Text Classification
Transformers
Safetensors
Thai
sentiment-analysis
thai
wangchanberta
bilstm
cnn
lstm
Instructions to use Dusit-P/thai-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dusit-P/thai-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dusit-P/thai-sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Dusit-P/thai-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download WCB_BiLSTM/config.json from Dusit-P/thai-sentiment: direct link, hf CLI and curl.
- Browser
- Download file 446 Bytes
-
https://huggingface.co/Dusit-P/thai-sentiment/resolve/main/WCB_BiLSTM/config.json
- Command line
-
hf download hf://Dusit-P/thai-sentiment/WCB_BiLSTM/config.json
-
curl -L -o config.json https://huggingface.co/Dusit-P/thai-sentiment/resolve/main/WCB_BiLSTM/config.json
446 Bytes
| { | |
| "model_type": "custom_wcb_sentiment", | |
| "base_model": "airesearch/wangchanberta-base-att-spm-uncased", | |
| "architecture": "WCB_BiLSTM", | |
| "num_labels": 2, | |
| "id2label": { | |
| "0": "NEG", | |
| "1": "POS" | |
| }, | |
| "label2id": { | |
| "NEG": 0, | |
| "POS": 1 | |
| }, | |
| "max_length": 128, | |
| "pooling_after_lstm": "masked_mean", | |
| "export_source_checkpoint": "best_m2_wcb_bilstm_5models_wcb_comparison.pth", | |
| "export_experiment": "5models_wcb_comparison" | |
| } |