Instructions to use connectivity/feather_berts_36 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_36 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_36")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_36") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_36", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/feather_berts_36: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/feather_berts_36/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/feather_berts_36/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/feather_berts_36/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 13c41a08fc173da4cf9d9f8cd3bd89b4628686a37923f3af757e89138568566b
- Size of remote file:
- 438 MB
- SHA256:
- b74b7389cbf6119f9cce0edfad5b6c0dce92c0dcaaf3710d2ba5f0202b490957
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