Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use UmberH/distilbert-base-uncased-finetuned-cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UmberH/distilbert-base-uncased-finetuned-cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UmberH/distilbert-base-uncased-finetuned-cola")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UmberH/distilbert-base-uncased-finetuned-cola") model = AutoModelForSequenceClassification.from_pretrained("UmberH/distilbert-base-uncased-finetuned-cola", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from UmberH/distilbert-base-uncased-finetuned-cola: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/UmberH/distilbert-base-uncased-finetuned-cola/resolve/main/training_args.bin
- Command line
-
hf download hf://UmberH/distilbert-base-uncased-finetuned-cola/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/UmberH/distilbert-base-uncased-finetuned-cola/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- e6b57cf836245d26c567537ce2bb68c2989ec52c7ec0be368b4ecc0290d14cfe
- Size of remote file:
- 3.38 kB
- SHA256:
- 76f5ed2ab628fbe319f29f283a3020233cd5674734d28cda3f93f2ee28e43a24
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