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--- |
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license: apache-2.0 |
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datasets: |
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- imdb |
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language: |
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- en |
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metrics: |
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- f1 |
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- accuracy |
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- recall |
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- precision |
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library_name: peft |
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--- |
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# A Finetuned Bloom 1b1 Model for Sequence Classification |
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<!-- Provide a quick summary of what the model is/does. --> |
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The model was developed as a personal learning experience to fine tune a ready language model for Text Classification and to use it |
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on real life data from the internet to perform sentiment analysis. |
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It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). |
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## Model Details |
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The model achieves the following scores on the evaluation set during the fine tuning: |
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Here is the train/ eval/ test split: |
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``` |
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DatasetDict({ |
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train: Dataset({ |
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features: ['review', 'sentiment'], |
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num_rows: 36000 |
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}) |
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test: Dataset({ |
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features: ['review', 'sentiment'], |
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num_rows: 5000 |
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}) |
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eval: Dataset({ |
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features: ['review', 'sentiment'], |
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num_rows: 9000 |
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}) |
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}) |
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``` |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Snoop088 |
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- **Model type:** Text Classification / Sequence Classification |
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- **Language(s) (NLP):** English |
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- **License:** Apache 2.0 |
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- **Finetuned from model: bigscience/bloom-1b1 |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://huggingface.co/snoop088/imdb_tuned-bloom1b1-sentiment-classifier/tree/main |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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The model is intended to be used for Text Classification. |
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### Direct Use |
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Example script to use the model. Please note that this is peft adapter on the Bloom 1b model: |
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``` |
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DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu" |
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model_name = 'snoop088/imdb_tuned-bloom1b1-sentiment-classifier' |
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loaded_model = AutoModelForSequenceClassification.from_pretrained(model_name, |
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trust_remote_code=True, |
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num_labels=2, |
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device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
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tokenizer.pad_token = tokenizer.eos_token |
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my_set = pd.read_csv("./data/df_manual.csv") |
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inputs = tokenizer(list(my_set["review"]), truncation=True, padding="max_length", max_length=256, return_tensors="pt").to(DEVICE) |
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outputs = loaded_model(**inputs) |
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outcome = np.argmax(torch.Tensor.cpu(outputs.logits), axis=-1) |
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``` |
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[More Information Needed] |
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### Downstream Use [optional] |
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The purpose of this model is to be used to perform sentiment analysis on a dataset similar to the one by IMDB. It should work well on product reviews, too in my opinion. |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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Training is done on the IMDB dataset available on the Hub: |
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[imdb](https://huggingface.co/datasets/imdb) |
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[More Information Needed] |
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### Training Procedure |
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``` |
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training_arguments = TrainingArguments( |
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output_dir="your_tuned_model_name", |
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save_strategy="epoch", |
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per_device_train_batch_size=4, |
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per_device_eval_batch_size=4, |
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gradient_accumulation_steps=4, |
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optim="adamw_torch", |
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evaluation_strategy="steps", |
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logging_steps=5, |
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learning_rate=1e-5, |
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max_grad_norm = 0.3, |
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eval_steps=0.2, |
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num_train_epochs=2, |
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warmup_ratio= 0.1, |
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# group_by_length=True, |
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fp16=False, |
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weight_decay=0.001, |
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lr_scheduler_type="constant", |
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) |
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peft_model = get_peft_model(model, LoraConfig( |
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task_type="SEQ_CLS", |
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r=16, |
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lora_alpha=16, |
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target_modules=[ |
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'query_key_value', |
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'dense' |
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], |
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bias="none", |
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lora_dropout=0.05, # Conventional |
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)) |
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``` |
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LORA results in: trainable params: 3,542,016 || all params: 1,068,859,392 || trainable%: 0.3313827830405592 |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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Simple preprocessing with DataCollator: |
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``` |
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def process_data(example): |
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item = tokenizer(example["review"], truncation=True, max_length=320) # see if this is OK for dyn padding |
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item["labels"] = [ 1 if sent == 'positive' else 0 for sent in example["sentiment"]] |
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return item |
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tokenised_data = tokenised_data.remove_columns(["review", "sentiment"]) |
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data_collator = DataCollatorWithPadding(tokenizer=tokenizer) |
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``` |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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Evaluation function: |
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``` |
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import evaluate |
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def compute_metrics(eval_pred): |
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# All metrics are already predefined in the HF `evaluate` package |
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precision_metric = evaluate.load("precision") |
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recall_metric = evaluate.load("recall") |
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f1_metric= evaluate.load("f1") |
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accuracy_metric = evaluate.load("accuracy") |
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logits, labels = eval_pred # eval_pred is the tuple of predictions and labels returned by the model |
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predictions = np.argmax(logits, axis=-1) |
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precision = precision_metric.compute(predictions=predictions, references=labels)["precision"] |
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recall = recall_metric.compute(predictions=predictions, references=labels)["recall"] |
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f1 = f1_metric.compute(predictions=predictions, references=labels)["f1"] |
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accuracy = accuracy_metric.compute(predictions=predictions, references=labels)["accuracy"] |
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# The trainer is expecting a dictionary where the keys are the metrics names and the values are the scores. |
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return {"precision": precision, "recall": recall, "f1-score": f1, 'accuracy': accuracy} |
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``` |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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- Model: 6.183.1 "13th Gen Intel(R) Core(TM) i9-13900K" |
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- GPU: Nvidia RTX 4900/ 24 GB |
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- Memory: 64 GB |
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#### Software |
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- python 3.11.6 |
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- transformers 4.36.2 |
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- torch 2.1.2 |
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- peft 0.7.1 |
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- numpy 1.26.2 |
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- datasets 2.16.0 |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
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