Instructions to use connectivity/feather_berts_57 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_57 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_57")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_57") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_57", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/feather_berts_57: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/feather_berts_57/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/feather_berts_57/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/feather_berts_57/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- bfb1924ae86049f377d7123e767fdd54f1c7b7668f7cf2efc1ee7022c38ff03a
- Size of remote file:
- 438 MB
- SHA256:
- 3317faadf2ec817a7af7be0330b98e1c5cc57db89aa9a206a38479b01029c10b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.