Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use jayavibhav/distilbert-classification-10ksamples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayavibhav/distilbert-classification-10ksamples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jayavibhav/distilbert-classification-10ksamples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jayavibhav/distilbert-classification-10ksamples") model = AutoModelForSequenceClassification.from_pretrained("jayavibhav/distilbert-classification-10ksamples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jayavibhav/distilbert-classification-10ksamples: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/jayavibhav/distilbert-classification-10ksamples/resolve/main/training_args.bin
- Command line
-
hf download hf://jayavibhav/distilbert-classification-10ksamples/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jayavibhav/distilbert-classification-10ksamples/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 049f0a93b68d79463a84af9fcce083aed9f09b09bb16826b36f2d32470dc641e
- Size of remote file:
- 4.03 kB
- SHA256:
- a16c1da5134e4838ac4022503ed6f4d79119ab6f79c10b53926789814b980c31
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