RAM to just load the model. To make the model more accessible, EleutherAI also provides float16 weights, and transformers has new options to reduce the memory footprint when loading large language models. Combining all this it should take roughly 12.1GB of CPU RAM to load the model. python from transformers import GPTJForCausalLM import torch model = GPTJForCausalLM.from_pretrained( "EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16, low_cpu_mem_usage=True ) The caveat of this example is that it takes a very long time until the model is loaded into memory and ready for use. In my experiments, it took 3 minutes and 32 seconds to load the model with the code snippet above on a P3.2xlarge AWS EC2 instance (the model was not stored on disk). This duration can be reduced by storing the model already on disk, which reduces the load time to 1 minute and 23 seconds, which is still very long for production workloads where you need to consider scaling and reliability. For example, Amazon SageMaker has a 60s limit for requests to respond, meaning the model needs to be loaded and the predictions to run within 60s, which in my opinion makes a lot of sense to keep the model/endpoint scalable and reliable for your workload. If you have longer predictions, you could use batch-transform. In Transformers the models loaded with the from_pretrained method are following PyTorch's recommended practice, which takes around 1.97 seconds for BERT [REF]. PyTorch offers an additional alternative way of saving and loading models using torch.save(model, PATH) and torch.load(PATH). “Saving a model in this way will save the entire module using Python’s pickle module. The disadvantage of this approach is that the serialized data is bound to the specific classes and the exact directory structure used when the model is saved.” This means that when we save a model with transformers==4.13.2 it could be potentially incompatible when trying to load with transformers==4.15.0. However, loading models this way reduces the loading time by ~12x, down to 0.166s for BERT. Applying this to GPT-J means that we can reduce the loading time from 1 minute and 23 seconds down to 7.7 seconds, which is ~10.5x faster. Figure 1. Model load time of BERT and GPTJ | [
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Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | philschmid | January 11, 2022 | gptj-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/gptj-sagemaker | key: children tweet: Maybe we die so we can come back as children. | [
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Boost Wav2Vec2 with n-gram LM in 🤗 Transformers | patrickvonplaten | January 12, 2022 | wav2vec2-with-ngram | research, guide, audio | https://huggingface.co/blog/wav2vec2-with-ngram | 1. Decoding audio data with Wav2Vec2 and a language model As shown in 🤗 Transformers exemple docs of Wav2Vec2, audio can be transcribed as follows. First, we install datasets and transformers. bash pip install datasets transformers Let's load a small excerpt of the Librispeech dataset to demonstrate Wav2Vec2's speech transcription capabilities. python from datasets import load_dataset dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") dataset Output: bash Reusing dataset librispeech_asr (/root/.cache/huggingface/datasets/hf-internal-testing___librispeech_asr/clean/2.1.0/f2c70a4d03ab4410954901bde48c54b85ca1b7f9bf7d616e7e2a72b5ee6ddbfc) Dataset({ features: ['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'], num_rows: 73 }) We can pick one of the 73 audio samples and listen to it. python audio_sample = dataset[2] audio_sample["text"].lower() Output: bash he tells us that at this festive season of the year with christmas and roast beef looming before us similes drawn from eating and its results occur most readily to the mind Having chosen a data sample, we now load the fine-tuned model and processor. python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-100h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-100h") Next, we process the data python inputs = processor(audio_sample["audio"]["array"], sampling_rate=audio_sample["audio"]["sampling_rate"], return_tensors="pt") forward it to the model python import torch with torch.no_grad(): logits = model(**inputs).logits and decode it python predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) transcription[0].lower() Output: bash 'he tells us that at this festive season of the year with christmaus and rose beef looming before us simalyis drawn from eating and its results occur most readily to the mind' Comparing the transcription to the target transcription above, we can see that some words sound correct, but are not spelled correctly, e.g.: - christmaus vs. christmas - rose vs. roast - simalyis vs. similes Let's see whether combining Wav2Vec2 with an n-gram lnguage model can help here. First, we need to install pyctcdecode and kenlm. bash pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode For demonstration purposes, we have prepared a new model repository patrickvonplaten/wav2vec2-base-100h-with-lm which contains the same Wav2Vec2 checkpoint but has an additional 4-gram language model for English. Instead of using Wav2Vec2Processor, this time we use Wav2Vec2ProcessorWithLM to load the 4-gram model in addition to the feature extractor and tokenizer. python from transformers import Wav2Vec2ProcessorWithLM processor = Wav2Vec2ProcessorWithLM.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm") In constrast to decoding the audio without language model, the processor now directly receives the model's output logits instead of the argmax(logits) (called predicted_ids) above. The reason is that when decoding with a language model, at each time step, the processor takes the probabilities of all possible output characters into account. Let's take a look at the dimension of the logits output. python logits.shape Output: bash torch.Size([1, 624, 32]) We can see that the logits correspond to a sequence of 624 vectors each having 32 entries. Each of the 32 entries thereby stands for the logit probability of one of the 32 possible output characters of the model: python " ".join(sorted(processor.tokenizer.get_vocab())) Output: bash "' </s> <pad> <s> <unk> A B C D E F G H I J K L M N O P Q R S T U V W X Y Z |" Intuitively, one can understand the decoding process of Wav2Vec2ProcessorWithLM as applying beam search through a matrix of size 624 $\times$ 32 probabilities while leveraging the probabilities of the next letters as given by the n-gram language model. OK, let's run the decoding step again. pyctcdecode language model decoder does not automatically convert torch tensors to numpy so we'll have to convert them ourselves before. python transcription = processor.batch_decode(logits.numpy()).text transcription[0].lower() Output: bash 'he tells us that at this festive season of the year with christmas and rose beef looming before us similes drawn from eating and its results occur most readily to the mind' Cool! Recalling the words facebook/wav2vec2-base-100h without a language model transcribed incorrectly previously, e.g., > - christmaus vs. christmas > - rose vs. roast > - simalyis vs. similes we can take another look at the transcription of facebook/wav2vec2-base-100h with a 4-gram language model. 2 out of 3 errors are corrected; christmas and similes have been correctly transcribed. Interestingly, the incorrect transcription of rose persists. However, this should not surprise us very much. Decoding audio without a language model is much more prone to yield spelling mistakes, such as christmaus or similes (those words don't exist in the English language as far as I know). This is because the speech recognition system almost solely bases its prediction on the acoustic input it was given and not really on the language modeling context of previous and successive predicted letters \( {}^1 \). If on the other hand, we add a language model, we can be fairly sure that the speech recognition system will heavily reduce spelling errors since a well-trained n-gram model will surely not predict a word that has spelling errors. But the word rose is a valid English word and therefore the 4-gram will predict this word with a probability that is not insignificant. The language model on its own most likely does favor the correct word roast since the word sequence roast beef is much more common in English than rose beef. Because the final transcription is derived from a weighted combination of facebook/wav2vec2-base-100h output probabilities and those of the n-gram language model, it is quite common to see incorrectly transcribed words such as rose. For more information on how you can tweak different parameters when decoding with Wav2Vec2ProcessorWithLM, please take a look at the official documentation here. ------------------------------------------------------------------------ \({}^1 \) Some research shows that a model such as facebook/wav2vec2-base-100h - when sufficiently large and trained on enough data - can learn language modeling dependencies between intermediate audio representations similar to a language model. Great, now that you have seen the advantages adding an n-gram language model can bring, let's dive into how to create an n-gram and Wav2Vec2ProcessorWithLM from scratch. | [
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Boost Wav2Vec2 with n-gram LM in 🤗 Transformers | patrickvonplaten | January 12, 2022 | wav2vec2-with-ngram | research, guide, audio | https://huggingface.co/blog/wav2vec2-with-ngram | === 4/5 Calculating and writing order-interpolated probabilities === Chain sizes: 1:4322496 2:87627856 3:363553620 4:728999592 5:1042486228 ----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100 | [
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Boost Wav2Vec2 with n-gram LM in 🤗 Transformers | patrickvonplaten | January 12, 2022 | wav2vec2-with-ngram | research, guide, audio | https://huggingface.co/blog/wav2vec2-with-ngram | 4. Combine an n-gram with Wav2Vec2 In a final step, we want to wrap the 5-gram into a Wav2Vec2ProcessorWithLM object to make the 5-gram boosted decoding as seamless as shown in Section 1. We start by downloading the currently "LM-less" processor of xls-r-300m-sv. python from transformers import AutoProcessor processor = AutoProcessor.from_pretrained("hf-test/xls-r-300m-sv") Next, we extract the vocabulary of its tokenizer as it represents the "labels" of pyctcdecode's BeamSearchDecoder class. python vocab_dict = processor.tokenizer.get_vocab() sorted_vocab_dict = {k.lower(): v for k, v in sorted(vocab_dict.items(), key=lambda item: item[1])} The "labels" and the previously built 5gram_correct.arpa file is all that's needed to build the decoder. python from pyctcdecode import build_ctcdecoder decoder = build_ctcdecoder( labels=list(sorted_vocab_dict.keys()), kenlm_model_path="5gram_correct.arpa", ) Output: bash Found entries of length > 1 in alphabet. This is unusual unless style is BPE, but the alphabet was not recognized as BPE type. Is this correct? Unigrams and labels don't seem to agree. We can safely ignore the warning and all that is left to do now is to wrap the just created decoder, together with the processor's tokenizer and feature_extractor into a Wav2Vec2ProcessorWithLM class. python from transformers import Wav2Vec2ProcessorWithLM processor_with_lm = Wav2Vec2ProcessorWithLM( feature_extractor=processor.feature_extractor, tokenizer=processor.tokenizer, decoder=decoder ) We want to directly upload the LM-boosted processor into the model folder of xls-r-300m-sv to have all relevant files in one place. Let's clone the repo, add the new decoder files and upload them afterward. First, we need to install git-lfs. bash sudo apt-get install git-lfs tree Cloning and uploading of modeling files can be done conveniently with the huggingface_hub's Repository class. More information on how to use the huggingface_hub to upload any files, please take a look at the official docs. python from huggingface_hub import Repository repo = Repository(local_dir="xls-r-300m-sv", clone_from="hf-test/xls-r-300m-sv") Output: bash Cloning https://huggingface.co/hf-test/xls-r-300m-sv into local empty directory. Having cloned xls-r-300m-sv, let's save the new processor with LM into it. python processor_with_lm.save_pretrained("xls-r-300m-sv") Let's inspect the local repository. The tree command conveniently can also show the size of the different files. bash tree -h xls-r-300m-sv/ Output: bash xls-r-300m-sv/ ├── [ 23] added_tokens.json ├── [ 401] all_results.json ├── [ 253] alphabet.json ├── [2.0K] config.json ├── [ 304] emissions.csv ├── [ 226] eval_results.json ├── [4.0K] language_model │ ├── [4.1G] 5gram_correct.arpa │ ├── [ 78] attrs.json │ └── [4.9M] unigrams.txt ├── [ 240] preprocessor_config.json ├── [1.2G] pytorch_model.bin ├── [3.5K] README.md ├── [4.0K] runs │ └── [4.0K] Jan09_22-00-50_brutasse │ ├── [4.0K] 1641765760.8871996 │ │ └── [4.6K] events.out.tfevents.1641765760.brutasse.31164.1 │ ├── [ 42K] events.out.tfevents.1641765760.brutasse.31164.0 │ └── [ 364] events.out.tfevents.1641794162.brutasse.31164.2 ├── [1.2K] run.sh ├── [ 30K] run_speech_recognition_ctc.py ├── [ 502] special_tokens_map.json ├── [ 279] tokenizer_config.json ├── [ 29K] trainer_state.json ├── [2.9K] training_args.bin ├── [ 196] train_results.json ├── [ 319] vocab.json └── [4.0K] wandb ├── [ 52] debug-internal.log -> run-20220109_220240-1g372i3v/logs/debug-internal.log ├── [ 43] debug.log -> run-20220109_220240-1g372i3v/logs/debug.log ├── [ 28] latest-run -> run-20220109_220240-1g372i3v └── [4.0K] run-20220109_220240-1g372i3v ├── [4.0K] files │ ├── [8.8K] conda-environment.yaml │ ├── [140K] config.yaml │ ├── [4.7M] output.log │ ├── [5.4K] requirements.txt │ ├── [2.1K] wandb-metadata.json │ └── [653K] wandb-summary.json ├── [4.0K] logs │ ├── [3.4M] debug-internal.log │ └── [8.2K] debug.log └── [113M] run-1g372i3v.wandb 9 directories, 34 files As can be seen the 5-gram LM is quite large - it amounts to more than 4 GB. To reduce the size of the n-gram and make loading faster, kenLM allows converting .arpa files to binary ones using the build_binary executable. Let's make use of it here. bash kenlm/build/bin/build_binary xls-r-300m-sv/language_model/5gram_correct.arpa xls-r-300m-sv/language_model/5gram.bin Output: bash Reading xls-r-300m-sv/language_model/5gram_correct.arpa ----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100 **************************************************************************************************** SUCCESS Great, it worked! Let's remove the .arpa file and check the size of the binary 5-gram LM. bash rm xls-r-300m-sv/language_model/5gram_correct.arpa && tree -h xls-r-300m-sv/ Output: bash xls-r-300m-sv/ ├── [ 23] added_tokens.json ├── [ 401] all_results.json ├── [ 253] alphabet.json ├── [2.0K] config.json ├── [ 304] emissions.csv ├── [ 226] eval_results.json ├── [4.0K] language_model │ ├── [1.8G] 5gram.bin │ ├── [ 78] attrs.json │ └── [4.9M] unigrams.txt ├── [ 240] preprocessor_config.json ├── [1.2G] pytorch_model.bin ├── [3.5K] README.md ├── [4.0K] runs │ └── [4.0K] Jan09_22-00-50_brutasse │ ├── [4.0K] 1641765760.8871996 │ │ └── [4.6K] events.out.tfevents.1641765760.brutasse.31164.1 │ ├── [ 42K] events.out.tfevents.1641765760.brutasse.31164.0 │ └── [ 364] events.out.tfevents.1641794162.brutasse.31164.2 ├── [1.2K] run.sh ├── [ 30K] run_speech_recognition_ctc.py ├── [ 502] special_tokens_map.json ├── [ 279] tokenizer_config.json ├── [ 29K] trainer_state.json ├── [2.9K] training_args.bin ├── [ 196] train_results.json ├── [ 319] vocab.json └── [4.0K] wandb ├── [ 52] debug-internal.log -> run-20220109_220240-1g372i3v/logs/debug-internal.log ├── [ 43] debug.log -> run-20220109_220240-1g372i3v/logs/debug.log ├── [ 28] latest-run -> run-20220109_220240-1g372i3v └── [4.0K] run-20220109_220240-1g372i3v ├── [4.0K] files │ ├── [8.8K] conda-environment.yaml │ ├── [140K] config.yaml │ ├── [4.7M] output.log │ ├── [5.4K] requirements.txt │ ├── [2.1K] wandb-metadata.json │ └── [653K] wandb-summary.json ├── [4.0K] logs │ ├── [3.4M] debug-internal.log │ └── [8.2K] debug.log └── [113M] run-1g372i3v.wandb 9 directories, 34 files Nice, we reduced the n-gram by more than half to less than 2GB now. In the final step, let's upload all files. python repo.push_to_hub(commit_message="Upload lm-boosted decoder") Output: bash Git LFS: (1 of 1 files) 1.85 GB / 1.85 GB Counting objects: 9, done. Delta compression using up to 2 threads. Compressing objects: 100% (9/9), done. Writing objects: 100% (9/9), 1.23 MiB | 1.92 MiB/s, done. Total 9 (delta 3), reused 0 (delta 0) To https://huggingface.co/hf-test/xls-r-300m-sv 27d0c57..5a191e2 main -> main That's it. Now you should be able to use the 5gram for LM-boosted decoding as shown in Section 1. As can be seen on xls-r-300m-sv's model card our 5gram LM-boosted decoder yields a WER of 18.85% on Common Voice's 7 test set which is a relative performance of ca. 30% 🔥. | [
