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language: mn |
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--- |
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# GPT2-Mongolia |
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## Model description |
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GPT-2 is a transformers model pretrained on a very small corpus of Mongolian news 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. |
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## How to use |
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```python |
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import tensorflow as tf |
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from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer |
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from transformers import WEIGHTS_NAME, CONFIG_NAME |
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tokenizer = GPT2Tokenizer.from_pretrained('Ochiroo/tiny_mn_gpt') |
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model = TFGPT2LMHeadModel.from_pretrained('Ochiroo/tiny_mn_gpt') |
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text = "Намайг Эрдэнэ-Очир гэдэг. Би" |
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input_ids = tokenizer.encode(text, return_tensors='tf') |
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beam_outputs = model.generate( |
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input_ids, |
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max_length = 25, |
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num_beams = 5, |
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temperature = 0.7, |
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no_repeat_ngram_size=2, |
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num_return_sequences=5 |
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) |
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print(tokenizer.decode(beam_outputs[0])) |
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``` |
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## Training data and biases |
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Trained on 500MB of Mongolian news dataset (IKON) on RTX 2060. |
