Instructions to use EricPeter/sw-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EricPeter/sw-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EricPeter/sw-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EricPeter/sw-model") model = AutoModelForSequenceClassification.from_pretrained("EricPeter/sw-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from EricPeter/sw-model: direct link, hf CLI and curl.
- Browser
- Download file 711 MB
-
https://huggingface.co/EricPeter/sw-model/resolve/4909fa64d3ad11421c8092f832c508b3b18451cb/model.safetensors
- Command line
-
hf download hf://EricPeter/sw-model@4909fa64d3ad11421c8092f832c508b3b18451cb/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/EricPeter/sw-model/resolve/4909fa64d3ad11421c8092f832c508b3b18451cb/model.safetensors
711 MB
- Xet hash:
- 6e913ec584f55985c2408c569a3e11f2a3bfa0a6d6b6f9fc11c2405ae88d0aeb
- Size of remote file:
- 711 MB
- SHA256:
- b45453108ffd73b35b557fd4abd003d0ef68b8f5745ac1bf7eafbc898b740aca
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