Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-qnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-qnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-qnli") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-qnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from JeremiahZ/bert-base-uncased-qnli: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/JeremiahZ/bert-base-uncased-qnli/resolve/main/training_args.bin
- Command line
-
hf download hf://JeremiahZ/bert-base-uncased-qnli/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/JeremiahZ/bert-base-uncased-qnli/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 6e1fd1dba496cccedd6792672d4f1cc7cc680cee8b5047efe53217b1e9949a1c
- Size of remote file:
- 3.31 kB
- SHA256:
- bf6b1b5a79259bdfa6340b8e4fa529085110e3b44fcbe4d8d2e4dcba7537b3c0
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