Instructions to use connectivity/feather_berts_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_3") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/feather_berts_3: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/feather_berts_3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/feather_berts_3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/feather_berts_3/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- de998bbee385b59edfdb5815ae2ed314e1af263b3c2b2b4b7089dfb63ab6f553
- Size of remote file:
- 438 MB
- SHA256:
- 47c87a8e5111094464cbf39b8d3862671fa30f2f285fdbec7778b5f9334d5a92
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