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--- |
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datasets: |
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- wikipedia |
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language: |
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- lt |
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license: apache-2.0 |
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tags: |
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- "text-generation" |
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widget: |
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- text: "Lietuva yra viena " |
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--- |
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## Model description |
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GPT-2 model from Lithuania using Wikipedia corpus dataset based on GPT-2 small model. |
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This is only the first version of the model, over time model will be improved using a bigger dataset and better data preparation. |
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## Training data |
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This model was pre-trained with 180MB of Lithuanian Wikipedia. The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE). |
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## Training |
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The model was trained on wiki-corpus for 40 hours using NVIDIA Tesla P100 GPU. |
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### How to use |
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### Load model |
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``` |
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from transformers import AutoTokenizer, TFAutoModelWithLMHead |
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import tensorflow as tf |
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tokenizer = AutoTokenizer.from_pretrained("DeividasM/gpt2_lithuanian_small") |
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model = TFAutoModelWithLMHead.from_pretrained("DeividasM/gpt2_lithuanian_small") |
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# Get sequence length max of 1024 |
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tokenizer.model_max_length=1024 |
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model.eval() |
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``` |
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## Generate text |
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``` |
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text = "tekstas " |
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inputs = tokenizer.encode(text, return_tensors="tf") |
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outputs = model.generate(inputs, eos_token_id=50256, pad_token_id=50256, |
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do_sample=True, |
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max_length=40, |
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top_k=40) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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## Limitations and bias |
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The training data used for this model come from Lithuanian Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their model card: |
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``` |
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"Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true. Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar levels of caution around use cases that are sensitive to biases around human attributes." |
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``` |
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## Author |
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Lithuanian GPT-2 small was trained and evaluated by Deividas Mataciunas (https://www.linkedin.com/in/deividasmataciunas/) |
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