Instructions to use connectivity/feather_berts_96 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_96 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_96")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_96") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_96", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/feather_berts_96: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/feather_berts_96/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/feather_berts_96/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/feather_berts_96/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- e4b0af2f97b78e6df14c56efbeaebe4c2dc8b85254503ed124a7eb356ae2b72e
- Size of remote file:
- 438 MB
- SHA256:
- 2f463b57fd1c20969f24a01ece708d2a87db4d493ff99d9f8b75a633a98ff078
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.