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--- |
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language: en |
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widget: |
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- text: "my example goes here in the requested language" |
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license: mit |
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--- |
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# WORK IN PROGRESS |
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# bettercallbloom-560m |
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Finetuned bloom-560m model on the PileOfLaw - r/legal_advice |
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## Model description |
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## Intended uses & limitations |
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### How to use |
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Here is how to use this model to get the features of a given text in PyTorch: |
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```python |
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from transformers import GPT2Tokenizer, GPT2Model |
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2') |
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model = GPT2Model.from_pretrained('gpt2') |
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text = "Replace me by any text you'd like." |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
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and in TensorFlow: |
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```python |
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from transformers import GPT2Tokenizer, TFGPT2Model |
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2') |
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model = TFGPT2Model.from_pretrained('gpt2') |
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text = "Replace me by any text you'd like." |
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encoded_input = tokenizer(text, return_tensors='tf') |
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output = model(encoded_input) |
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``` |
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### Limitations and bias |
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The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of |
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unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their |
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[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases): |
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> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases |
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> that require the generated text to be true. |
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> |
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> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do |
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> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a |
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> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, |
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> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar |
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> levels of caution around use cases that are sensitive to biases around human attributes. |
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Here's an example of how the model can have biased predictions: |
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```python |
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>>> from transformers import pipeline, set_seed |
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>>> generator = pipeline('text-generation', model='gpt2') |
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>>> set_seed(42) |
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>>> generator("The White man worked as a", max_length=10, num_return_sequences=5) |
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[{'generated_text': 'The White man worked as a mannequin for'}, |
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{'generated_text': 'The White man worked as a maniser of the'}, |
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{'generated_text': 'The White man worked as a bus conductor by day'}, |
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{'generated_text': 'The White man worked as a plumber at the'}, |
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{'generated_text': 'The White man worked as a journalist. He had'}] |
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>>> set_seed(42) |
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>>> generator("The Black man worked as a", max_length=10, num_return_sequences=5) |
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[{'generated_text': 'The Black man worked as a man at a restaurant'}, |
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{'generated_text': 'The Black man worked as a car salesman in a'}, |
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{'generated_text': 'The Black man worked as a police sergeant at the'}, |
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{'generated_text': 'The Black man worked as a man-eating monster'}, |
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{'generated_text': 'The Black man worked as a slave, and was'}] |
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``` |
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This bias will also affect all fine-tuned versions of this model. |
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## Training data |
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The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web |
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pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from |
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this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights |
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40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText |
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[here](https://github.com/openai/gpt-2/blob/master/domains.txt). |
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## Training procedure |
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### Preprocessing |
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The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a |
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vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. |
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The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact |
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details of training. |
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## Evaluation results |
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The model achieves the following results without any fine-tuning (zero-shot): |
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| Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW | |
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|:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:| |
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| (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) | |
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| | 35.13 | 45.99 | 87.65 | 83.4 | 29.41 | 65.85 | 1.16 | 1,17 | 37.50 | 75.20 | |
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