Instructions to use namesarnav/corr2cause-bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namesarnav/corr2cause-bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="namesarnav/corr2cause-bert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("namesarnav/corr2cause-bert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained("namesarnav/corr2cause-bert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from namesarnav/corr2cause-bert-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/namesarnav/corr2cause-bert-base-uncased/resolve/main/training_args.bin
- Command line
-
hf download hf://namesarnav/corr2cause-bert-base-uncased/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/namesarnav/corr2cause-bert-base-uncased/resolve/main/training_args.bin
5.91 kB
- Xet hash:
- e494e59664202bed45f9506a55b6d54c3bdbab86879a1c3eb6388379d9bad8a9
- Size of remote file:
- 5.91 kB
- SHA256:
- 58ab5243a6c2e830f3b4c1db98a5310f62effd9b65a3f24e3c39a9d0a6d976ef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.