Instructions to use connectivity/cola_6ep_ft-30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/cola_6ep_ft-30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/cola_6ep_ft-30")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/cola_6ep_ft-30") model = AutoModelForSequenceClassification.from_pretrained("connectivity/cola_6ep_ft-30", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/cola_6ep_ft-30: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/cola_6ep_ft-30/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/cola_6ep_ft-30/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/cola_6ep_ft-30/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 552b6a982f94e63a09f1fe5c9388e29569d090d4f5ad8a477767189b876737ff
- Size of remote file:
- 438 MB
- SHA256:
- 6b325a81f6c5266f8d6f896957ba321a6e68f130a4c51c967ed992fdc0cad1cc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.