Instructions to use connectivity/feather_berts_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_5") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/feather_berts_5: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/feather_berts_5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/feather_berts_5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/feather_berts_5/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- ba5ce2deb0b48060cf1e3f7e90db238018c02d69736c03f907e7822684f69f0a
- Size of remote file:
- 438 MB
- SHA256:
- f17021963cd3d16b6d41642c2437d43491c7cd3c3fb5da02815680149244ba0e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.