From 6a8c60234a94a6df46bb7ec5ba4e7a6459fc5eab Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Fri, 11 Dec 2020 22:25:28 +0100 Subject: [PATCH] Migrate model card from transformers-repo Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755 Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/gpt2-README.md --- README.md | 163 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 163 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..65fe7b5 --- /dev/null +++ b/README.md @@ -0,0 +1,163 @@ +--- +language: en +tags: +- exbert + +license: mit +--- + + +# GPT-2 + +Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large + +Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in +[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) +and first released at [this page](https://openai.com/blog/better-language-models/). + +Disclaimer: The team releasing GPT-2 also wrote a +[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card +has been written by the Hugging Face team to complete the information they provided and give specific examples of bias. + +## Model description + +GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This +means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots +of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, +it was trained to guess the next word in sentences. + +More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, +shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the +predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens. + +This way, the model learns an inner representation of the English language that can then be used to extract features +useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a +prompt. + +## Intended uses & limitations + +You can use the raw model for text generation or fine-tune it to a downstream task. See the +[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you. + +### 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: + +```python +>>> from transformers import pipeline, set_seed +>>> generator = pipeline('text-generation', model='gpt2') +>>> set_seed(42) +>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) + +[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."}, + {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"}, + {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"}, + {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"}, + {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}] +``` + +Here is how to use this model to get the features of a given text in PyTorch: + +```python +from transformers import GPT2Tokenizer, GPT2Model +tokenizer = GPT2Tokenizer.from_pretrained('gpt2') +model = GPT2Model.from_pretrained('gpt2') +text = "Replace me by any text you'd like." +encoded_input = tokenizer(text, return_tensors='pt') +output = model(**encoded_input) +``` + +and in TensorFlow: + +```python +from transformers import GPT2Tokenizer, TFGPT2Model +tokenizer = GPT2Tokenizer.from_pretrained('gpt2') +model = TFGPT2Model.from_pretrained('gpt2') +text = "Replace me by any text you'd like." +encoded_input = tokenizer(text, return_tensors='tf') +output = model(encoded_input) +``` + +### Limitations and bias + +The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of +unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their +[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases): + +> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases +> that require the generated text to be true. +> +> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do +> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a +> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, +> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar +> levels of caution around use cases that are sensitive to biases around human attributes. + +Here's an example of how the model can have biased predictions: + +```python +>>> from transformers import pipeline, set_seed +>>> generator = pipeline('text-generation', model='gpt2') +>>> set_seed(42) +>>> generator("The White man worked as a", max_length=10, num_return_sequences=5) + +[{'generated_text': 'The White man worked as a mannequin for'}, + {'generated_text': 'The White man worked as a maniser of the'}, + {'generated_text': 'The White man worked as a bus conductor by day'}, + {'generated_text': 'The White man worked as a plumber at the'}, + {'generated_text': 'The White man worked as a journalist. He had'}] + +>>> set_seed(42) +>>> generator("The Black man worked as a", max_length=10, num_return_sequences=5) + +[{'generated_text': 'The Black man worked as a man at a restaurant'}, + {'generated_text': 'The Black man worked as a car salesman in a'}, + {'generated_text': 'The Black man worked as a police sergeant at the'}, + {'generated_text': 'The Black man worked as a man-eating monster'}, + {'generated_text': 'The Black man worked as a slave, and was'}] +``` + +This bias will also affect all fine-tuned versions of this model. + +## Training data + +The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web +pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from +this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights +40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText +[here](https://github.com/openai/gpt-2/blob/master/domains.txt). + +## Training procedure + +### Preprocessing + +The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a +vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. + +The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact +details of training. + +## Evaluation results + +The model achieves the following results without any fine-tuning (zero-shot): + +| Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW | +|:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:| +| (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) | +| | 35.13 | 45.99 | 87.65 | 83.4 | 29.41 | 65.85 | 1.16 | 1,17 | 37.50 | 75.20 | + + +### BibTeX entry and citation info + +```bibtex +@article{radford2019language, + title={Language Models are Unsupervised Multitask Learners}, + author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, + year={2019} +} +``` + + + +