Text Classification
Transformers
PyTorch
English
roberta
text
stance
Eval Results (legacy)
text-embeddings-inference
Instructions to use eevvgg/Stance-Tw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eevvgg/Stance-Tw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eevvgg/Stance-Tw")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eevvgg/Stance-Tw") model = AutoModelForSequenceClassification.from_pretrained("eevvgg/Stance-Tw", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from eevvgg/Stance-Tw: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/eevvgg/Stance-Tw/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
-
hf download hf://eevvgg/Stance-Tw@refs/pr/2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/eevvgg/Stance-Tw/resolve/refs%2Fpr%2F2/tokenizer.json
2.11 MB
- Xet hash:
- 7a142a876f46e2add5ee9eb08788fb30caa540353f84d43e6b06dab40004154a
- Size of remote file:
- 2.11 MB
- SHA256:
- 8e0ca8af4fdb273d80852c5b20f96ce794a2d4161dff49f8f2d4e0a05afc3342
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