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--- |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: tmpq0jhm_jh |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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## Model description |
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This is a gpt2 model trained on 142 612 different Lithuanian Wikipedia articles + 11 405 articles taken from delfi.lt, ve.lt and respublika.lt news portals. |
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## Intended uses & limitations |
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This is a model I trained when writing my bachelors. You can use it anywhere you want. |
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### Training results |
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Model reached 36.83% accuracy with training data and 37.02% with validation data |
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### Framework versions |
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Transformers 3.5.0 |
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TensorFlow 2.4.1 |
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Tokenizers 0.12.1 |
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Torch 1.4.0 |
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How to use it: |
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```python |
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import tensorflow as tf |
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from transformers import WEIGHTS_NAME, CONFIG_NAME |
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from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer |
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import os |
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output_dir = '...' #local file or link to this page |
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tokenizer = GPT2Tokenizer.from_pretrained(output_dir) |
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model = TFGPT2LMHeadModel.from_pretrained(output_dir) |
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text = "Siekdamas" |
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# encoding the input text |
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input_ids = tokenizer.encode(text, return_tensors='tf') |
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# getting out output |
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beam_outputs = model.generate( |
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input_ids, |
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max_length = 150, |
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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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``` |
