85 lines
2.9 KiB
Markdown
85 lines
2.9 KiB
Markdown
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---
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inference: false
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language:
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- bg
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license: mit
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datasets:
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- oscar
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- chitanka
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- wikipedia
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tags:
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- torch
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---
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# GPT-2
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Pretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was introduced in
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[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
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and first released at [this page](https://openai.com/blog/better-language-models/).
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## Model description
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This is the **MEDIUM** version.
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The training data is Bulgarian text from [OSCAR](https://oscar-corpus.com/post/oscar-2019/), [Chitanka](https://chitanka.info/) and [Wikipedia](https://bg.wikipedia.org/).
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## Intended uses & limitations
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You can use the raw model for:
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- text generation
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- auto-complete
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- spelling correction
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Or fine-tune it to a downstream task.
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### How to use
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Here is how to use this model in PyTorch:
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```python
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>>> from transformers import AutoModel, AutoTokenizer
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>>>
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>>> model_id = "rmihaylov/gpt2-medium-bg"
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>>> tokenizer = AutoTokenizer.from_pretrained(model_id)
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>>> model = AutoModel.from_pretrained(model_id, trust_remote_code=True)
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>>>
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>>> input_ids = tokenizer.encode(
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>>> "Здравей,",
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>>> add_special_tokens=False,
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>>> return_tensors='pt')
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>>>
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>>> output_ids = model.generate(
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>>> input_ids,
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>>> do_sample=True,
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>>> max_length=50,
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>>> top_p=0.92,
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>>> pad_token_id=2,
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>>> top_k=0)
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>>>
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>>> output = tokenizer.decode(output_ids[0])
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>>>
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>>> output = output.replace('<|endoftext|>', '\n\n\n')
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>>> output = output.replace('<|unknown|>', '')
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>>> output = output.replace('▁', ' ')
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>>> output = output.replace('<|n|>', '\n')
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>>>
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>>> print(output)
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Здравей, господин Фиш. — Добс забеляза как пребледня Ривера.
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— Не си тръгвайте още. Имам да ви задам няколко въпроса.
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— Благодаря, благодаря. — Фиш не изчака да му покаже, че е забелязал жеста й
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```
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### Limitations and bias
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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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