70 lines
2.4 KiB
Markdown
70 lines
2.4 KiB
Markdown
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---
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language: th
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tags:
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- gpt2-base-thai
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license: mit
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datasets:
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- oscar
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widget:
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- text: "สวัสดีตอนเช้า"
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---
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## GPT-2 Base Thai
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GPT-2 Base Thai is a causal language model based on the [OpenAI GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) model. It was trained on the [OSCAR](https://huggingface.co/datasets/oscar) dataset, specifically the `unshuffled_deduplicated_th` subset. The model was trained from scratch and achieved an evaluation loss of 1.708 and an evaluation perplexity of 5.516.
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This model was trained using HuggingFace's Flax framework and is part of the [JAX/Flax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organized by HuggingFace. All training was done on a TPUv3-8 VM, sponsored by the Google Cloud team.
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All necessary scripts used for training could be found in the [Files and versions](https://hf.co/flax-community/gpt2-base-thai/tree/main) tab, as well as the [Training metrics](https://hf.co/flax-community/gpt2-base-thai/tensorboard) logged via Tensorboard.
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## Model
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| Model | #params | Arch. | Training/Validation data (text) |
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| ---------------- | ------- | ----- | ------------------------------------ |
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| `gpt2-base-thai` | 124M | GPT-2 | `unshuffled_deduplicated_th` Dataset |
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## Evaluation Results
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The model was trained for 3 epochs and the following is the final result once the training ended.
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| train loss | valid loss | valid PPL | total time |
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| ---------- | ---------- | --------- | ---------- |
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| 1.638 | 1.708 | 5.516 | 6:12:34 |
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## How to Use
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### As Causal Language Model
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```python
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from transformers import pipeline
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pretrained_name = "flax-community/gpt2-base-thai"
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nlp = pipeline(
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"text-generation",
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model=pretrained_name,
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tokenizer=pretrained_name
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)
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nlp("สวัสดีตอนเช้า")
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```
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### Feature Extraction in PyTorch
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```python
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from transformers import GPT2Model, GPT2TokenizerFast
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pretrained_name = "flax-community/gpt2-base-thai"
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model = GPT2Model.from_pretrained(pretrained_name)
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tokenizer = GPT2TokenizerFast.from_pretrained(pretrained_name)
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prompt = "สวัสดีตอนเช้า"
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encoded_input = tokenizer(prompt, return_tensors='pt')
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output = model(**encoded_input)
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```
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## Team Members
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- Sakares Saengkaew ([@sakares](https://hf.co/sakares))
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- Wilson Wongso ([@w11wo](https://hf.co/w11wo))
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