Text Classification
Transformers
ONNX
Safetensors
modernbert
semantic-router
vela
text-embeddings-inference
Instructions to use vllm-sr/Vela-1.0-Encoder-307M-FactCheck with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/Vela-1.0-Encoder-307M-FactCheck with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vllm-sr/Vela-1.0-Encoder-307M-FactCheck")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vllm-sr/Vela-1.0-Encoder-307M-FactCheck") model = AutoModelForSequenceClassification.from_pretrained("vllm-sr/Vela-1.0-Encoder-307M-FactCheck", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from vllm-sr/Vela-1.0-Encoder-307M-FactCheck: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M-FactCheck/resolve/main/tokenizer.json
- Command line
-
hf download hf://vllm-sr/Vela-1.0-Encoder-307M-FactCheck/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M-FactCheck/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- 0f1e48b1433bef830d51e2fef9b58d29bbb51b7134a8011cea438bbbfa9515fb
- Size of remote file:
- 34.4 MB
- SHA256:
- 22fd4a60565f25fee8ebd2866aab07456dd46cbbeb45eaf506529ed8451ee58f
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