Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use nickprock/distilbert-base-uncased-banking77-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nickprock/distilbert-base-uncased-banking77-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nickprock/distilbert-base-uncased-banking77-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nickprock/distilbert-base-uncased-banking77-classification") model = AutoModelForSequenceClassification.from_pretrained("nickprock/distilbert-base-uncased-banking77-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nickprock/distilbert-base-uncased-banking77-classification: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/nickprock/distilbert-base-uncased-banking77-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://nickprock/distilbert-base-uncased-banking77-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nickprock/distilbert-base-uncased-banking77-classification/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 26450e5d698a4115cec006bee7ac04026305220f4a3f57a588fc694ea19f4f48
- Size of remote file:
- 3.31 kB
- SHA256:
- 57630d99ced6beed2a638e391450f4895ee9a9433fcc20b31c4d3a7e28a59f6f
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