Instructions to use Raj411/roberta-wr-rationale with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Raj411/roberta-wr-rationale with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Raj411/roberta-wr-rationale")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Raj411/roberta-wr-rationale") model = AutoModelForSequenceClassification.from_pretrained("Raj411/roberta-wr-rationale", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Raj411/roberta-wr-rationale: direct link, hf CLI and curl.
- Browser
- Download file 6.41 MB
-
https://huggingface.co/Raj411/roberta-wr-rationale/resolve/main/tokenizer.json
- Command line
-
hf download hf://Raj411/roberta-wr-rationale/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Raj411/roberta-wr-rationale/resolve/main/tokenizer.json
6.41 MB
- Xet hash:
- 3719cb1602e7fa93abe9310d1a13a2836cd50d5c6dddc984efbfd8ef23460746
- Size of remote file:
- 6.41 MB
- SHA256:
- 9513c0917e9c1673c3af00a3bd1f6e6f0239afdae7fbee68ba24b5d7786193ed
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