Instructions to use connectivity/feather_berts_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_1") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/feather_berts_1: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/feather_berts_1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/feather_berts_1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/feather_berts_1/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 96155aee3a0f75cba3aa7f3f27221ccf91fda55e1a151c200181b6e6e49d572b
- Size of remote file:
- 438 MB
- SHA256:
- 9f182a737b527027d5837403189b2a3b4d0917195991681e685c679839507e5a
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