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Boost Wav2Vec2 with n-gram LM in 🤗 Transformers | patrickvonplaten | January 12, 2022 | wav2vec2-with-ngram | research, guide, audio | https://huggingface.co/blog/wav2vec2-with-ngram | 2. Getting data for your language model A language model that is useful for a speech recognition system should support the acoustic model, e.g. Wav2Vec2, in predicting the next word (or token, letter) and therefore model the following distribution: \( \mathbf{P}(w_n | \mathbf{w}_0^{t-1}) \) with \( w_n \) being the next word and \( \mathbf{w}_0^{t-1} \) being the sequence of all previous words since the beginning of the utterance. Simply said, the language model should be good at predicting the next word given all previously transcribed words regardless of the audio input given to the speech recognition system. As always a language model is only as good as the data it is trained on. In the case of speech recognition, we should therefore ask ourselves for what kind of data, the speech recognition will be used for: conversations, audiobooks, movies, speeches, , etc, ...? The language model should be good at modeling language that corresponds to the target transcriptions of the speech recognition system. For demonstration purposes, we assume here that we have fine-tuned a pre-trained facebook/wav2vec2-xls-r-300m on Common Voice 7 in Swedish. The fine-tuned checkpoint can be found here. Common Voice 7 is a relatively crowd-sourced read-out audio dataset and we will evaluate the model on its test data. Let's now look for suitable text data on the Hugging Face Hub. We search all datasets for those that contain Swedish data. Browsing a bit through the datasets, we are looking for a dataset that is similar to Common Voice's read-out audio data. The obvious choices of oscar and mc4 might not be the most suitable here because they: - are generated from crawling the web, which might not be very clean and correspond well to spoken language - require a lot of pre-processing - are very large which is not ideal for demonstration purposes here 😉 A dataset that seems sensible here and which is relatively clean and easy to pre-process is europarl_bilingual as it's a dataset that is based on discussions and talks of the European parliament. It should therefore be relatively clean and correspond well to read-out audio data. The dataset is originally designed for machine translation and can therefore only be accessed in translation pairs. We will only extract the text of the target language, Swedish (sv), from the English-to-Swedish translations. python target_lang="sv" # change to your target lang Let's download the data. python from datasets import load_dataset dataset = load_dataset("europarl_bilingual", lang1="en", lang2=target_lang, split="train") We see that the data is quite large - it has over a million translations. Since it's only text data, it should be relatively easy to process though. Next, let's look at how the data was preprocessed when training the fine-tuned XLS-R checkpoint in Swedish. Looking at the run.sh file, we can see that the following characters were removed from the official transcriptions: python chars_to_ignore_regex = '[,?.!\-\;\:"“%‘”�—’…–]' # change to the ignored characters of your fine-tuned model Let's do the same here so that the alphabet of our language model matches the one of the fine-tuned acoustic checkpoints. We can write a single map function to extract the Swedish text and process it right away. python import re def extract_text(batch): text = batch["translation"][target_lang] batch["text"] = re.sub(chars_to_ignore_regex, "", text.lower()) return batch Let's apply the .map() function. This should take roughly 5 minutes. python dataset = dataset.map(extract_text, remove_columns=dataset.column_names) Great. Let's upload it to the Hub so that we can inspect and reuse it better. You can log in by executing the following cell. python from huggingface_hub import notebook_login notebook_login() Output: bash Login successful Your token has been saved to /root/.huggingface/token Authenticated through git-credential store but this isn't the helper defined on your machine. You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default git config --global credential.helper store Next, we call 🤗 Hugging Face's push_to_hub method to upload the dataset to the repo "sv_corpora_parliament_processed". python dataset.push_to_hub(f"{target_lang}_corpora_parliament_processed", split="train") That was easy! The dataset viewer is automatically enabled when uploading a new dataset, which is very convenient. You can now directly inspect the dataset online. Feel free to look through our preprocessed dataset directly on hf-test/sv_corpora_parliament_processed. Even if we are not a native speaker in Swedish, we can see that the data is well processed and seems clean. Next, let's use the data to build a language model. | [
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Boost Wav2Vec2 with n-gram LM in 🤗 Transformers | patrickvonplaten | January 12, 2022 | wav2vec2-with-ngram | research, guide, audio | https://huggingface.co/blog/wav2vec2-with-ngram | 3. Build an n-gram with KenLM* While large language models based on the Transformer architecture have become the standard in NLP, it is still very common to use an n-gram LM to boost speech recognition systems - as shown in Section 1. Looking again at Table 9 of Appendix C of the official Wav2Vec2 paper, it can be noticed that using a Transformer-based LM for decoding clearly yields better results than using an n-gram model, but the difference between n-gram and Transformer-based LM is much less significant than the difference between n-gram and no LM. E.g., for the large Wav2Vec2 checkpoint that was fine-tuned on 10min only, an n-gram reduces the word error rate (WER) compared to no LM by ca. 80% while a Transformer-based LM only reduces the WER by another 23% compared to the n-gram. This relative WER reduction becomes less, the more data the acoustic model has been trained on. E.g., for the large checkpoint a Transformer-based LM reduces the WER by merely 8% compared to an n-gram LM whereas the n-gram still yields a 21% WER reduction compared to no language model. The reason why an n-gram is preferred over a Transformer-based LM is that n-grams come at a significantly smaller computational cost. For an n-gram, retrieving the probability of a word given previous words is almost only as computationally expensive as querying a look-up table or tree-like data storage - i.e. it's very fast compared to modern Transformer-based language models that would require a full forward pass to retrieve the next word probabilities. For more information on how n-grams function and why they are (still) so useful for speech recognition, the reader is advised to take a look at this excellent summary from Stanford. Great, let's see step-by-step how to build an n-gram. We will use the popular KenLM library to do so. Let's start by installing the Ubuntu library prerequisites: bash sudo apt install build-essential cmake libboost-system-dev libboost-thread-dev libboost-program-options-dev libboost-test-dev libeigen3-dev zlib1g-dev libbz2-dev liblzma-dev before downloading and unpacking the KenLM repo. bash wget -O - https://kheafield.com/code/kenlm.tar.gz | tar xz KenLM is written in C++, so we'll make use of cmake to build the binaries. bash mkdir kenlm/build && cd kenlm/build && cmake .. && make -j2 ls kenlm/build/bin Great, as we can see, the executable functions have successfully been built under kenlm/build/bin/. KenLM by default computes an n-gram with Kneser-Ney smooting. All text data used to create the n-gram is expected to be stored in a text file. We download our dataset and save it as a .txt file. python from datasets import load_dataset username = "hf-test" # change to your username dataset = load_dataset(f"{username}/{target_lang}_corpora_parliament_processed", split="train") with open("text.txt", "w") as file: file.write(" ".join(dataset["text"])) Now, we just have to run KenLM's lmplz command to build our n-gram, called "5gram.arpa". As it's relatively common in speech recognition, we build a 5-gram by passing the -o 5 parameter. For more information on the different n-gram* LM that can be built with KenLM, one can take a look at the official website of KenLM. Executing the command below might take a minute or so. bash kenlm/build/bin/lmplz -o 5 <"text.txt" > "5gram.arpa" Output: bash === 1/5 Counting and sorting n-grams === Reading /content/swedish_text.txt ----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100 tcmalloc: large alloc 1918697472 bytes == 0x55d40d0f0000 @ 0x7fdccb1a91e7 0x55d40b2f17a2 0x55d40b28c51e 0x55d40b26b2eb 0x55d40b257066 0x7fdcc9342bf7 0x55d40b258baa tcmalloc: large alloc 8953896960 bytes == 0x55d47f6c0000 @ 0x7fdccb1a91e7 0x55d40b2f17a2 0x55d40b2e07ca 0x55d40b2e1208 0x55d40b26b308 0x55d40b257066 0x7fdcc9342bf7 0x55d40b258baa **************************************************************************************************** Unigram tokens 42153890 types 360209 === 2/5 Calculating and sorting adjusted counts === Chain sizes: 1:4322508 2:1062772928 3:1992699264 4:3188318720 5:4649631744 tcmalloc: large alloc 4649631744 bytes == 0x55d40d0f0000 @ 0x7fdccb1a91e7 0x55d40b2f17a2 0x55d40b2e07ca 0x55d40b2e1208 0x55d40b26b8d7 0x55d40b257066 0x7fdcc9342bf7 0x55d40b258baa tcmalloc: large alloc 1992704000 bytes == 0x55d561ce0000 @ 0x7fdccb1a91e7 0x55d40b2f17a2 0x55d40b2e07ca 0x55d40b2e1208 0x55d40b26bcdd 0x55d40b257066 0x7fdcc9342bf7 0x55d40b258baa tcmalloc: large alloc 3188326400 bytes == 0x55d695a86000 @ 0x7fdccb1a91e7 0x55d40b2f17a2 0x55d40b2e07ca 0x55d40b2e1208 0x55d40b26bcdd 0x55d40b257066 0x7fdcc9342bf7 0x55d40b258baa Statistics: 1 360208 D1=0.686222 D2=1.01595 D3+=1.33685 2 5476741 D1=0.761523 D2=1.06735 D3+=1.32559 3 18177681 D1=0.839918 D2=1.12061 D3+=1.33794 4 30374983 D1=0.909146 D2=1.20496 D3+=1.37235 5 37231651 D1=0.944104 D2=1.25164 D3+=1.344 Memory estimate for binary LM: type MB probing 1884 assuming -p 1.5 probing 2195 assuming -r models -p 1.5 trie 922 without quantization trie 518 assuming -q 8 -b 8 quantization trie 806 assuming -a 22 array pointer compression trie 401 assuming -a 22 -q 8 -b 8 array pointer compression and quantization === 3/5 Calculating and sorting initial probabilities === Chain sizes: 1:4322496 2:87627856 3:363553620 4:728999592 5:1042486228 ----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100 | [
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Boost Wav2Vec2 with n-gram LM in 🤗 Transformers | patrickvonplaten | January 12, 2022 | wav2vec2-with-ngram | research, guide, audio | https://huggingface.co/blog/wav2vec2-with-ngram | # Boosting Wav2Vec2 with n-grams in 🤗 Transformers Wav2Vec2 is a popular pre-trained model for speech recognition. Released in September 2020 by Meta AI Research, the novel architecture catalyzed progress in self-supervised pretraining for speech recognition, e.g. G. Ng et al., 2021, Chen et al, 2021, Hsu et al., 2021 and Babu et al., 2021. On the Hugging Face Hub, Wav2Vec2's most popular pre-trained checkpoint currently amounts to over 250,000 monthly downloads. Using Connectionist Temporal Classification (CTC), pre-trained Wav2Vec2-like checkpoints are extremely easy to fine-tune on downstream speech recognition tasks. In a nutshell, fine-tuning pre-trained Wav2Vec2 checkpoints works as follows: A single randomly initialized linear layer is stacked on top of the pre-trained checkpoint and trained to classify raw audio input to a sequence of letters. It does so by: 1. extracting audio representations from the raw audio (using CNN layers), 2. processing the sequence of audio representations with a stack of transformer layers, and, 3. classifying the processed audio representations into a sequence of output letters. Previously audio classification models required an additional language model (LM) and a dictionary to transform the sequence of classified audio frames to a coherent transcription. Wav2Vec2's architecture is based on transformer layers, thus giving each processed audio representation context from all other audio representations. In addition, Wav2Vec2 leverages the CTC algorithm for fine-tuning, which solves the problem of alignment between a varying "input audio length"-to-"output text length" ratio. Having contextualized audio classifications and no alignment problems, Wav2Vec2 does not require an external language model or dictionary to yield acceptable audio transcriptions. As can be seen in Appendix C of the official paper, Wav2Vec2 gives impressive downstream performances on LibriSpeech without using a language model at all. However, from the appendix, it also becomes clear that using Wav2Vec2 in combination with a language model can yield a significant improvement, especially when the model was trained on only 10 minutes of transcribed audio. Until recently, the 🤗 Transformers library did not offer a simple user interface to decode audio files with a fine-tuned Wav2Vec2 and a language model. This has thankfully changed. 🤗 Transformers now offers an easy-to-use integration with Kensho Technologies' pyctcdecode library. This blog post is a step-by-step technical guide to explain how one can create an n-gram language model and combine it with an existing fine-tuned Wav2Vec2 checkpoint using 🤗 Datasets and 🤗 Transformers. We start by: 1. How does decoding audio with an LM differ from decoding audio without an LM? 2. How to get suitable data for a language model? 3. How to build an n-gram with KenLM? 4. How to combine the n-gram with a fine-tuned Wav2Vec2 checkpoint? For a deep dive into how Wav2Vec2 functions - which is not necessary for this blog post - the reader is advised to consult the following material: - wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations - Fine-Tune Wav2Vec2 for English ASR with 🤗 Transformers - An Illustrated Tour of Wav2vec 2.0 | [
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Boost Wav2Vec2 with n-gram LM in 🤗 Transformers | patrickvonplaten | January 12, 2022 | wav2vec2-with-ngram | research, guide, audio | https://huggingface.co/blog/wav2vec2-with-ngram | === 5/5 Writing ARPA model === ----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100 **************************************************************************************************** Name:lmplz VmPeak:14181536 kB VmRSS:2199260 kB RSSMax:4160328 kB user:120.598 sys:26.6659 CPU:147.264 real:136.344 Great, we have built a 5-gram LM! Let's inspect the first couple of lines. bash head -20 5gram.arpa Output: bash \data\ ngram 1=360208 ngram 2=5476741 ngram 3=18177681 ngram 4=30374983 ngram 5=37231651 \1-grams: -6.770219 <unk> 0 0 <s> -0.11831701 -4.6095004 återupptagande -1.2174699 -2.2361007 av -0.79668784 -4.8163533 sessionen -0.37327805 -2.2251768 jag -1.4205662 -4.181505 förklarar -0.56261665 -3.5790775 europaparlamentets -0.63611007 -4.771945 session -0.3647111 -5.8043895 återupptagen -0.3058712 -2.8580177 efter -0.7557702 -5.199537 avbrottet -0.43322718 There is a small problem that 🤗 Transformers will not be happy about later on. The 5-gram correctly includes a "Unknown" or <unk>, as well as a begin-of-sentence, <s> token, but no end-of-sentence, </s> token. This sadly has to be corrected currently after the build. We can simply add the end-of-sentence token by adding the line 0 </s> -0.11831701 below the begin-of-sentence token and increasing the ngram 1 count by 1. Because the file has roughly 100 million lines, this command will take ca. 2 minutes. python with open("5gram.arpa", "r") as read_file, open("5gram_correct.arpa", "w") as write_file: has_added_eos = False for line in read_file: if not has_added_eos and "ngram 1=" in line: count=line.strip().split("=")[-1] write_file.write(line.replace(f"{count}", f"{int(count)+1}")) elif not has_added_eos and "<s>" in line: write_file.write(line) write_file.write(line.replace("<s>", "</s>")) has_added_eos = True else: write_file.write(line) Let's now inspect the corrected 5-gram. bash head -20 5gram_correct.arpa Output: bash \data\ ngram 1=360209 ngram 2=5476741 ngram 3=18177681 ngram 4=30374983 ngram 5=37231651 \1-grams: -6.770219 <unk> 0 0 <s> -0.11831701 0 </s> -0.11831701 -4.6095004 återupptagande -1.2174699 -2.2361007 av -0.79668784 -4.8163533 sessionen -0.37327805 -2.2251768 jag -1.4205662 -4.181505 förklarar -0.56261665 -3.5790775 europaparlamentets -0.63611007 -4.771945 session -0.3647111 -5.8043895 återupptagen -0.3058712 -2.8580177 efter -0.7557702 Great, this looks better! We're done at this point and all that is left to do is to correctly integrate the "ngram" with pyctcdecode and 🤗 Transformers. | [
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Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs | philschmid | January 13, 2022 | infinity-cpu-performance | analysis | https://huggingface.co/blog/infinity-cpu-performance | Introduction Transfer learning has changed Machine Learning by reaching new levels of accuracy from Natural Language Processing (NLP) to Audio and Computer Vision tasks. At Hugging Face, we work hard to make these new complex models and large checkpoints as easily accessible and usable as possible. But while researchers and data scientists have converted to the new world of Transformers, few companies have been able to deploy these large, complex models in production at scale. The main bottleneck is the latency of predictions which can make large deployments expensive to run and real-time use cases impractical. Solving this is a difficult engineering challenge for any Machine Learning Engineering team and requires the use of advanced techniques to optimize models all the way down to the hardware. With Hugging Face Infinity, we offer a containerized solution that makes it easy to deploy low-latency, high-throughput, hardware-accelerated inference pipelines for the most popular Transformer models. Companies can get both the accuracy of Transformers and the efficiency necessary for large volume deployments, all in a simple to use package. In this blog post, we want to share detailed performance results for Infinity running on the latest generation of Intel Xeon CPU, to achieve optimal cost, efficiency, and latency for your Transformer deployments. | [
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Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs | philschmid | January 13, 2022 | infinity-cpu-performance | analysis | https://huggingface.co/blog/infinity-cpu-performance | Methodologies When it comes to benchmarking BERT-like models, two metrics are most adopted: * Latency: Time it takes for a single prediction of the model (pre-process, prediction, post-process) * Throughput: Number of executions performed in a fixed amount of time for one benchmark configuration, respecting Physical CPU cores, Sequence Length, and Batch Size These two metrics will be used to benchmark Hugging Face Infinity across different setups to understand the benefits and tradeoffs in this blog post. --- | [
