Instructions to use rppadmakumar/distilbert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rppadmakumar/distilbert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rppadmakumar/distilbert_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rppadmakumar/distilbert_model") model = AutoModelForSequenceClassification.from_pretrained("rppadmakumar/distilbert_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rppadmakumar/distilbert_model: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/rppadmakumar/distilbert_model/resolve/main/training_args.bin
- Command line
-
hf download hf://rppadmakumar/distilbert_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rppadmakumar/distilbert_model/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- 674b3f86fe429121d1b52247c93b014066826b8ab78737c4f4e193684af4c077
- Size of remote file:
- 4.86 kB
- SHA256:
- 0601f79f6d4025dc4dc0d05d4c0cec6a79caac8aade9f2a1322d1f2d99e18a49
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