Instructions to use m4faisal/NLI-Conventional-Fine-Tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m4faisal/NLI-Conventional-Fine-Tuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="m4faisal/NLI-Conventional-Fine-Tuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("m4faisal/NLI-Conventional-Fine-Tuning") model = AutoModelForSequenceClassification.from_pretrained("m4faisal/NLI-Conventional-Fine-Tuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from m4faisal/NLI-Conventional-Fine-Tuning: direct link, hf CLI and curl.
- Browser
- Download file 2.27 MB
-
https://huggingface.co/m4faisal/NLI-Conventional-Fine-Tuning/resolve/main/tokenizer.json
- Command line
-
hf download hf://m4faisal/NLI-Conventional-Fine-Tuning/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/m4faisal/NLI-Conventional-Fine-Tuning/resolve/main/tokenizer.json
2.27 MB
File too large to display, you can check the raw version instead.