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Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs | philschmid | January 13, 2022 | infinity-cpu-performance | analysis | https://huggingface.co/blog/infinity-cpu-performance | Latency Below, you can find the latency results for an experiment running Hugging Face Infinity on 2 Physical Cores with Batch Size 1. It is remarkable to see how robust and constant Infinity is, with minimal deviation for p95, p99, or p100 (max latency). This result is confirmed for other experiments as well in the benchmark. Figure 4. Latency (Batch=1, Physical Cores=2) --- | [
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Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs | philschmid | January 13, 2022 | infinity-cpu-performance | analysis | https://huggingface.co/blog/infinity-cpu-performance | Conclusion In this post, we showed how Hugging Face Infinity performs on the new Intel Ice Lake Xeon CPU. We created a detailed benchmark with over 190 different configurations sharing the results you can expect when using Hugging Face Infinity on CPU, what would be the best configuration to optimize your Infinity Container for latency, and what would be the best configuration to maximize throughput. Hugging Face Infinity can deliver up to 800% higher throughput compared to vanilla transformers, and down to 1-4ms latency for sequence lengths up to 64 tokens. The flexibility to optimize transformer models for throughput, latency, or both enables businesses to either reduce the amount of infrastructure cost for the same workload or to enable real-time use cases that were not possible before. If you are interested in trying out Hugging Face Infinity sign up for your trial at hf.co/infinity-trial | [
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Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs | philschmid | January 13, 2022 | infinity-cpu-performance | analysis | https://huggingface.co/blog/infinity-cpu-performance | Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs December 2022 Update: Infinity is no longer offered by Hugging Face as a commercial inference solution. To deploy and accelerate your models, we recommend the following new solutions: * Inference Endpoints to easily deploy models on dedicated infrastructure managed by Hugging Face. * Our open-source optimization libraries, 🤗 Optimum Intel and 🤗 Optimum ONNX Runtime, to get the highest efficiency out of training and running models for inference. * Hugging Face Expert Acceleration Program, a commercial service for Hugging Face experts to work directly with your team to accelerate your Machine Learning roadmap and models. | [
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Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs | philschmid | January 13, 2022 | infinity-cpu-performance | analysis | https://huggingface.co/blog/infinity-cpu-performance | Benchmark Inference performance benchmarks often only measure the execution of the model. In this blog post, and when discussing the performance of Infinity, we always measure the end-to-end pipeline including pre-processing, prediction, post-processing. Please keep this in mind when comparing these results with other latency measurements. Figure 2. Infinity End-to-End Pipeline | [
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Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs | philschmid | January 13, 2022 | infinity-cpu-performance | analysis | https://huggingface.co/blog/infinity-cpu-performance | Environment As a benchmark environment, we are going to use the Amazon EC2 C6i instances, which are compute-optimized instances powered by the 3rd generation of Intel Xeon Scalable processors. These new Intel-based instances are using the ice-lake Process Technology and support Intel AVX-512, Intel Turbo Boost, and Intel Deep Learning Boost. In addition to superior performance for machine learning workloads, the Intel Ice Lake C6i instances offer great cost-performance and are our recommendation to deploy Infinity on Amazon Web Services. To learn more, visit the EC2 C6i instance page. | [
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Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs | philschmid | January 13, 2022 | infinity-cpu-performance | analysis | https://huggingface.co/blog/infinity-cpu-performance | Throughput Below you can find the throughput comparison for running infinity on 2 physical cores with batch size 1, compared with vanilla transformers. Figure 3. Throughput: Infinity vs Transformers | Sequence Length | Infinity | Transformers | improvement | |-----------------|-------------|--------------|-------------| | 8 | 248 req/sec | 49 req/sec | +506% | | 16 | 212 req/sec | 50 req/sec | +424% | | 32 | 150 req/sec | 40 req/sec | +375% | | 64 | 97 req/sec | 28 req/sec | +346% | | 128 | 55 req/sec | 18 req/sec | +305% | | 256 | 27 req/sec | 9 req/sec | +300% | | 384 | 17 req/sec | 5 req/sec | +340% | | 512 | 12 req/sec | 4 req/sec | +300% | | [
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Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs | philschmid | January 13, 2022 | infinity-cpu-performance | analysis | https://huggingface.co/blog/infinity-cpu-performance | Results To run the benchmark, we created an infinity container for the EC2 C6i instance (Ice-lake) and optimized a DistilBERT model for sequence classification using Infinity Multiverse. This ice-lake optimized Infinity Container can achieve up to 34% better latency & throughput compared to existing cascade-lake-based instances, and up to 800% better latency & throughput compared to vanilla transformers running on ice-lake. The Benchmark we created consists of 192 different experiments and configurations. We ran experiments for: * Physical CPU cores: 1, 2, 4, 8 * Sequence length: 8, 16, 32, 64, 128, 256, 384, 512 * Batch_size: 1, 2, 4, 8, 16, 32 In each experiment, we collect numbers for: * Throughput (requests per second) * Latency (min, max, avg, p90, p95, p99) You can find the full data of the benchmark in this google spreadsheet: 🤗 Infinity: CPU Ice-Lake Benchmark. In this blog post, we will highlight a few results of the benchmark including the best latency and throughput configurations. In addition to this, we deployed the DistilBERT model we used for the benchmark as an API endpoint on 2 physical cores. You can test it and get a feeling for the performance of Infinity. Below you will find a curl command on how to send a request to the hosted endpoint. The API returns a x-compute-time HTTP Header, which contains the duration of the end-to-end pipeline. bash curl --request POST `-i` \ --url https://infinity.huggingface.co/cpu/distilbert-base-uncased-emotion \ --header 'Content-Type: application/json' \ --data '{"inputs":"I like you. I love you"}' | [
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Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs | philschmid | January 13, 2022 | infinity-cpu-performance | analysis | https://huggingface.co/blog/infinity-cpu-performance | What is Hugging Face Infinity Hugging Face Infinity is a containerized solution for customers to deploy end-to-end optimized inference pipelines for State-of-the-Art Transformer models, on any infrastructure. Hugging Face Infinity consists of 2 main services: * The Infinity Container is a hardware-optimized inference solution delivered as a Docker container. * Infinity Multiverse is a Model Optimization Service through which a Hugging Face Transformer model is optimized for the Target Hardware. Infinity Multiverse is compatible with Infinity Container. The Infinity Container is built specifically to run on a Target Hardware architecture and exposes an HTTP /predict endpoint to run inference. Figure 1. Infinity Overview An Infinity Container is designed to serve 1 Model and 1 Task. A Task corresponds to machine learning tasks as defined in the Transformers Pipelines documentation. As of the writing of this blog post, supported tasks include feature extraction/document embedding, ranking, sequence classification, and token classification. You can find more information about Hugging Face Infinity at hf.co/infinity, and if you are interested in testing it for yourself, you can sign up for a free trial at hf.co/infinity-trial. --- | [
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Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs | philschmid | January 13, 2022 | infinity-cpu-performance | analysis | https://huggingface.co/blog/infinity-cpu-performance | Resources * Hugging Face Infinity * Hugging Face Infinity Trial * Amazon EC2 C6i instances * DistilBERT * DistilBERT paper * DistilBERT model * 🤗 Infinity: CPU Ice-Lake Benchmark | [
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Welcome Stable-baselines3 to the Hugging Face Hub 🤗 | ThomasSimonini | January 21, 2022 | sb3 | open-source-collab, rl | https://huggingface.co/blog/sb3 | filename = name of the model zip file from the repository including the extension .zip checkpoint = load_from_hub( repo_id="sb3/demo-hf-CartPole-v1", filename="ppo-CartPole-v1.zip", ) model = PPO.load(checkpoint) # Evaluate the agent and watch it eval_env = gym.make("CartPole-v1") mean_reward, std_reward = evaluate_policy( model, eval_env, render=True, n_eval_episodes=5, deterministic=True, warn=False ) print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") | [
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Welcome Stable-baselines3 to the Hugging Face Hub 🤗 | ThomasSimonini | January 21, 2022 | sb3 | open-source-collab, rl | https://huggingface.co/blog/sb3 | filename: the name of the file == "name" inside model.save("ppo-CartPole-v1") push_to_hub( repo_id="ThomasSimonini/demo-hf-CartPole-v1", filename="ppo-CartPole-v1.zip", commit_message="Added Cartpole-v1 model trained with PPO", ) `````` Try it out and share your models with the community! | [
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Welcome Stable-baselines3 to the Hugging Face Hub 🤗 | ThomasSimonini | January 21, 2022 | sb3 | open-source-collab, rl | https://huggingface.co/blog/sb3 | Finding Models We’re currently uploading saved models of agents playing Space Invaders, Breakout, LunarLander and more. On top of this, you can find all stable-baselines-3 models from the community here When you found the model you need, you just have to copy the repository id: | [
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Welcome Stable-baselines3 to the Hugging Face Hub 🤗 | ThomasSimonini | January 21, 2022 | sb3 | open-source-collab, rl | https://huggingface.co/blog/sb3 | Download a model from the Hub The coolest feature of this integration is that you can now very easily load a saved model from Hub to Stable-baselines3. In order to do that you just need to copy the repo-id that contains your saved model and the name of the saved model zip file in the repo. For instancesb3/demo-hf-CartPole-v1: python import gym from huggingface_sb3 import load_from_hub from stable_baselines3 import PPO from stable_baselines3.common.evaluation import evaluate_policy # Retrieve the model from the hub | [
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Welcome Stable-baselines3 to the Hugging Face Hub 🤗 | ThomasSimonini | January 21, 2022 | sb3 | open-source-collab, rl | https://huggingface.co/blog/sb3 | Conclusion We're excited to see what you're working on with Stable-baselines3 and try your models in the Hub 😍. And we would love to hear your feedback 💖. 📧 Feel free to [reach us](mailto:[email protected]). Finally, we would like to thank the SB3 team and in particular [Antonin Raffin](https://araffin.github.io/) for their precious help for the integration of the library 🤗. | [
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Welcome Stable-baselines3 to the Hugging Face Hub 🤗 | ThomasSimonini | January 21, 2022 | sb3 | open-source-collab, rl | https://huggingface.co/blog/sb3 | # Welcome Stable-baselines3 to the Hugging Face Hub 🤗 At Hugging Face, we are contributing to the ecosystem for Deep Reinforcement Learning researchers and enthusiasts. That’s why we’re happy to announce that we integrated Stable-Baselines3 to the Hugging Face Hub. Stable-Baselines3 is one of the most popular PyTorch Deep Reinforcement Learning library that makes it easy to train and test your agents in a variety of environments (Gym, Atari, MuJoco, Procgen...). With this integration, you can now host your saved models 💾 and load powerful models from the community. In this article, we’re going to show how you can do it. | [
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Welcome Stable-baselines3 to the Hugging Face Hub 🤗 | ThomasSimonini | January 21, 2022 | sb3 | open-source-collab, rl | https://huggingface.co/blog/sb3 | Installation To use stable-baselines3 with Hugging Face Hub, you just need to install these 2 libraries: bash pip install huggingface_hub pip install huggingface_sb3 | [
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Welcome Stable-baselines3 to the Hugging Face Hub 🤗 | ThomasSimonini | January 21, 2022 | sb3 | open-source-collab, rl | https://huggingface.co/blog/sb3 | Sharing a model to the Hub In just a minute, you can get your saved model in the Hub. First, you need to be logged in to Hugging Face to upload a model: - If you're using Colab/Jupyter Notebooks: python from huggingface_hub import notebook_login notebook_login() - Else: bash huggingface-cli login Then, in this example, we train a PPO agent to play CartPole-v1 and push it to a new repo ThomasSimonini/demo-hf-CartPole-v1 `````python from huggingface_sb3 import push_to_hub from stable_baselines3 import PPO # Define a PPO model with MLP policy network model = PPO("MlpPolicy", "CartPole-v1", verbose=1) # Train it for 10000 timesteps model.learn(total_timesteps=10_000) # Save the model model.save("ppo-CartPole-v1") # Push this saved model to the hf repo # If this repo does not exists it will be created | [
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Welcome Stable-baselines3 to the Hugging Face Hub 🤗 | ThomasSimonini | January 21, 2022 | sb3 | open-source-collab, rl | https://huggingface.co/blog/sb3 | What's next? In the coming weeks and months, we will be extending the ecosystem by: - Integrating [RL-baselines3-zoo](https://github.com/DLR-RM/rl-baselines3-zoo) - Uploading [RL-trained-agents models](https://github.com/DLR-RM/rl-trained-agents/tree/master) into the Hub: a big collection of pre-trained Reinforcement Learning agents using stable-baselines3 - Integrating other Deep Reinforcement Learning libraries - Implementing Decision Transformers 🔥 - And more to come 🥳 The best way to keep in touch is to [join our discord server](https://discord.gg/YRAq8fMnUG) to exchange with us and with the community. And if you want to dive deeper, we wrote a tutorial where you’ll learn: - How to train a Deep Reinforcement Learning lander agent to land correctly on the Moon 🌕 - How to upload it to the Hub 🚀  - How to download and use a saved model from the Hub that plays Space Invaders 👾.  👉 [The tutorial](https://github.com/huggingface/huggingface_sb3/blob/main/Stable_Baselines_3_and_Hugging_Face_%F0%9F%A4%97_tutorial.ipynb) | [
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] |
Welcome Stable-baselines3 to the Hugging Face Hub 🤗 | ThomasSimonini | January 21, 2022 | sb3 | open-source-collab, rl | https://huggingface.co/blog/sb3 | Would you like to integrate your library to the Hub? This integration is possible thanks to the [huggingface_hub`](https://github.com/huggingface/huggingface_hub) library which has all our widgets and the API for all our supported libraries. If you would like to integrate your library to the Hub, we have a guide for you! | [
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Welcome Stable-baselines3 to the Hugging Face Hub 🤗 | ThomasSimonini | January 21, 2022 | sb3 | open-source-collab, rl | https://huggingface.co/blog/sb3 | repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name}) | [
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Supercharged Searching on the Hugging Face Hub | muellerzr | January 25, 2022 | searching-the-hub | guide | https://huggingface.co/blog/searching-the-hub | # Supercharged Searching on the Hugging Face Hub The huggingface_hub library is a lightweight interface that provides a programmatic approach to exploring the hosting endpoints Hugging Face provides: models, datasets, and Spaces. Up until now, searching on the Hub through this interface was tricky to pull off, and there were many aspects of it a user had to "just know" and get accustomed to. In this article, we will be looking at a few exciting new features added to huggingface_hub to help lower that bar and provide users with a friendly API to search for the models and datasets they want to use without leaving their Jupyter or Python interfaces. > Before we begin, if you do not have the latest version of the huggingface_hub library on your system, please run the following cell: python !pip install huggingface_hub -U | [
