Instructions to use connectivity/feather_berts_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_2") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/feather_berts_2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/feather_berts_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/feather_berts_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/feather_berts_2/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 4e8f641a3e6e1f34e377fe9a12db0f0c14670c215db4977cc54fcc822f524ba6
- Size of remote file:
- 438 MB
- SHA256:
- 215a2a414a40f795a2dae446aee44396f48f87801448a4ef25c3b197e511f9fc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.