Instructions to use connectivity/feather_berts_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_4") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/feather_berts_4: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/feather_berts_4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/feather_berts_4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/feather_berts_4/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- e3580b2849758d7e28eb2a1cf50133a62b22da5cba850b4c0cf82628d1a4e047
- Size of remote file:
- 438 MB
- SHA256:
- 6f7b7a33be74ad08c7588094aedd37af5cee1009d49f9b357677e418d66ed6dc
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