Instructions to use connectivity/feather_berts_9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_9")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_9") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/feather_berts_9: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/feather_berts_9/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/feather_berts_9/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/feather_berts_9/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 716bb7548763a8221ad6a077d411508592b985399c02392d1770309df49ed149
- Size of remote file:
- 438 MB
- SHA256:
- 4bd392c45b23383de1aa27fd027ef982686c2756dc7e56139043bd4227c51331
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