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Supercharged Searching on the Hugging Face Hub | muellerzr | January 25, 2022 | searching-the-hub | guide | https://huggingface.co/blog/searching-the-hub | Taking it up a Notch We saw how we could use the ModelSearchArguments and DatasetSearchArguments to remove the guesswork from when we want to search the Hub, but what about if we have a very complex, messy query? Such as: I want to search for all models trained for both text-classification and zero-shot classification, were trained on the Multi NLI and GLUE datasets, and are compatible with both PyTorch and TensorFlow (a more exact query to get the above model). To setup this query, we'll make use of the ModelFilter class. It's designed to handle these types of situations, so we don't need to scratch our heads: python >>> filt = ModelFilter( >>> task = ["text-classification", "zero-shot-classification"], >>> trained_dataset = [model_args.dataset.multi_nli, model_args.dataset.glue], >>> library = ['pytorch', 'tensorflow'] >>> ) >>> api.list_models(filt) [ModelInfo: { modelId: Jiva/xlm-roberta-large-it-mnli sha: c6e64469ec4aa17fedbd1b2522256f90a90b5b86 lastModified: 2021-12-10T14:56:38.000Z tags: ['pytorch', 'xlm-roberta', 'text-classification', 'it', 'dataset:multi_nli', 'dataset:glue', 'arxiv:1911.02116', 'transformers', 'tensorflow', 'license:mit', 'zero-shot-classification'] pipeline_tag: zero-shot-classification siblings: [ModelFile(rfilename='.gitattributes'), ModelFile(rfilename='README.md'), ModelFile(rfilename='config.json'), ModelFile(rfilename='pytorch_model.bin'), ModelFile(rfilename='sentencepiece.bpe.model'), ModelFile(rfilename='special_tokens_map.json'), ModelFile(rfilename='tokenizer.json'), ModelFile(rfilename='tokenizer_config.json')] config: None private: False downloads: 680 library_name: transformers likes: 1 }] Very quickly we see that it's a much more coordinated approach for searching through the API, with no added headache for you! | [
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Supercharged Searching on the Hugging Face Hub | muellerzr | January 25, 2022 | searching-the-hub | guide | https://huggingface.co/blog/searching-the-hub | Situating the Problem: First, let's imagine the scenario you are in. You'd like to find all models hosted on the Hugging Face Hub for Text Classification, were trained on the GLUE dataset, and are compatible with PyTorch. You may simply just open https://huggingface.co/models and use the widgets on there. But this requires leaving your IDE and scanning those results, all of which requires a few button clicks to get you the information you need. What if there were a solution to this without having to leave your IDE? With a programmatic interface, it also could be easy to see this being integrated into workflows for exploring the Hub. This is where the huggingface_hub comes in. For those familiar with the library, you may already know that we can search for these type of models. However, getting the query right is a painful process of trial and error. Could we simplify that? Let's find out! | [
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Supercharged Searching on the Hugging Face Hub | muellerzr | January 25, 2022 | searching-the-hub | guide | https://huggingface.co/blog/searching-the-hub | Concluding thoughts Hopefully by now you have a brief understanding of how this new searching API can directly impact your workflow and exploration of the Hub! Along with this, perhaps you know of a place in your code where the AttributeDictionary might be useful for you to use. From here, make sure to check out the official documentation on Searching the Hub Efficiently and don't forget to give us a star! | [
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Supercharged Searching on the Hugging Face Hub | muellerzr | January 25, 2022 | searching-the-hub | guide | https://huggingface.co/blog/searching-the-hub | What is the magic? Very briefly we'll talk about the underlying magic at play that gives us this enum-dictionary-like datatype, the AttributeDictionary. Heavily inspired by the AttrDict class from the fastcore library, the general idea is we take a normal dictionary and supercharge it for exploratory programming by providing tab-completion for every key in the dictionary. As we saw earlier, this gets even stronger when we have nested dictionaries we can explore through, such as model_args.dataset.glue! > For those familiar with JavaScript, we mimic how the object class is working. This simple utility class can provide a much more user-focused experience when exploring nested datatypes and trying to understand what is there, such as the return of an API request! As mentioned before, we expand on the AttrDict in a few key ways: - You can delete keys with del model_args[key] or with del model_args.key - That clean __repr__ we saw earlier One very important concept to note though, is that if a key contains a number or special character it must be indexed as a dictionary, and not as an object. python >>> from huggingface_hub.utils.endpoint_helpers import AttributeDictionary A very brief example of this is if we have an AttributeDictionary with a key of 3_c: python >>> d = {"a":2, "b":3, "3_c":4} >>> ad = AttributeDictionary(d) python >>> # As an attribute >>> ad.3_c File "", line 2 ad.3_c ^ SyntaxError: invalid token python >>> # As a dictionary key >>> ad["3_c"] 4 | [
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Supercharged Searching on the Hugging Face Hub | muellerzr | January 25, 2022 | searching-the-hub | guide | https://huggingface.co/blog/searching-the-hub | Finding what we need First we'll import the HfApi, which is a class that helps us interact with the backend hosting for Hugging Face. We can interact with the models, datasets, and more through it. Along with this, we'll import a few helper classes: the ModelFilter and ModelSearchArguments python from huggingface_hub import HfApi, ModelFilter, ModelSearchArguments api = HfApi() These two classes can help us frame a solution to our above problem. The ModelSearchArguments class is a namespace-like one that contains every single valid parameter we can search for! Let's take a peek: python >>> model_args = ModelSearchArguments() >>> model_args Available Attributes or Keys: * author * dataset * language * library * license * model_name * pipeline_tag We can see a variety of attributes available to us (more on how this magic is done later). If we were to categorize what we wanted, we could likely separate them out as: - pipeline_tag (or task): Text Classification - dataset: GLUE - library: PyTorch Given this separation, it would make sense that we would find them within our model_args we've declared: python >>> model_args.pipeline_tag.TextClassification 'text-classification' python >>> model_args.dataset.glue 'dataset:glue' python >>> model_args.library.PyTorch 'pytorch' What we begin to notice though is some of the convience wrapping we perform here. ModelSearchArguments (and the complimentary DatasetSearchArguments) have a human-readable interface with formatted outputs the API wants, such as how the GLUE dataset should be searched with dataset:glue. This is key because without this "cheat sheet" of knowing how certain parameters should be written, you can very easily sit in frustration as you're trying to search for models with the API! Now that we know what the right parameters are, we can search the API easily: python >>> models = api.list_models(filter = ( >>> model_args.pipeline_tag.TextClassification, >>> model_args.dataset.glue, >>> model_args.library.PyTorch) >>> ) >>> print(len(models)) 140 We find that there were 140 matching models that fit our criteria! (at the time of writing this). And if we take a closer look at one, we can see that it does indeed look right: python >>> models[0] ModelInfo: { modelId: Jiva/xlm-roberta-large-it-mnli sha: c6e64469ec4aa17fedbd1b2522256f90a90b5b86 lastModified: 2021-12-10T14:56:38.000Z tags: ['pytorch', 'xlm-roberta', 'text-classification', 'it', 'dataset:multi_nli', 'dataset:glue', 'arxiv:1911.02116', 'transformers', 'tensorflow', 'license:mit', 'zero-shot-classification'] pipeline_tag: zero-shot-classification siblings: [ModelFile(rfilename='.gitattributes'), ModelFile(rfilename='README.md'), ModelFile(rfilename='config.json'), ModelFile(rfilename='pytorch_model.bin'), ModelFile(rfilename='sentencepiece.bpe.model'), ModelFile(rfilename='special_tokens_map.json'), ModelFile(rfilename='tokenizer.json'), ModelFile(rfilename='tokenizer_config.json')] config: None private: False downloads: 680 library_name: transformers likes: 1 } It's a bit more readable, and there's no guessing involved with "Did I get this parameter right?" > Did you know you can also get the information of this model programmatically with its model ID? Here's how you would do it: > python > api.model_info('Jiva/xlm-roberta-large-it-mnli') > | [
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Making automatic speech recognition work on large files with Wav2Vec2 in 🤗 Transformers | Narsil | February 1, 2022 | asr-chunking | guide, research, audio | https://huggingface.co/blog/asr-chunking | # Making automatic speech recognition work on large files with Wav2Vec2 in 🤗 Transformers Tl;dr: This post explains how to use the specificities of the Connectionist Temporal Classification (CTC) architecture in order to achieve very good quality automatic speech recognition (ASR) even on arbitrarily long files or during live inference. Wav2Vec2 is a popular pre-trained model for speech recognition. Released in September 2020 by Meta AI Research, the novel architecture catalyzed progress in self-supervised pretraining for speech recognition, e.g. G. Ng et al., 2021, Chen et al, 2021, Hsu et al., 2021 and Babu et al., 2021. On the Hugging Face Hub, Wav2Vec2's most popular pre-trained checkpoint currently amounts to over 250,000 monthly downloads. Wav2Vec2 is at its core a transformers models and one caveat of transformers is that it usually has a finite amount of sequence length it can handle. Either because it uses position encodings (not the case here) or simply because the cost of attention in transformers is actually O(n²) in sequence_length, meaning that using very large sequence_length explodes in complexity/memory. So you cannot run with finite hardware (even a very large GPU like A100), simply run Wav2Vec2 on an hour long file. Your program will crash. Let's try it ! bash pip install transformers python from transformers import pipeline # This will work on any of the thousands of models at # https://huggingface.co/models?pipeline_tag=automatic-speech-recognition pipe = pipeline(model="facebook/wav2vec2-base-960h") # The Public Domain LibriVox file used for the test #!wget https://ia902600.us.archive.org/8/items/thecantervilleghostversion_2_1501_librivox/thecantervilleghostversion2_01_wilde_128kb.mp3 -o very_long_file.mp3 pipe("very_long_file.mp3") # Crash out of memory ! pipe("very_long_file.mp3", chunk_length_s=10) # This works and prints a very long string ! # This whole blogpost will explain how to make things work Simple Chunking --------------- The simplest way to achieve inference on very long files would be to simply chunk the initial audio into shorter samples, let's say 10 seconds each, run inference on those, and end up with a final reconstruction. This is efficient computationally but usually leads to subpar results, the reason being that in order to do good inference, the model needs some context, so around the chunking border, inference tends to be of poor quality. Look at the following diagram: There are ways to try and work around the problem in a general fashion, but they are never entirely robust. You can try to chunk only when you encounter silence but you may have a non silent audio for a long time (a song, or noisy café audio). You can also try to cut only when there's no voice but it requires another model and this is not an entirely solved problem. You could also have a continous voice for a very long time. As it turns out, CTC structure, which is used by Wav2Vec2, can be exploited in order to achieve very robust speech recognition even on very long files without falling into those pitfalls. Chunking with stride -------------------- Wav2Vec2 uses the CTC algorithm, which means that every frame of audio is mapped to a single letter prediction (logit). That's the main feature we're going to use in order to add a stride. This link explains it in the image context, but it's the same concept for audio. Because of this property, we can: - Start doing inference on overlapping chunks so that the model actually has proper context in the center. - Drop the inferenced logits on the side. - Chain the logits without their dropped sides to recover something extremely close to what the model would have predicted on the full length audio. This is not technically 100% the same thing as running the model on the whole file so it is not enabled by default, but as you saw in the earlier example you need only to add chunk_length_s to your pipeline for it to work. In practice, we observed that most of the bad inference is kept within the strides, which get dropped before inference, leading to a proper inference of the full text. Let's note that you can choose every argument of this technique: python from transformers import pipeline pipe = pipeline(model="facebook/wav2vec2-base-960h") # stride_length_s is a tuple of the left and right stride length. # With only 1 number, both sides get the same stride, by default # the stride_length on one side is 1/6th of the chunk_length_s output = pipe("very_long_file.mp3", chunk_length_s=10, stride_length_s=(4, 2)) Chunking with stride on LM augmented models ------------------------------------------- In transformers, we also added support for adding LM to Wav2Vec2 in order to boost the WER performance of the models without even finetuning. See this excellent blogpost explaining how it works. It turns out, that the LM works directly on the logits themselves, so we can actually apply the exact same technique as before without any modification ! So chunking large files on these LM boosted models still works out of the box. Live inference -------------- A very nice perk of using a CTC model like Wav2vec2, is that it is a single pass model, so it is very fast. Especially on GPU. We can exploit that in order to do live inference. The principle is exactly the same as regular striding, but this time we can feed the pipeline data as it is coming in and simply use striding on full chunks of length 10s for instance with 1s striding to get proper context. That requires running much more inference steps than simple file chunking, but it can make the live experience much better because the model can print things as you are speaking, without having to wait for X seconds before seeing something displayed. | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | 1. Activate GPU and Install Dependencies As a first step, let's set up Google Colab to use a GPU (instead of CPU) to train the model much faster. You can do this by going to the menu, clicking on 'Runtime' > 'Change runtime type', and selecting 'GPU' as the Hardware accelerator. Once you do this, you should check if GPU is available on our notebook by running the following code: python import torch torch.cuda.is_available() Then, install the libraries you will be using in this tutorial: python !pip install datasets transformers huggingface_hub You should also install git-lfs to use git in our model repository: python !apt-get install git-lfs | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | 3. Training the model Now that the preprocessing is done, you can go ahead and train your model 🚀 You will be throwing away the pretraining head of the DistilBERT model and replacing it with a classification head fine-tuned for sentiment analysis. This enables you to transfer the knowledge from DistilBERT to your custom model 🔥 For training, you will be using the Trainer API, which is optimized for fine-tuning Transformers🤗 models such as DistilBERT, BERT and RoBERTa. First, let's define DistilBERT as your base model: python from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2) Then, let's define the metrics you will be using to evaluate how good is your fine-tuned model (accuracy and f1 score): python import numpy as np from datasets import load_metric def compute_metrics(eval_pred): load_accuracy = load_metric("accuracy") load_f1 = load_metric("f1") logits, labels = eval_pred predictions = np.argmax(logits, axis=-1) accuracy = load_accuracy.compute(predictions=predictions, references=labels)["accuracy"] f1 = load_f1.compute(predictions=predictions, references=labels)["f1"] return {"accuracy": accuracy, "f1": f1} Next, let's login to your Hugging Face account so you can manage your model repositories. notebook_login will launch a widget in your notebook where you'll need to add your Hugging Face token: python from huggingface_hub import notebook_login notebook_login() You are almost there! Before training our model, you need to define the training arguments and define a Trainer with all the objects you constructed up to this point: python from transformers import TrainingArguments, Trainer repo_name = "finetuning-sentiment-model-3000-samples" training_args = TrainingArguments( output_dir=repo_name, learning_rate=2e-5, per_device_train_batch_size=16, per_device_eval_batch_size=16, num_train_epochs=2, weight_decay=0.01, save_strategy="epoch", push_to_hub=True, ) trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_train, eval_dataset=tokenized_test, tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics, ) Now, it's time to fine-tune the model on the sentiment analysis dataset! 🙌 You just have to call the train() method of your Trainer: python trainer.train() And voila! You fine-tuned a DistilBERT model for sentiment analysis! 🎉 Training time depends on the hardware you use and the number of samples in the dataset. In our case, it took almost 10 minutes using a GPU and fine-tuning the model with 3,000 samples. The more samples you use for training your model, the more accurate it will be but training could be significantly slower. Next, let's compute the evaluation metrics to see how good your model is: python trainer.evaluate() In our case, we got 88% accuracy and 89% f1 score. Quite good for a sentiment analysis model just trained with 3,000 samples! | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | 2. How to Use Pre-trained Sentiment Analysis Models with Python Now that we have covered what sentiment analysis is, we are ready to play with some sentiment analysis models! 