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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bc01959440e088f323088666243898fc1dcc50295604c250594de97088279d7
- Size of remote file:
- 268 MB
- SHA256:
- f93970521927489138a45c00d29c14997fcaf517cbe08c95ae285334902ec4a9
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