Instructions to use connectivity/feather_berts_82 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_82 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_82")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_82") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_82", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/feather_berts_82: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/feather_berts_82/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/feather_berts_82/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/feather_berts_82/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 11b76c133fd63ba0836c6b7ecf6c3afa4e7136fcedc7e99cefdfe984ccddd1b9
- Size of remote file:
- 438 MB
- SHA256:
- 94dc3f1b809636c4e20e52373acf3111c4ec5bd17eb5ea6a9e8c682ef6bb7094
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.