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--- |
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language: |
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- mk |
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thumbnail: https://huggingface.co/macedonizer/mk-roberta-base/blaze-koneski.jpg |
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license: Apache 2.0 |
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datasets: |
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- wiki-mk |
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- time-mk-news-2010-2015 |
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--- |
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# mk-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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mk-gpt2 is a transformers model pretrained on a very large corpus of Macedonian data in a self-supervised fashion. This |
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means it was pretrained on the raw texts only, with no humans labelling 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 continuous text of a certain length and the targets are the same sequence, |
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shifted one token (word or piece of 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 |
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from transformers import AutoTokenizer, AutoModelWithLMHead |
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tokenizer = AutoTokenizer.from_pretrained('macedonizer/mk-gpt2') \ |
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model = AutoModelWithLMHead.from_pretrained('macedonizer/mk-gpt2') |
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input_text = 'Скопје е ' |
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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) |
