Instructions to use WilliamStar/sequence_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WilliamStar/sequence_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="WilliamStar/sequence_classification_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("WilliamStar/sequence_classification_model") model = AutoModelForSequenceClassification.from_pretrained("WilliamStar/sequence_classification_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from WilliamStar/sequence_classification_model: direct link, hf CLI and curl.
- Browser
- Download file 204 MB
-
https://huggingface.co/WilliamStar/sequence_classification_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://WilliamStar/sequence_classification_model@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/WilliamStar/sequence_classification_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
204 MB
- Xet hash:
- 353c1815ff63c806188a285b029f10d54ca3ec49d44f0e1bff5eb5530e16fa58
- Size of remote file:
- 204 MB
- SHA256:
- e489afef8b1c207167557023fe958d0e2cf486502ebcbc503dcd5330548e0759
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