Instructions to use Devtrick/bert_nli_ensemble with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Devtrick/bert_nli_ensemble with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import BertNLIClassifier model = BertNLIClassifier.from_pretrained("Devtrick/bert_nli_ensemble", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Devtrick/bert_nli_ensemble: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Devtrick/bert_nli_ensemble/resolve/7bbec5de4f700bd86724c9a1237bf98df80e7b10/training_args.bin
- Command line
-
hf download hf://Devtrick/bert_nli_ensemble@7bbec5de4f700bd86724c9a1237bf98df80e7b10/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Devtrick/bert_nli_ensemble/resolve/7bbec5de4f700bd86724c9a1237bf98df80e7b10/training_args.bin
5.37 kB
- Xet hash:
- 42d861ac0138d4a78d3810a2376566edd22c2af0513707e313130f6f28ae7c6d
- Size of remote file:
- 5.37 kB
- SHA256:
- 87b6a912bc6e60723620880da9d882c4fd165053f41002e45521ecc05c9ee800
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.