🎉 On the Hugging Face Hub, we are building the largest collection of models and datasets publicly available in order to democratize machine learning 🚀. In the Hub, you can find more than 27,000 models shared by the AI community with state-of-the-art performances on tasks such as sentiment analysis, object detection, text generation, speech recognition and more. The Hub is free to use and most models have a widget that allows to test them directly on your browser! There are more than 215 sentiment analysis models publicly available on the Hub and integrating them with Python just takes 5 lines of code: python pip install -q transformers from transformers import pipeline sentiment_pipeline = pipeline("sentiment-analysis") data = ["I love you", "I hate you"] sentiment_pipeline(data) This code snippet uses the pipeline class to make predictions from models available in the Hub. It uses the default model for sentiment analysis to analyze the list of texts data and it outputs the following results: python [{'label': 'POSITIVE', 'score': 0.9998}, {'label': 'NEGATIVE', 'score': 0.9991}] You can use a specific sentiment analysis model that is better suited to your language or use case by providing the name of the model. For example, if you want a sentiment analysis model for tweets, you can specify the model id: python specific_model = pipeline(model="finiteautomata/bertweet-base-sentiment-analysis") specific_model(data) You can test these models with your own data using this Colab notebook: The following are some popular models for sentiment analysis models available on the Hub that we recommend checking out: - Twitter-roberta-base-sentiment is a roBERTa model trained on ~58M tweets and fine-tuned for sentiment analysis. Fine-tuning is the process of taking a pre-trained large language model (e.g. roBERTa in this case) and then tweaking it with additional training data to make it perform a second similar task (e.g. sentiment analysis). - Bert-base-multilingual-uncased-sentiment is a model fine-tuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. - Distilbert-base-uncased-emotion is a model fine-tuned for detecting emotions in texts, including sadness, joy, love, anger, fear and surprise. Are you interested in doing sentiment analysis in languages such as Spanish, French, Italian or German? On the Hub, you will find many models fine-tuned for different use cases and ~28 languages. You can check out the complete list of sentiment analysis models here and filter at the left according to the language of your interest. | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | b. Training a sentiment model with AutoNLP AutoNLP is a tool to train state-of-the-art machine learning models without code. It provides a friendly and easy-to-use user interface, where you can train custom models by simply uploading your data. AutoNLP will automatically fine-tune various pre-trained models with your data, take care of the hyperparameter tuning and find the best model for your use case. All models trained with AutoNLP are deployed and ready for production. Training a sentiment analysis model using AutoNLP is super easy and it just takes a few clicks 🤯. Let's give it a try! As a first step, let's get some data! You'll use Sentiment140, a popular sentiment analysis dataset that consists of Twitter messages labeled with 3 sentiments: 0 (negative), 2 (neutral), and 4 (positive). The dataset is quite big; it contains 1,600,000 tweets. As you don't need this amount of data to get your feet wet with AutoNLP and train your first models, we have prepared a smaller version of the Sentiment140 dataset with 3,000 samples that you can download from here. This is how the dataset looks like: Sentiment 140 dataset Next, let's create a new project on AutoNLP to train 5 candidate models: Creating a new project on AutoNLP Then, upload the dataset and map the text column and target columns: Adding a dataset to AutoNLP Once you add your dataset, go to the "Trainings" tab and accept the pricing to start training your models. AutoNLP pricing can be as low as $10 per model: Adding a dataset to AutoNLP After a few minutes, AutoNLP has trained all models, showing the performance metrics for all of them: Trained sentiment analysis models by AutoNLP The best model has 77.87% accuracy 🔥 Pretty good for a sentiment analysis model for tweets trained with just 3,000 samples! All these models are automatically uploaded to the Hub and deployed for production. You can use any of these models to start analyzing new data right away by using the pipeline class as shown in previous sections of this post. | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | 3. Search for tweets using Tweepy At this point, you are ready to start using the Twitter API to collect tweets 🎉. You will use Tweepy Cursor to extract 1,000 tweets mentioning #NFTs: python # Helper function for handling pagination in our search and handle rate limits def limit_handled(cursor): while True: try: yield cursor.next() except tweepy.RateLimitError: print('Reached rate limite. Sleeping for >15 minutes') time.sleep(15 * 61) except StopIteration: break # Define the term you will be using for searching tweets query = '#NFTs' query = query + ' -filter:retweets' # Define how many tweets to get from the Twitter API count = 1000 # Let's search for tweets using Tweepy search = limit_handled(tweepy.Cursor(api.search, q=query, tweet_mode='extended', lang='en', result_type="recent").items(count)) | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | # Getting Started with Sentiment Analysis using Python Sentiment analysis is the automated process of tagging data according to their sentiment, such as positive, negative and neutral. Sentiment analysis allows companies to analyze data at scale, detect insights and automate processes. In the past, sentiment analysis used to be limited to researchers, machine learning engineers or data scientists with experience in natural language processing. However, the AI community has built awesome tools to democratize access to machine learning in recent years. Nowadays, you can use sentiment analysis with a few lines of code and no machine learning experience at all! 🤯 In this guide, you'll learn everything to get started with sentiment analysis using Python, including: 1. What is sentiment analysis? 2. How to use pre-trained sentiment analysis models with Python 3. How to build your own sentiment analysis model 4. How to analyze tweets with sentiment analysis Let's get started! 🚀 | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | 5. Explore the results of sentiment analysis How are people talking about NFTs on Twitter? Are they talking mostly positively or negatively? Let's explore the results of the sentiment analysis to find out! First, let's load the results on a dataframe and see examples of tweets that were labeled for each sentiment: python import pandas as pd # Load the data in a dataframe df = pd.DataFrame(tweets) pd.set_option('display.max_colwidth', None) # Show a tweet for each sentiment display(df[df["sentiment"] == 'POS'].head(1)) display(df[df["sentiment"] == 'NEU'].head(1)) display(df[df["sentiment"] == 'NEG'].head(1)) Output: Tweet: @NFTGalIery Warm, exquisite and elegant palette of charming beauty Its price is 2401 ETH. \nhttps://t.co/Ej3BfVOAqc\n#NFTs #NFTartists #art #Bitcoin #Crypto #OpenSeaNFT #Ethereum #BTC Sentiment: POS Tweet: How much our followers made on #Crypto in December:\n#DAPPRadar airdrop — $200\nFree #VPAD tokens — $800\n#GasDAO airdrop — up to $1000\nStarSharks_SSS IDO — $3500\nCeloLaunch IDO — $3000\n12 Binance XMas #NFTs — $360 \nTOTAL PROFIT: $8500+\n\nJoin and earn with us https://t.co/fS30uj6SYx Sentiment: NEU Tweet: Stupid guy #2\nhttps://t.co/8yKzYjCYIl\n\n#NFT #NFTs #nftcollector #rarible https://t.co/O4V19gMmVk Sentiment: NEG Then, let's see how many tweets you got for each sentiment and visualize these results: python # Let's count the number of tweets by sentiments sentiment_counts = df.groupby(['sentiment']).size() print(sentiment_counts) # Let's visualize the sentiments fig = plt.figure(figsize=(6,6), dpi=100) ax = plt.subplot(111) sentiment_counts.plot.pie(ax=ax, autopct='%1.1f%%', startangle=270, fontsize=12, label="") Interestingly, most of the tweets about NFTs are positive (56.1%) and almost none are negative (2.0%): Sentiment analysis result of NFTs tweets Finally, let's see what words stand out for each sentiment by creating a word cloud: python from wordcloud import WordCloud from wordcloud import STOPWORDS # Wordcloud with positive tweets positive_tweets = df['tweet'][df["sentiment"] == 'POS'] stop_words = ["https", "co", "RT"] + list(STOPWORDS) positive_wordcloud = WordCloud(max_font_size=50, max_words=100, background_color="white", stopwords = stop_words).generate(str(positive_tweets)) plt.figure() plt.title("Positive Tweets - Wordcloud") plt.imshow(positive_wordcloud, interpolation="bilinear") plt.axis("off") plt.show() # Wordcloud with negative tweets negative_tweets = df['tweet'][df["sentiment"] == 'NEG'] stop_words = ["https", "co", "RT"] + list(STOPWORDS) negative_wordcloud = WordCloud(max_font_size=50, max_words=100, background_color="white", stopwords = stop_words).generate(str(negative_tweets)) plt.figure() plt.title("Negative Tweets - Wordcloud") plt.imshow(negative_wordcloud, interpolation="bilinear") plt.axis("off") plt.show() Some of the words associated with positive tweets include Discord, Ethereum, Join, Mars4 and Shroom: Word cloud for positive tweets In contrast, words associated with negative tweets include: cookies chaos, Solana, and OpenseaNFT: Word cloud for negative tweets And that is it! With just a few lines of python code, you were able to collect tweets, analyze them with sentiment analysis and create some cool visualizations to analyze the results! Pretty cool, huh? | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | 5. Wrapping up Sentiment analysis with Python has never been easier! Tools such as 🤗Transformers and the 🤗Hub makes sentiment analysis accessible to all developers. You can use open source, pre-trained models for sentiment analysis in just a few lines of code 🔥 Do you want to train a custom model for sentiment analysis with your own data? Easy peasy! You can fine-tune a model using Trainer API to build on top of large language models and get state-of-the-art results. If you want something even easier, you can use AutoNLP to train custom machine learning models by simply uploading data. If you have questions, the Hugging Face community can help answer and/or benefit from, please ask them in the Hugging Face forum. Also, join our discord server to talk with us and with the Hugging Face community. | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | a. Fine-tuning model with Python In this tutorial, you'll use the IMDB dataset to fine-tune a DistilBERT model for sentiment analysis. The IMDB dataset contains 25,000 movie reviews labeled by sentiment for training a model and 25,000 movie reviews for testing it. DistilBERT is a smaller, faster and cheaper version of BERT. It has 40% smaller than BERT and runs 60% faster while preserving over 95% of BERT’s performance. You'll use the IMDB dataset to fine-tune a DistilBERT model that is able to classify whether a movie review is positive or negative. Once you train the model, you will use it to analyze new data! ⚡️ We have created this notebook so you can use it through this tutorial in Google Colab. | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | 4. Analyzing new data with the model Now that you have trained a model for sentiment analysis, let's use it to analyze new data and get 🤖 predictions! This unlocks the power of machine learning; using a model to automatically analyze data at scale, in real-time ⚡️ First, let's upload the model to the Hub: python trainer.push_to_hub() Now that you have pushed the model to the Hub, you can use it pipeline class to analyze two new movie reviews and see how your model predicts its sentiment with just two lines of code 🤯: python from transformers import pipeline sentiment_model = pipeline(model="federicopascual/finetuning-sentiment-model-3000-samples") sentiment_model(["I love this move", "This movie sucks!"]) These are the predictions from our model: python [{'label': 'LABEL_1', 'score': 0.9558}, {'label': 'LABEL_0', 'score': 0.9413}] In the IMDB dataset, Label 1 means positive and Label 0 is negative. Quite good! 🔥 | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | 1. What is Sentiment Analysis? Sentiment analysis is a natural language processing technique that identifies the polarity of a given text. There are different flavors of sentiment analysis, but one of the most widely used techniques labels data into positive, negative and neutral. For example, let's take a look at these tweets mentioning @VerizonSupport: - "dear @verizonsupport your service is straight 💩 in dallas.. been with y’all over a decade and this is all time low for y’all. i’m talking no internet at all." → Would be tagged as "Negative". - "@verizonsupport ive sent you a dm" → would be tagged as "Neutral". - "thanks to michelle et al at @verizonsupport who helped push my no-show-phone problem along. order canceled successfully and ordered this for pickup today at the apple store in the mall." → would be tagged as "Positive". Sentiment analysis allows processing data at scale and in real-time. For example, do you want to analyze thousands of tweets, product reviews or support tickets? Instead of sorting through this data manually, you can use sentiment analysis to automatically understand how people are talking about a specific topic, get insights for data-driven decisions and automate business processes. Sentiment analysis is used in a wide variety of applications, for example: - Analyze social media mentions to understand how people are talking about your brand vs your competitors. - Analyze feedback from surveys and product reviews to quickly get insights into what your customers like and dislike about your product. - Analyze incoming support tickets in real-time to detect angry customers and act accordingly to prevent churn. | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | 2. Preprocess data You need data to fine-tune DistilBERT for sentiment analysis. So, let's use 🤗Datasets library to download and preprocess the IMDB dataset so you can then use this data for training your model: python from datasets import load_dataset imdb = load_dataset("imdb") IMDB is a huge dataset, so let's create smaller datasets to enable faster training and testing: python small_train_dataset = imdb["train"].shuffle(seed=42).select([i for i in list(range(3000))]) small_test_dataset = imdb["test"].shuffle(seed=42).select([i for i in list(range(300))]) To preprocess our data, you will use DistilBERT tokenizer: python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") Next, you will prepare the text inputs for the model for both splits of our dataset (training and test) by using the map method: python def preprocess_function(examples): return tokenizer(examples["text"], truncation=True) tokenized_train = small_train_dataset.map(preprocess_function, batched=True) tokenized_test = small_test_dataset.map(preprocess_function, batched=True) To speed up training, let's use a data_collator to convert your training samples to PyTorch tensors and concatenate them with the correct amount of padding: python from transformers import DataCollatorWithPadding data_collator = DataCollatorWithPadding(tokenizer=tokenizer) | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | 1. Install dependencies First, let's install all the libraries you will use in this tutorial: !pip install -q transformers tweepy wordcloud matplotlib | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | 2. Set up Twitter API credentials Next, you will set up the credentials for interacting with the Twitter API. First, you'll need to sign up for a developer account on Twitter. Then, you have to create a new project and connect an app to get an API key and token. You can follow this step-by-step guide to get your credentials. Once you have the API key and token, let's create a wrapper with Tweepy for interacting with the Twitter API: python import tweepy # Add Twitter API key and secret consumer_key = "XXXXXX" consumer_secret = "XXXXXX" # Handling authentication with Twitter auth = tweepy.AppAuthHandler(consumer_key, consumer_secret) # Create a wrapper for the Twitter API api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True) | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | 4. Run sentiment analysis on the tweets Now you can put our new skills to work and run sentiment analysis on your data! 🎉 You will use one of the models available on the Hub fine-tuned for sentiment analysis of tweets. Like in other sections of this post, you will use the pipeline class to make the predictions with this model: python from transformers import pipeline # Set up the inference pipeline using a model from the 🤗 Hub sentiment_analysis = pipeline(model="finiteautomata/bertweet-base-sentiment-analysis") # Let's run the sentiment analysis on each tweet tweets = [] for tweet in search: try: content = tweet.full_text sentiment = sentiment_analysis(content) tweets.append({'tweet': content, 'sentiment': sentiment[0]['label']}) except: pass | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | 4. Analyzing Tweets with Sentiment Analysis and Python In this last section, you'll take what you have learned so far in this post and put it into practice with a fun little project: analyzing tweets about NFTs with sentiment analysis! First, you'll use Tweepy, an easy-to-use Python library for getting tweets mentioning #NFTs using the Twitter API. Then, you will use a sentiment analysis model from the 🤗Hub to analyze these tweets. Finally, you will create some visualizations to explore the results and find some interesting insights. You can use this notebook to follow this tutorial. Let’s jump into it! | [
