Instructions to use connectivity/feather_berts_83 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_83 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_83")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_83") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_83", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/feather_berts_83: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/feather_berts_83/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/feather_berts_83/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/feather_berts_83/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 52af8842dac1613b4e73af5d8f74dc3af9a588f21b7d3edc30ec8df951788c50
- Size of remote file:
- 438 MB
- SHA256:
- a90f3b71a13f62f4d6d1e0d16f1a8e56e7c1b1b7ddbd2a9f0e482df616b22888
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