Instructions to use PranavY2k/my_distilbert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PranavY2k/my_distilbert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PranavY2k/my_distilbert_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PranavY2k/my_distilbert_model") model = AutoModelForSequenceClassification.from_pretrained("PranavY2k/my_distilbert_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 595070a11b22d595c6c0dfdb989fca9960b334d6f0549c7ae018504c875ddfda
- Size of remote file:
- 4.54 kB
- SHA256:
- dceb5e9544db359864ac2636097c544abae3daed70ca9d35ba2fdfa7a0e81803
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