Instructions to use Wasuthorn/BERTK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wasuthorn/BERTK with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Wasuthorn/BERTK")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Wasuthorn/BERTK") model = AutoModelForMaskedLM.from_pretrained("Wasuthorn/BERTK", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Wasuthorn/BERTK: direct link, hf CLI and curl.
- Browser
- Download file 710 Bytes
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https://huggingface.co/Wasuthorn/BERTK/resolve/main/README.md
- Command line
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hf download hf://Wasuthorn/BERTK/README.md
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curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/Wasuthorn/BERTK/resolve/main/README.md
710 Bytes
| datasets: | |
| - oscar | |
| language: | |
| - th | |
| # Model Card for Model ID | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). | |
| ## Model Details | |
| BERTK is ThaiBERT Training with OSCAR Thai corpus | |
| ### Model Description | |
| <!-- Provide a longer summary of what this model is. --> | |
| - **Developed by:** Wasuthorn Watcharakul | |
| - **Model type:** BERT | |
| - **Language(s) (NLP):** Thai | |
| - **License:** [More Information Needed] | |
| - **Finetuned from model [optional]:** [More Information Needed] | |