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Getting Started with Sentiment Analysis using Python | FedericoPascual | February 2, 2022 | sentiment-analysis-python | sentiment-analysis, nlp, guide | https://huggingface.co/blog/sentiment-analysis-python | 3. Building Your Own Sentiment Analysis Model Using pre-trained models publicly available on the Hub is a great way to get started right away with sentiment analysis. These models use deep learning architectures such as transformers that achieve state-of-the-art performance on sentiment analysis and other machine learning tasks. However, you can fine-tune a model with your own data to further improve the sentiment analysis results and get an extra boost of accuracy in your particular use case. In this section, we'll go over two approaches on how to fine-tune a model for sentiment analysis with your own data and criteria. The first approach uses the Trainer API from the 🤗Transformers, an open source library with 50K stars and 1K+ contributors and requires a bit more coding and experience. The second approach is a bit easier and more straightforward, it uses AutoNLP, a tool to automatically train, evaluate and deploy state-of-the-art NLP models without code or ML experience. Let's dive in! | [
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Fine-Tune ViT for Image Classification with 🤗 Transformers | nateraw | February 11, 2022 | fine-tune-vit | guide, cv | https://huggingface.co/blog/fine-tune-vit | Evaluate 📊 python metrics = trainer.evaluate(prepared_ds['validation']) trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) Here were my evaluation results - Cool beans! Sorry, had to say it. * eval metrics *** epoch = 4.0 eval_accuracy = 0.985 eval_loss = 0.0637 eval_runtime = 0:00:02.13 eval_samples_per_second = 62.356 eval_steps_per_second = 7.97 Finally, if you want, you can push your model up to the hub. Here, we'll push it up if you specified push_to_hub=True in the training configuration. Note that in order to push to hub, you'll have to have git-lfs installed and be logged into your Hugging Face account (which can be done via huggingface-cli login). python kwargs = { "finetuned_from": model.config._name_or_path, "tasks": "image-classification", "dataset": 'beans', "tags": ['image-classification'], } if training_args.push_to_hub: trainer.push_to_hub('🍻 cheers', **kwargs) else: trainer.create_model_card(**kwargs) The resulting model has been shared to nateraw/vit-base-beans. I'm assuming you don't have pictures of bean leaves laying around, so I added some examples for you to give it a try! 🚀 | [
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Fine-Tune ViT for Image Classification with 🤗 Transformers | nateraw | February 11, 2022 | fine-tune-vit | guide, cv | https://huggingface.co/blog/fine-tune-vit | # Fine-Tune ViT for Image Classification with 🤗 Transformers Just as transformers-based models have revolutionized NLP, we're now seeing an explosion of papers applying them to all sorts of other domains. One of the most revolutionary of these was the Vision Transformer (ViT), which was introduced in June 2021 by a team of researchers at Google Brain. This paper explored how you can tokenize images, just as you would tokenize sentences, so that they can be passed to transformer models for training. It's quite a simple concept, really... 1. Split an image into a grid of sub-image patches 1. Embed each patch with a linear projection 1. Each embedded patch becomes a token, and the resulting sequence of embedded patches is the sequence you pass to the model. It turns out that once you've done the above, you can pre-train and fine-tune transformers just as you're used to with NLP tasks. Pretty sweet 😎. --- In this blog post, we'll walk through how to leverage 🤗 datasets to download and process image classification datasets, and then use them to fine-tune a pre-trained ViT with 🤗 transformers. To get started, let's first install both those packages. bash pip install datasets transformers | [
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Fine-Tune ViT for Image Classification with 🤗 Transformers | nateraw | February 11, 2022 | fine-tune-vit | guide, cv | https://huggingface.co/blog/fine-tune-vit | Loading ViT Image Processor Now we know what our images look like and better understand the problem we're trying to solve. Let's see how we can prepare these images for our model! When ViT models are trained, specific transformations are applied to images fed into them. Use the wrong transformations on your image, and the model won't understand what it's seeing! 🖼 ➡️ 🔢 To make sure we apply the correct transformations, we will use a ViTImageProcessor initialized with a configuration that was saved along with the pretrained model we plan to use. In our case, we'll be using the google/vit-base-patch16-224-in21k model, so let's load its image processor from the Hugging Face Hub. python from transformers import ViTImageProcessor model_name_or_path = 'google/vit-base-patch16-224-in21k' processor = ViTImageProcessor.from_pretrained(model_name_or_path) You can see the image processor configuration by printing it. ViTImageProcessor { "do_normalize": true, "do_resize": true, "image_mean": [ 0.5, 0.5, 0.5 ], "image_std": [ 0.5, 0.5, 0.5 ], "resample": 2, "size": 224 } To process an image, simply pass it to the image processor's call function. This will return a dict containing pixel values, which is the numeric representation to be passed to the model. You get a NumPy array by default, but if you add the return_tensors='pt' argument, you'll get back torch tensors instead. python processor(image, return_tensors='pt') Should give you something like... { 'pixel_values': tensor([[[[ 0.2706, 0.3255, 0.3804, ...]]]]) } ...where the shape of the tensor is (1, 3, 224, 224). | [
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Fine-Tune ViT for Image Classification with 🤗 Transformers | nateraw | February 11, 2022 | fine-tune-vit | guide, cv | https://huggingface.co/blog/fine-tune-vit | Load a dataset Let's start by loading a small image classification dataset and taking a look at its structure. We'll use the beans dataset, which is a collection of pictures of healthy and unhealthy bean leaves. 🍃 python from datasets import load_dataset ds = load_dataset('beans') ds Let's take a look at the 400th example from the 'train' split from the beans dataset. You'll notice each example from the dataset has 3 features: 1. image: A PIL Image 1. image_file_path: The str path to the image file that was loaded as image 1. labels: A datasets.ClassLabel feature, which is an integer representation of the label. (Later you'll see how to get the string class names, don't worry!) python ex = ds['train'][400] ex { 'image': , 'image_file_path': '/root/.cache/.../bean_rust_train.4.jpg', 'labels': 1 } Let's take a look at the image 👀 python image = ex['image'] image That's definitely a leaf! But what kind? 😅 Since the 'labels' feature of this dataset is a datasets.features.ClassLabel, we can use it to look up the corresponding name for this example's label ID. First, let's access the feature definition for the 'labels'. python labels = ds['train'].features['labels'] labels ClassLabel(num_classes=3, names=['angular_leaf_spot', 'bean_rust', 'healthy'], names_file=None, id=None) Now, let's print out the class label for our example. You can do that by using the int2str function of ClassLabel, which, as the name implies, allows to pass the integer representation of the class to look up the string label. python labels.int2str(ex['labels']) 'bean_rust' Turns out the leaf shown above is infected with Bean Rust, a serious disease in bean plants. 😢 Let's write a function that'll display a grid of examples from each class to get a better idea of what you're working with. python import random from PIL import ImageDraw, ImageFont, Image def show_examples(ds, seed: int = 1234, examples_per_class: int = 3, size=(350, 350)): w, h = size labels = ds['train'].features['labels'].names grid = Image.new('RGB', size=(examples_per_class * w, len(labels) * h)) draw = ImageDraw.Draw(grid) font = ImageFont.truetype("/usr/share/fonts/truetype/liberation/LiberationMono-Bold.ttf", 24) for label_id, label in enumerate(labels): # Filter the dataset by a single label, shuffle it, and grab a few samples ds_slice = ds['train'].filter(lambda ex: ex['labels'] == label_id).shuffle(seed).select(range(examples_per_class)) # Plot this label's examples along a row for i, example in enumerate(ds_slice): image = example['image'] idx = examples_per_class * label_id + i box = (idx % examples_per_class * w, idx // examples_per_class * h) grid.paste(image.resize(size), box=box) draw.text(box, label, (255, 255, 255), font=font) return grid show_examples(ds, seed=random.randint(0, 1337), examples_per_class=3) A grid of a few examples from each class in the dataset From what I'm seeing, - Angular Leaf Spot: Has irregular brown patches - Bean Rust: Has circular brown spots surrounded with a white-ish yellow ring - Healthy: ...looks healthy. 🤷♂️ | [
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Fine-Tune ViT for Image Classification with 🤗 Transformers | nateraw | February 11, 2022 | fine-tune-vit | guide, cv | https://huggingface.co/blog/fine-tune-vit | Train 🚀 python train_results = trainer.train() trainer.save_model() trainer.log_metrics("train", train_results.metrics) trainer.save_metrics("train", train_results.metrics) trainer.save_state() | [
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Fine-Tune ViT for Image Classification with 🤗 Transformers | nateraw | February 11, 2022 | fine-tune-vit | guide, cv | https://huggingface.co/blog/fine-tune-vit | Define an evaluation metric The accuracy metric from datasets can easily be used to compare the predictions with the labels. Below, you can see how to use it within a compute_metrics function that will be used by the Trainer. python import numpy as np from datasets import load_metric metric = load_metric("accuracy") def compute_metrics(p): return metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids) Let's load the pretrained model. We'll add num_labels on init so the model creates a classification head with the right number of units. We'll also include the id2label and label2id mappings to have human-readable labels in the Hub widget (if you choose to push_to_hub). python from transformers import ViTForImageClassification labels = ds['train'].features['labels'].names model = ViTForImageClassification.from_pretrained( model_name_or_path, num_labels=len(labels), id2label={str(i): c for i, c in enumerate(labels)}, label2id={c: str(i) for i, c in enumerate(labels)} ) Almost ready to train! The last thing needed before that is to set up the training configuration by defining TrainingArguments. Most of these are pretty self-explanatory, but one that is quite important here is remove_unused_columns=False. This one will drop any features not used by the model's call function. By default it's True because usually it's ideal to drop unused feature columns, making it easier to unpack inputs into the model's call function. But, in our case, we need the unused features ('image' in particular) in order to create 'pixel_values'. What I'm trying to say is that you'll have a bad time if you forget to set remove_unused_columns=False. python from transformers import TrainingArguments training_args = TrainingArguments( output_dir="./vit-base-beans", per_device_train_batch_size=16, evaluation_strategy="steps", num_train_epochs=4, fp16=True, save_steps=100, eval_steps=100, logging_steps=10, learning_rate=2e-4, save_total_limit=2, remove_unused_columns=False, push_to_hub=False, report_to='tensorboard', load_best_model_at_end=True, ) Now, all instances can be passed to Trainer and we are ready to start training! python from transformers import Trainer trainer = Trainer( model=model, args=training_args, data_collator=collate_fn, compute_metrics=compute_metrics, train_dataset=prepared_ds["train"], eval_dataset=prepared_ds["validation"], tokenizer=processor, ) | [
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Fine-Tune ViT for Image Classification with 🤗 Transformers | nateraw | February 11, 2022 | fine-tune-vit | guide, cv | https://huggingface.co/blog/fine-tune-vit | Define our data collator Batches are coming in as lists of dicts, so you can just unpack + stack those into batch tensors. Since the collate_fn will return a batch dict, you can **unpack the inputs to the model later. ✨ python import torch def collate_fn(batch): return { 'pixel_values': torch.stack([x['pixel_values'] for x in batch]), 'labels': torch.tensor([x['labels'] for x in batch]) } | [
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Fine-Tune ViT for Image Classification with 🤗 Transformers | nateraw | February 11, 2022 | fine-tune-vit | guide, cv | https://huggingface.co/blog/fine-tune-vit | Processing the Dataset Now that you know how to read images and transform them into inputs, let's write a function that will put those two things together to process a single example from the dataset. python def process_example(example): inputs = processor(example['image'], return_tensors='pt') inputs['labels'] = example['labels'] return inputs python process_example(ds['train'][0]) { 'pixel_values': tensor([[[[-0.6157, -0.6000, -0.6078, ..., ]]]]), 'labels': 0 } While you could call ds.map and apply this to every example at once, this can be very slow, especially if you use a larger dataset. Instead, you can apply a transform to the dataset. Transforms are only applied to examples as you index them. First, though, you'll need to update the last function to accept a batch of data, as that's what ds.with_transform expects. python ds = load_dataset('beans') def transform(example_batch): # Take a list of PIL images and turn them to pixel values inputs = processor([x for x in example_batch['image']], return_tensors='pt') # Don't forget to include the labels! inputs['labels'] = example_batch['labels'] return inputs You can directly apply this to the dataset using ds.with_transform(transform). python prepared_ds = ds.with_transform(transform) Now, whenever you get an example from the dataset, the transform will be applied in real time (on both samples and slices, as shown below) python prepared_ds['train'][0:2] This time, the resulting pixel_values tensor will have shape (2, 3, 224, 224). { 'pixel_values': tensor([[[[-0.6157, -0.6000, -0.6078, ..., ]]]]), 'labels': [0, 0] } # Training and Evaluation The data is processed and you are ready to start setting up the training pipeline. This blog post uses 🤗's Trainer, but that'll require us to do a few things first: - Define a collate function. - Define an evaluation metric. During training, the model should be evaluated on its prediction accuracy. You should define a compute_metrics function accordingly. - Load a pretrained checkpoint. You need to load a pretrained checkpoint and configure it correctly for training. - Define the training configuration. After fine-tuning the model, you will correctly evaluate it on the evaluation data and verify that it has indeed learned to correctly classify the images. | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 4.2 SWAG [SWAG](https://huggingface.co/datasets/swag) (Situations With Adversarial Generations) is an interesting evaluation in that it detects a model’s ability to infer commonsense! It does this through a large-scale dataset of 113k multiple choice questions about common sense situations. These questions are transcribed from a video scene/situation and SWAG provides the model with four possible outcomes in the next scene. The model then does its’ best at predicting the correct answer. BERT out outperformed top previous top models including human-level performance: | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 2. How does BERT Work? BERT works by leveraging the following: | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 4.3 GLUE Benchmark [GLUE](https://huggingface.co/datasets/glue) (General Language Understanding Evaluation) benchmark is a group of resources for training, measuring, and analyzing language models comparatively to one another. These resources consist of nine “difficult” tasks designed to test an NLP model’s understanding. Here’s a summary of each of those tasks: While some of these tasks may seem irrelevant and banal, it’s important to note that these evaluation methods are _incredibly_ powerful in indicating which models are best suited for your next NLP application. Attaining performance of this caliber isn’t without consequences. Next up, let’s learn about Machine Learning's impact on the environment. | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 7. How to get started using BERT We've [created this notebook](https://colab.research.google.com/drive/1YtTqwkwaqV2n56NC8xerflt95Cjyd4NE?usp=sharing) so you can try BERT through this easy tutorial in Google Colab. Open the notebook or add the following code to your own. Pro Tip: Use (Shift + Click) to run a code cell. Note: Hugging Face's [pipeline class](https://huggingface.co/docs/transformers/main_classes/pipelines) makes it incredibly easy to pull in open source ML models like transformers with just a single line of code. | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 6. The open source power of BERT Unlike other large learning models like GPT-3, BERT’s source code is publicly accessible ([view BERT’s code on Github](https://github.com/google-research/bert)) allowing BERT to be more widely used all around the world. This is a game-changer! Developers are now able to get a state-of-the-art model like BERT up and running quickly without spending large amounts of time and money. 