Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JIWON/bert-base-finetuned-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JIWON/bert-base-finetuned-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JIWON/bert-base-finetuned-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JIWON/bert-base-finetuned-nli") model = AutoModelForSequenceClassification.from_pretrained("JIWON/bert-base-finetuned-nli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from JIWON/bert-base-finetuned-nli: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/JIWON/bert-base-finetuned-nli/resolve/main/training_args.bin
- Command line
-
hf download hf://JIWON/bert-base-finetuned-nli/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/JIWON/bert-base-finetuned-nli/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- acd7bd7716b8acbdadf2942f21552e06d0316fcacaa893048744ec943a3a6c2d
- Size of remote file:
- 3.06 kB
- SHA256:
- 4e7e6bd497ff99ad012e8e7f60da4c6acb747f3e3f8a3ede1448180685964779
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.