Text Classification
Transformers
Safetensors
Vietnamese
roberta
esg
greenwashing
phobert
vietnamese
commitment-detection
Instructions to use dqa2412/esg-washing-optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dqa2412/esg-washing-optimized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dqa2412/esg-washing-optimized")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dqa2412/esg-washing-optimized") model = AutoModelForSequenceClassification.from_pretrained("dqa2412/esg-washing-optimized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download best_params_commitment.json from dqa2412/esg-washing-optimized: direct link, hf CLI and curl.
- Browser
- Download file 140 Bytes
-
https://huggingface.co/dqa2412/esg-washing-optimized/resolve/main/best_params_commitment.json
- Command line
-
hf download hf://dqa2412/esg-washing-optimized/best_params_commitment.json
-
curl -L -o best_params_commitment.json https://huggingface.co/dqa2412/esg-washing-optimized/resolve/main/best_params_commitment.json
140 Bytes
| { | |
| "learning_rate": 1.34336568680343e-05, | |
| "train_batch_size": 16, | |
| "weight_decay": 0.0432, | |
| "warmup_ratio": 0.058245828039608386 | |
| } |