🤯 Developers can instead focus their efforts on fine-tuning BERT to customize the model’s performance to their unique tasks. It’s important to note that [thousands](https://huggingface.co/models?sort=downloads&search=bert) of open-source and free, pre-trained BERT models are currently available for specific use cases if you don’t want to fine-tune BERT. BERT models pre-trained for specific tasks: - [Twitter sentiment analysis](https://huggingface.co/finiteautomata/bertweet-base-sentiment-analysis) - [Analysis of Japanese text](https://huggingface.co/cl-tohoku/bert-base-japanese-char) - [Emotion categorizer (English - anger, fear, joy, etc.)](https://huggingface.co/j-hartmann/emotion-english-distilroberta-base) - [Clinical Notes analysis](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) - [Speech to text translation](https://huggingface.co/facebook/hubert-large-ls960-ft) - [Toxic comment detection](https://huggingface.co/unitary/toxic-bert?) You can also find [hundreds of pre-trained, open-source Transformer models](https://huggingface.co/models?library=transformers&sort=downloads) available on the Hugging Face Hub. | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 1. What is BERT used for? BERT can be used on a wide variety of language tasks: - Can determine how positive or negative a movie’s reviews are. [(Sentiment Analysis)](https://huggingface.co/blog/sentiment-analysis-python) - Helps chatbots answer your questions. [(Question answering)](https://huggingface.co/tasks/question-answering) - Predicts your text when writing an email (Gmail). [(Text prediction)](https://huggingface.co/tasks/fill-mask) - Can write an article about any topic with just a few sentence inputs. [(Text generation)](https://huggingface.co/tasks/text-generation) - Can quickly summarize long legal contracts. [(Summarization)](https://huggingface.co/tasks/summarization) - Can differentiate words that have multiple meanings (like ‘bank’) based on the surrounding text. (Polysemy resolution) **There are many more language/NLP tasks + more detail behind each of these.** ***Fun Fact:*** You interact with NLP (and likely BERT) almost every single day! NLP is behind Google Translate, voice assistants (Alexa, Siri, etc.), chatbots, Google searches, voice-operated GPS, and more. --- | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 2.3 What is Next Sentence Prediction? NSP (Next Sentence Prediction) is used to help BERT learn about relationships between sentences by predicting if a given sentence follows the previous sentence or not. **Next Sentence Prediction Example:** 1. Paul went shopping. He bought a new shirt. (correct sentence pair) 2. Ramona made coffee. Vanilla ice cream cones for sale. (incorrect sentence pair) In training, 50% correct sentence pairs are mixed in with 50% random sentence pairs to help BERT increase next sentence prediction accuracy. **Fun Fact:** BERT is trained on both MLM (50%) and NSP (50%) at the same time. | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | # BERT 101 🤗 State Of The Art NLP Model Explained | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 4. BERT's performance on common language tasks BERT has successfully achieved state-of-the-art accuracy on 11 common NLP tasks, outperforming previous top NLP models, and is the first to outperform humans! But, how are these achievements measured? | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 2.4 Transformers The Transformer architecture makes it possible to parallelize ML training extremely efficiently. Massive parallelization thus makes it feasible to train BERT on large amounts of data in a relatively short period of time. Transformers use an attention mechanism to observe relationships between words. A concept originally proposed in the popular [2017 Attention Is All You Need](https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) paper sparked the use of Transformers in NLP models all around the world. >Since their introduction in 2017, Transformers have rapidly become the state-of-the-art approach to tackle tasks in many domains such as natural language processing, speech recognition, and computer vision. In short, if you’re doing deep learning, then you need Transformers! Lewis Tunstall, Hugging Face ML Engineer & Author of Natural Language Processing with Transformers Timeline of popular Transformer model releases: Source | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 7.3 Be aware of model bias Let's see what jobs BERT suggests for a "man": ```python unmasker("The man worked as a [MASK].") ``` When you run the above code you should see an output that looks something like: ```python [{'score': 0.09747546911239624, 'sequence': 'the man worked as a carpenter.', 'token': 10533, 'token_str': 'carpenter'}, {'score': 0.052383411675691605, 'sequence': 'the man worked as a waiter.', 'token': 15610, 'token_str': 'waiter'}, {'score': 0.04962698742747307, 'sequence': 'the man worked as a barber.', 'token': 13362, 'token_str': 'barber'}, {'score': 0.037886083126068115, 'sequence': 'the man worked as a mechanic.', 'token': 15893, 'token_str': 'mechanic'}, {'score': 0.037680838257074356, 'sequence': 'the man worked as a salesman.', 'token': 18968, 'token_str': 'salesman'}] ``` BERT predicted the man's job to be a Carpenter, Waiter, Barber, Mechanic, or Salesman Now let's see what jobs BERT suggesst for "woman" ```python unmasker("The woman worked as a [MASK].") ``` You should see an output that looks something like: ```python [{'score': 0.21981535851955414, 'sequence': 'the woman worked as a nurse.', 'token': 6821, 'token_str': 'nurse'}, {'score': 0.1597413569688797, 'sequence': 'the woman worked as a waitress.', 'token': 13877, 'token_str': 'waitress'}, {'score': 0.11547300964593887, 'sequence': 'the woman worked as a maid.', 'token': 10850, 'token_str': 'maid'}, {'score': 0.03796879202127457, 'sequence': 'the woman worked as a prostitute.', 'token': 19215, 'token_str': 'prostitute'}, {'score': 0.030423851683735847, 'sequence': 'the woman worked as a cook.', 'token': 5660, 'token_str': 'cook'}] ``` BERT predicted the woman's job to be a Nurse, Waitress, Maid, Prostitute, or Cook displaying a clear gender bias in professional roles. | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 5. Environmental impact of deep learning Large Machine Learning models require massive amounts of data which is expensive in both time and compute resources. These models also have an environmental impact: Source Machine Learning’s environmental impact is one of the many reasons we believe in democratizing the world of Machine Learning through open source! Sharing large pre-trained language models is essential in reducing the overall compute cost and carbon footprint of our community-driven efforts. | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | NLP Evaluation Methods: | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 7.2 Try out BERT Feel free to swap out the sentence below for one of your own. However, leave [MASK] in somewhere to allow BERT to predict the missing word ```python from transformers import pipeline unmasker = pipeline('fill-mask', model='bert-base-uncased') unmasker("Artificial Intelligence [MASK] take over the world.") ``` When you run the above code you should see an output like this: ``` [{'score': 0.3182411789894104, 'sequence': 'artificial intelligence can take over the world.', 'token': 2064, 'token_str': 'can'}, {'score': 0.18299679458141327, 'sequence': 'artificial intelligence will take over the world.', 'token': 2097, 'token_str': 'will'}, {'score': 0.05600147321820259, 'sequence': 'artificial intelligence to take over the world.', 'token': 2000, 'token_str': 'to'}, {'score': 0.04519503191113472, 'sequence': 'artificial intelligences take over the world.', 'token': 2015, 'token_str': '##s'}, {'score': 0.045153118669986725, 'sequence': 'artificial intelligence would take over the world.', 'token': 2052, 'token_str': 'would'}] ``` Kind of frightening right? 🙃 | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 2.2 What is a Masked Language Model? MLM enables/enforces bidirectional learning from text by masking (hiding) a word in a sentence and forcing BERT to bidirectionally use the words on either side of the covered word to predict the masked word. This had never been done before! **Fun Fact:** We naturally do this as humans! **Masked Language Model Example:** Imagine your friend calls you while camping in Glacier National Park and their service begins to cut out. The last thing you hear before the call drops is: Friend: “Dang! I’m out fishing and a huge trout just [blank] my line!” Can you guess what your friend said?? You’re naturally able to predict the missing word by considering the words bidirectionally before and after the missing word as context clues (in addition to your historical knowledge of how fishing works). Did you guess that your friend said, ‘broke’? That’s what we predicted as well but even we humans are error-prone to some of these methods. **Note:** This is why you’ll often see a “Human Performance” comparison to a language model’s performance scores. And yes, newer models like BERT can be more accurate than humans! 🤯 The bidirectional methodology you did to fill in the [blank] word above is similar to how BERT attains state-of-the-art accuracy. A random 15% of tokenized words are hidden during training and BERT’s job is to correctly predict the hidden words. Thus, directly teaching the model about the English language (and the words we use). Isn’t that neat? Play around with BERT’s masking predictions: Hosted inference API Fill-Mask Examples Mask token: [MASK] Compute This model can be loaded on the Inference API on-demand. JSON Output Maximize **Fun Fact:** Masking has been around a long time - [1953 Paper on Cloze procedure (or ‘Masking’)](https://psycnet.apa.org/record/1955-00850-001). | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 7.4 Some other BERT Notebooks you might enjoy: [A Visual Notebook to BERT for the First Time](https://colab.research.google.com/github/jalammar/jalammar.github.io/blob/master/notebooks/bert/A_Visual_Notebook_to_Using_BERT_for_the_First_Time.ipynb) [Train your tokenizer](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/tokenizer_training.ipynb) +Don't forget to checkout the [Hugging Face Transformers Course](https://huggingface.co/course/chapter1/1) to learn more 🎉 | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 2.1 Large amounts of training data A massive dataset of 3.3 Billion words has contributed to BERT’s continued success. BERT was specifically trained on Wikipedia (\~2.5B words) and Google’s BooksCorpus (\~800M words). These large informational datasets contributed to BERT’s deep knowledge not only of the English language but also of our world! 🚀 Training on a dataset this large takes a long time. BERT’s training was made possible thanks to the novel Transformer architecture and sped up by using TPUs (Tensor Processing Units - Google’s custom circuit built specifically for large ML models). —64 TPUs trained BERT over the course of 4 days. **Note:** Demand for smaller BERT models is increasing in order to use BERT within smaller computational environments (like cell phones and personal computers). [23 smaller BERT models were released in March 2020](https://github.com/google-research/bert). [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert) offers a lighter version of BERT; runs 60% faster while maintaining over 95% of BERT’s performance. | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 7.1 Install Transformers First, let's install Transformers via the following code: ```python !pip install transformers ``` | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 2.4.1 How do Transformers work? Transformers work by leveraging attention, a powerful deep-learning algorithm, first seen in computer vision models. —Not all that different from how we humans process information through attention. We are incredibly good at forgetting/ignoring mundane daily inputs that don’t pose a threat or require a response from us. For example, can you remember everything you saw and heard coming home last Tuesday? Of course not! Our brain’s memory is limited and valuable. Our recall is aided by our ability to forget trivial inputs. Similarly, Machine Learning models need to learn how to pay attention only to the things that matter and not waste computational resources processing irrelevant information. Transformers create differential weights signaling which words in a sentence are the most critical to further process. A transformer does this by successively processing an input through a stack of transformer layers, usually called the encoder. If necessary, another stack of transformer layers - the decoder - can be used to predict a target output. —BERT however, doesn’t use a decoder. Transformers are uniquely suited for unsupervised learning because they can efficiently process millions of data points. Fun Fact: Google has been using your reCAPTCHA selections to label training data since 2011. The entire Google Books archive and 13 million articles from the New York Times catalog have been transcribed/digitized via people entering reCAPTCHA text. Now, reCAPTCHA is asking us to label Google Street View images, vehicles, stoplights, airplanes, etc. Would be neat if Google made us aware of our participation in this effort (as the training data likely has future commercial intent) but I digress.. To learn more about Transformers check out our Hugging Face Transformers Course. | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | What is BERT? BERT, short for Bidirectional Encoder Representations from Transformers, is a Machine Learning (ML) model for natural language processing. It was developed in 2018 by researchers at Google AI Language and serves as a swiss army knife solution to 11+ of the most common language tasks, such as sentiment analysis and named entity recognition. Language has historically been difficult for computers to ‘understand’. Sure, computers can collect, store, and read text inputs but they lack basic language _context_. So, along came Natural Language Processing (NLP): the field of artificial intelligence aiming for computers to read, analyze, interpret and derive meaning from text and spoken words. This practice combines linguistics, statistics, and Machine Learning to assist computers in ‘understanding’ human language. Individual NLP tasks have traditionally been solved by individual models created for each specific task. That is, until— BERT! BERT revolutionized the NLP space by solving for 11+ of the most common NLP tasks (and better than previous models) making it the jack of all NLP trades. In this guide, you'll learn what BERT is, why it’s different, and how to get started using BERT: 1. [What is BERT used for?](#1-what-is-bert-used-for) 2. [How does BERT work?](#2-how-does-bert-work) 3. [BERT model size & architecture](#3-bert-model-size--architecture) 4. [BERT’s performance on common language tasks](#4-berts-performance-on-common-language-tasks) 5. [Environmental impact of deep learning](#5-enviornmental-impact-of-deep-learning) 6. [The open source power of BERT](#6-the-open-source-power-of-bert) 7. [How to get started using BERT](#7-how-to-get-started-using-bert) 8. [BERT FAQs](#8-bert-faqs) 9. [Conclusion](#9-conclusion) Let's get started! 🚀 | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 3. BERT model size & architecture Let’s break down the architecture for the two original BERT models: ML Architecture Glossary: | ML Architecture Parts | Definition | |-----------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Parameters: | Number of learnable variables/values available for the model. | | Transformer Layers: | Number of Transformer blocks. A transformer block transforms a sequence of word representations to a sequence of contextualized words (numbered representations). | | Hidden Size: | Layers of mathematical functions, located between the input and output, that assign weights (to words) to produce a desired result. | | Attention Heads: | The size of a Transformer block. | | Processing: | Type of processing unit used to train the model. | | Length of Training: | Time it took to train the model. Here’s how many of the above ML architecture parts BERTbase and BERTlarge has: | | Transformer Layers | Hidden Size | Attention Heads | Parameters | Processing | Length of Training | |-----------|--------------------|-------------|-----------------|------------|------------|--------------------| | BERTbase | 12 | 768 | 12 | 110M | 4 TPUs | 4 days | | BERTlarge | 24 | 1024 | 16 | 340M | 16 TPUs | 4 days | Let’s take a look at how BERTlarge’s additional layers, attention heads, and parameters have increased its performance across NLP tasks. | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 8. BERT FAQs Can BERT be used with PyTorch? Yes! Our experts at Hugging Face have open-sourced the PyTorch transformers repository on GitHub. Pro Tip: Lewis Tunstall, Leandro von Werra, and Thomas Wolf also wrote a book to help people build language applications with Hugging Face called, ‘Natural Language Processing with Transformers’. Can BERT be used with Tensorflow? Yes! You can use Tensorflow as the backend of Transformers. How long does it take to pre-train BERT? The 2 original BERT models were trained on 4(BERTbase) and 16(BERTlarge) Cloud TPUs for 4 days. How long does it take to fine-tune BERT? For common NLP tasks discussed above, BERT takes between 1-25mins on a single Cloud TPU or between 1-130mins on a single GPU. What makes BERT different? BERT was one of the first models in NLP that was trained in a two-step way: BERT was trained on massive amounts of unlabeled data (no human annotation) in an unsupervised fashion. BERT was then trained on small amounts of human-annotated data starting from the previous pre-trained model resulting in state-of-the-art performance.
