64 lines
2.6 KiB
Markdown
64 lines
2.6 KiB
Markdown
---
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language:
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- hr
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thumbnail: https://huggingface.co/macedonizer/hr-gpt2/lets-talk-about-nlp-hr.jpg
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license: apache-2.0
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datasets:
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- wiki-hr
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---
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# hr-gpt2
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Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
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Pretrained model on English 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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hr-gpt2 is a transformers model pretrained on a very large corpus of Croation data in a self-supervised fashion. This
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means it was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots
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of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
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it was trained to guess the next word in sentences.
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More precisely, inputs are sequences of the continuous text of a certain length and the targets are the same sequence,
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shifted one token (word or piece of the word) to the right. The model uses internally a mask-mechanism to make sure the
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predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
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This way, the model learns an inner representation of the Macedonian language that can then be used to extract features
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useful for downstream tasks. The model is best at what it was pretrained for, however, which is generating texts from a
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prompt.
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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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import random \\nfrom transformers import AutoTokenizer, AutoModelWithLMHead
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tokenizer = AutoTokenizer.from_pretrained('macedonizer/hr-gpt2') \
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model = AutoModelWithLMHead.from_pretrained('macedonizer/sr-gpt2')
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input_text = 'Ja sam bio '
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if len(input_text) == 0: \
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encoded_input = tokenizer(input_text, return_tensors="pt") \
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output = model.generate( \
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bos_token_id=random.randint(1, 50000), \
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do_sample=True, \
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top_k=50, \
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max_length=1024, \
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top_p=0.95, \
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num_return_sequences=1, \
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) \
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else: \
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encoded_input = tokenizer(input_text, return_tensors="pt") \
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output = model.generate( \
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**encoded_input, \
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bos_token_id=random.randint(1, 50000), \
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do_sample=True, \
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top_k=50, \
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max_length=1024, \
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top_p=0.95, \
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num_return_sequences=1, \
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)
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decoded_output = [] \
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for sample in output: \
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decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
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print(decoded_output) |