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
|
Download README.md from Wasuthorn/BERTK: direct link, hf CLI and curl.
- Browser
- Download file 710 Bytes
-
https://huggingface.co/Wasuthorn/BERTK/resolve/main/README.md
- Command line
-
hf download hf://Wasuthorn/BERTK/README.md
-
curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/Wasuthorn/BERTK/resolve/main/README.md
710 Bytes
metadata
datasets:
- oscar
language:
- th
Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
BERTK is ThaiBERT Training with OSCAR Thai corpus
Model Description
- Developed by: Wasuthorn Watcharakul
- Model type: BERT
- Language(s) (NLP): Thai
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]