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 1.1 Example of BERT BERT helps Google better surface (English) results for nearly all searches since November of 2020. Here’s an example of how BERT helps Google better understand specific searches like: Source Pre-BERT Google surfaced information about getting a prescription filled. Post-BERT Google understands that “for someone” relates to picking up a prescription for someone else and the search results now help to answer that. --- | [
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BERT 101 🤗 State Of The Art NLP Model Explained | britneymuller | March 2, 2022 | bert-101 | guide, nlp | https://huggingface.co/blog/bert-101 | 4.1 SQuAD v1.1 & v2.0 [SQuAD](https://huggingface.co/datasets/squad) (Stanford Question Answering Dataset) is a reading comprehension dataset of around 108k questions that can be answered via a corresponding paragraph of Wikipedia text. BERT’s performance on this evaluation method was a big achievement beating previous state-of-the-art models and human-level performance: | [
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Guiding Text Generation with Constrained Beam Search in 🤗 Transformers | cwkeam | March 11, 2022 | constrained-beam-search | guide, nlp | https://huggingface.co/blog/constrained-beam-search | More About Constraint Classes and Custom Constraints The main takeaway from the explanation can be summarized as the following. At every step, we keep pestering the model to consider the tokens that fulfill our constraints, all the while keeping track of beams that don't, until we end up with reasonably high probability sequences that contain our desired phrases. So a principled way to design this implementation was to represent each constraint as a Constraint object, whose purpose was to keep track of its progress and tell the beam search which tokens to generate next. Although we have provided the keyword argument force_words_ids for model.generate(), the following is what actually happens in the back-end: python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, PhrasalConstraint tokenizer = AutoTokenizer.from_pretrained("t5-base") model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") encoder_input_str = "translate English to German: How old are you?" constraints = [ PhrasalConstraint( tokenizer("Sie", add_special_tokens=False).input_ids ) ] input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids outputs = model.generate( input_ids, constraints=constraints, num_beams=10, num_return_sequences=1, no_repeat_ngram_size=1, remove_invalid_values=True, ) print("Output:\n" + 100 * '-') print(tokenizer.decode(outputs[0], skip_special_tokens=True)) Output: ---------------------------------------------------------------------------------------------------- Wie alt sind Sie? You can define one yourself and input it into the constraints keyword argument to design your unique constraints. You just have to create a sub-class of the Constraint abstract interface class and follow its requirements. You can find more information in the definition of Constraint found here. Some unique ideas (not yet implemented; maybe you can give it a try!) include constraints like OrderedConstraints, TemplateConstraints that may be added further down the line. Currently, the generation is fulfilled by including the sequences, wherever in the output. For example, a previous example had one sequence with scared -> screaming and the other with screamed -> scared. OrderedConstraints could allow the user to specify the order in which these constraints are fulfilled. TemplateConstraints could allow for a more niche use of the feature, where the objective can be something like: python starting_text = "The woman" template = ["the", "", "School of", "", "in"] possible_outputs == [ "The woman attended the Ross School of Business in Michigan.", "The woman was the administrator for the Harvard School of Business in MA." ] or: python starting_text = "The woman" template = ["the", "", "", "University", "", "in"] possible_outputs == [ "The woman attended the Carnegie Mellon University in Pittsburgh.", ] impossible_outputs == [ "The woman attended the Harvard University in MA." ] or if the user does not care about the number of tokens that can go in between two words, then one can just use OrderedConstraint. | [
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Guiding Text Generation with Constrained Beam Search in 🤗 Transformers | cwkeam | March 11, 2022 | constrained-beam-search | guide, nlp | https://huggingface.co/blog/constrained-beam-search | Conclusion Constrained beam search gives us a flexible means to inject external knowledge and requirements into text generation. Previously, there was no easy way to tell the model to 1. include a list of sequences where 2. some of which are optional and some are not, such that 3. they're generated somewhere in the sequence at respective reasonable positions. Now, we can have full control over our generation with a mix of different subclasses of Constraint objects! This new feature is based mainly on the following papers: - Guided Open Vocabulary Image Captioning with Constrained Beam Search - Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation - Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting - Guided Generation of Cause and Effect Like the ones above, many new research papers are exploring ways of using external knowledge (e.g., KGs, KBs) to guide the outputs of large deep learning models. Hopefully, this constrained beam search feature becomes another effective way to achieve this purpose. Thanks to everybody that gave guidance for this feature contribution: Patrick von Platen for being involved from the initial issue to the final PR, and Narsil Patry, for providing detailed feedback on the code. Thumbnail of this post uses an icon with the attribution: Shorthand icons created by Freepik - Flaticon | [
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Guiding Text Generation with Constrained Beam Search in 🤗 Transformers | cwkeam | March 11, 2022 | constrained-beam-search | guide, nlp | https://huggingface.co/blog/constrained-beam-search | Constrained Beam Search Constrained beam search attempts to fulfill the constraints by injecting the desired tokens at every step of the generation. Let's say that we're trying to force the phrase "is fast" in the generated output. In the traditional beam search setting, we find the top k most probable next tokens at each branch and append them for consideration. In the constrained setting, we do the same but also append the tokens that will take us closer to fulfilling our constraints. Here's a demonstration: On top of the usual high-probability next tokens like "dog" and "nice", we force the token "is" in order to get us closer to fulfilling our constraint of "is fast". For the next step, the branched-out candidates below are mostly the same as that of traditional beam search. But like the above example, constrained beam search adds onto the existing candidates by forcing the constraints at each new branch: | [
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Guiding Text Generation with Constrained Beam Search in 🤗 Transformers | cwkeam | March 11, 2022 | constrained-beam-search | guide, nlp | https://huggingface.co/blog/constrained-beam-search | Example 1: Forcing a Word Let's say we're trying to translate "How old are you?" to German. "Wie alt bist du?" is what you'd say in an informal setting, and "Wie alt sind Sie?" is what you'd say in a formal setting. And depending on the context, we might want one form of formality over the other, but how do we tell the model that? | [
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Guiding Text Generation with Constrained Beam Search in 🤗 Transformers | cwkeam | March 11, 2022 | constrained-beam-search | guide, nlp | https://huggingface.co/blog/constrained-beam-search | Traditional Beam search The following is an example of traditional beam search, taken from a previous blog post: Unlike greedy search, beam search works by keeping a longer list of hypotheses. In the above picture, we have displayed three next possible tokens at each possible step in the generation. Here's another way to look at the first step of the beam search for the above example, in the case of num_beams=3: Instead of only choosing "The dog" like what a greedy search would do, a beam search would allow further consideration of "The nice" and "The car". In the next step, we consider the next possible tokens for each of the three branches we created in the previous step. Though we end up considering significantly more than num_beams outputs, we reduce them down to num_beams at the end of the step. We can't just keep branching out, then the number of beams we'd have to keep track of would be \( \text{beams}^{n} \) for \( n \) steps, which becomes very large very quickly ( \( 10 \) beams after \( 10 \) steps is \( 10,000,000,000 \) beams!). For the rest of the generation, we repeat the above step until the ending criteria has been met, like generating the <eos> token or reaching max_length, for example. Branch out, rank, reduce, and repeat. | [
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Guiding Text Generation with Constrained Beam Search in 🤗 Transformers | cwkeam | March 11, 2022 | constrained-beam-search | guide, nlp | https://huggingface.co/blog/constrained-beam-search | Traditional Beam Search Here's how we would do text translation in the traditional beam search setting. !pip install -q git+https://github.com/huggingface/transformers.git python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("t5-base") model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") encoder_input_str = "translate English to German: How old are you?" input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids outputs = model.generate( input_ids, num_beams=10, num_return_sequences=1, no_repeat_ngram_size=1, remove_invalid_values=True, ) print("Output:\n" + 100 * '-') print(tokenizer.decode(outputs[0], skip_special_tokens=True)) Output: ---------------------------------------------------------------------------------------------------- Wie alt bist du? | [
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Guiding Text Generation with Constrained Beam Search in 🤗 Transformers | cwkeam | March 11, 2022 | constrained-beam-search | guide, nlp | https://huggingface.co/blog/constrained-beam-search | Why It's Difficult However, this is actually a very non-trivial problem. This is because the task requires us to force the generation of certain subsequences somewhere in the final output, at some point during the generation. Let's say that we're want to generate a sentence S that has to include the phrase \( p_1={ t_1, t_2 } \) with tokens \( t_1, t_2 \) in order. Let's define the expected sentence \( S \) as: $$ S_{expected} = { s_1, s_2, ..., s_k, t_1, t_2, s_{k+1}, ..., s_n } $$ The problem is that beam search generates the sequence token-by-token. Though not entirely accurate, one can think of beam search as the function \( B(\mathbf{s}{0:i}) = s{i+1} \), where it looks at the currently generated sequence of tokens from \( 0 \) to \( i \) then predicts the next token at \( i+1 \) . But how can this function know, at an arbitrary step \( i < k \) , that the tokens must be generated at some future step \( k \) ? Or when it's at the step \( i=k \) , how can it know for sure that this is the best spot to force the tokens, instead of some future step \( i>k \) ? And what if you have multiple constraints with varying requirements? What if you want to force the phrase \( p_1={t_1, t_2} \) and also the phrase \( p_2={ t_3, t_4, t_5, t_6} \) ? What if you want the model to choose between the two phrases? What if we want to force the phrase \( p_1 \) and force just one phrase among the list of phrases \( {p_{21}, p_{22}, p_{23}} \) ? The above examples are actually very reasonable use-cases, as it will be shown below, and the new constrained beam search feature allows for all of them! This post will quickly go over what the new constrained beam search feature can do for you and then go into deeper details about how it works under the hood. | [
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Guiding Text Generation with Constrained Beam Search in 🤗 Transformers | cwkeam | March 11, 2022 | constrained-beam-search | guide, nlp | https://huggingface.co/blog/constrained-beam-search | Banks Before we talk about the next step, we need to think about the resulting undesirable behavior we can see in the above step. The problem with naively just forcing the desired phrase "is fast" in the output is that, most of the time, you'd end up with nonsensical outputs like "The is fast" above. This is actually what makes this a nontrivial problem to solve. A deeper discussion about the complexities of solving this problem can be found in the original feature request issue that was raised in huggingface/transformers. Banks solve this problem by creating a balance between fulfilling the constraints and creating sensible output. Bank \( n \) refers to the list of beams that have made \( n \) steps progress in fulfilling the constraints. After sorting all the possible beams into their respective banks, we do a round-robin selection. With the above example, we'd select the most probable output from Bank 2, then most probable from Bank 1, one from Bank 0, the second most probable from Bank 2, the second most probable from Bank 1, and so forth. Since we're using num_beams=3, we just do the above process three times to end up with ["The is fast", "The dog is", "The dog and"]. This way, even though we're forcing the model to consider the branch where we've manually appended the desired token, we still keep track of other high-probable sequences that probably make more sense. Even though "The is fast" fulfills our constraint completely, it's not a very sensible phrase. Luckily, we have "The dog is" and "The dog and" to work with in future steps, which hopefully will lead to more sensible outputs later on. This behavior is demonstrated in the third step of the above example: Notice how "The is fast" doesn't require any manual appending of constraint tokens since it's already fulfilled (i.e., already contains the phrase "is fast"). Also, notice how beams like "The dog is slow" or "The dog is mad" are actually in Bank 0, since, although it includes the token "is", it must restart from the beginning to generate "is fast". By appending something like "slow" after "is", it has effectively reset its progress. And finally notice how we ended up at a sensible output that contains our constraint phrase: "The dog is fast"! We were worried initially because blindly appending the desired tokens led to nonsensical phrases like "The is fast". However, using round-robin selection from banks, we implicitly ended up getting rid of nonsensical outputs in preference for the more sensible outputs. | [
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Guiding Text Generation with Constrained Beam Search in 🤗 Transformers | cwkeam | March 11, 2022 | constrained-beam-search | guide, nlp | https://huggingface.co/blog/constrained-beam-search | # Guiding Text Generation with Constrained Beam Search in 🤗 Transformers | [
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Guiding Text Generation with Constrained Beam Search in 🤗 Transformers | cwkeam | March 11, 2022 | constrained-beam-search | guide, nlp | https://huggingface.co/blog/constrained-beam-search | Introduction This blog post assumes that the reader is familiar with text generation methods using the different variants of beam search, as explained in the blog post: "How to generate text: using different decoding methods for language generation with Transformers" Unlike ordinary beam search, constrained beam search allows us to exert control over the output of text generation. This is useful because we sometimes know exactly what we want inside the output. For example, in a Neural Machine Translation task, we might know which words must be included in the final translation with a dictionary lookup. Sometimes, generation outputs that are almost equally possible to a language model might not be equally desirable for the end-user due to the particular context. Both of these situations could be solved by allowing the users to tell the model which words must be included in the end output. | [
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Guiding Text Generation with Constrained Beam Search in 🤗 Transformers | cwkeam | March 11, 2022 | constrained-beam-search | guide, nlp | https://huggingface.co/blog/constrained-beam-search | Example 2: Disjunctive Constraints We mentioned above a use-case where we know which words we want to be included in the final output. An example of this might be using a dictionary lookup during neural machine translation. But what if we don't know which word forms to use, where we'd want outputs like ["raining", "rained", "rains", ...] to be equally possible? In a more general sense, there are always cases when we don't want the exact word verbatim, letter by letter, and might be open to other related possibilities too. Constraints that allow for this behavior are Disjunctive Constraints, which allow the user to input a list of words, whose purpose is to guide the generation such that the final output must contain just at least one among the list of words. Here's an example that uses a mix of the above two types of constraints: python from transformers import GPT2LMHeadModel, GPT2Tokenizer model = GPT2LMHeadModel.from_pretrained("gpt2") tokenizer = GPT2Tokenizer.from_pretrained("gpt2") force_word = "scared" force_flexible = ["scream", "screams", "screaming", "screamed"] force_words_ids = [ tokenizer([force_word], add_prefix_space=True, add_special_tokens=False).input_ids, tokenizer(force_flexible, add_prefix_space=True, add_special_tokens=False).input_ids, ] starting_text = ["The soldiers", "The child"] input_ids = tokenizer(starting_text, return_tensors="pt").input_ids outputs = model.generate( input_ids, force_words_ids=force_words_ids, num_beams=10, num_return_sequences=1, no_repeat_ngram_size=1, remove_invalid_values=True, ) print("Output:\n" + 100 * '-') print(tokenizer.decode(outputs[0], skip_special_tokens=True)) print(tokenizer.decode(outputs[1], skip_special_tokens=True)) Setting pad_token_id to eos_token_id:50256 for open-end generation. Output: ---------------------------------------------------------------------------------------------------- The soldiers, who were all scared and screaming at each other as they tried to get out of the The child was taken to a local hospital where she screamed and scared for her life, police said. As you can see, the first output used "screaming", the second output used "screamed", and both used "scared" verbatim. The list to choose from ["screaming", "screamed", ...] doesn't have to be word forms; this can satisfy any use-case where we need just one from a list of words. | [
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