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 training_args.bin from WilliamStar/sequence_classification_model: direct link, hf CLI and curl.
- Browser
- Download file 4.41 kB
-
https://huggingface.co/WilliamStar/sequence_classification_model/resolve/main/training_args.bin
- Command line
-
hf download hf://WilliamStar/sequence_classification_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/WilliamStar/sequence_classification_model/resolve/main/training_args.bin
4.41 kB
- Xet hash:
- c6391090ef008ec3b5602cc15b418154fe6137d290dff65f2a35339a1b78bb15
- Size of remote file:
- 4.41 kB
- SHA256:
- bbd635016e63e6293f346a7903351f7ba7e9d4e669da6e2e4ffbca1840bb9287
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