language: en
tags:
- exbert
OLM GPT-2 October 2022
This is a more up-to-date version of the original GPT-2. In addition to being more up-to-date, it also tends to perform better than the original GPT2 on standard benchmarks. It was trained on a cleaned October 2022 snapshot of Common Crawl and Wikipedia.
Intended uses
You can use the raw model for text generation or fine-tune it to a downstream task.
How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
>>> from transformers import pipeline, set_seed
>>> # It is important to include the bad_words_ids=[[0,2]] if you want this model to stay on topic.
>>> # Otherwise, the model may generate start and end tokens followed by text that is not relevant to
>>> # the previous text.
>>> generator = pipeline('text-generation', model='olm/olm-gpt2-oct-2022', bad_words_ids=[[0,2]])
>>> set_seed(42)
>>> # This example also illustrates that sometimes our model generates
>>> # bloggy/spammy/webb-y things, even though it gets higher evaluation results
>>> # than the original GPT-2 accross a variety of benchmarks. See the first output.
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
[
{'generated_text': "Hello, I'm a language model, but you can take me if I want.\nReplyDelete\nReplies\nReply\nAnonymous October 17, 2011"},
{'generated_text': "Hello, I'm a language model, and here's some useful news for you all: The release date for the new release of"},
{'generated_text': "Hello, I'm a language model, I'm not a developer or anybody who's working on those. I'm a freelancer... I"},
{'generated_text': "Hello, I'm a language model, a language analyst, and a language system designer. I'm just curious about the"},
{'generated_text': "Hello, I'm a language model, I'm passionate about languages, but I don't understand how my system works, the interaction"}
]
Here is how to use this model to get the features of a given text in PyTorch:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained('olm/olm-gpt2-oct-2022')
model = AutoModelForCausalLM.from_pretrained('olm/olm-gpt2-oct-2022')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
Dataset
The model and tokenizer were trained with this October 2022 cleaned Common Crawl dataset plus this October 2022 cleaned Wikipedia dataset. The tokenized version of these concatenated datasets is here. The datasets were created with this repo.
Training
The model was trained according to the GPT2 instructions at this repo.
Evaluation results
The model achieves the following results without any fine-tuning (zero-shot):
| Task | Metric | Original GPT2 | OLM GPT2 (Ours) | Significance (two-tailed p-value) |
|---|---|---|---|---|
| rte | acc | 0.5307 | 0.5415 | 0.7188 |
| piqa | acc/acc_norm | 0.6289/0.6251 | 0.6638/0.6670 | 0.0020/0.0002 |
| copa | acc | 0.6400 | 0.6900 | 0.3000 |
| record | f1/em | 0.7094/0.7026 | 0.6874/0.6810 | 0.0000/0.0000 |
| boolq | acc | 0.4872 | 0.5606 | 0.0000 |
| cb | acc/f1 | 0.4101/0.2619 | 0.3571/0.1754 | 0.4193/NA |
| hellaswag | acc/acc_norm | 0.2892/0.3114 | 0.3076/0.3491 | 0.0000/0.0000 |
| mrpc | acc/f1 | 0.5662/0.6911 | 0.6495/0.7741 | 0.0007/0.0002 |
| multirc | acc | 0.0189 | 0.0115 | 0.0959 |
| lambada | ppl/acc | 40.0554/0.3256 | 28.6733/0.3625 | 0.0000/0.0000 |
| wsc | acc | 0.4327 | 0.3654 | 0.1679 |
| wic | acc | 0.4922 | 0.5 | 0.6924 |
| mnli | acc | 0.3372 | 0.3471 | 0.0384 |
| qnli | acc | 0.5017 | 0.4981 | 0.5884 |
| cola | mcc | 0.0126 | 0.0181 | 0.8614 |
| triviaqa | acc | 0.0151 | 0.0182 | 0.0048 |
| winogrande | acc | 0.5162 | 0.5114 | 0.7360 |
| webqs | acc | 0.0030 | 0.0108 | 0.0000 |
| arc_easy | acc/acc_norm | 0.4381/0.3948 | 0.4651/0.4247 | 0.0082/0.0029 |
| arc_challenge | acc/acc_norm | 0.1903/0.2270 | 0.1997/0.2329 | 0.4132/0.6256 |
To get these results, we used the Eleuther AI evaluation harness here The harness can produce results a little different than those reported in the GPT2 paper. The p-values come from the stderr from the evaluation harness, plus a normal distribution assumption.