Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use bryjaco/my_tc_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bryjaco/my_tc_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bryjaco/my_tc_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bryjaco/my_tc_model") model = AutoModelForSequenceClassification.from_pretrained("bryjaco/my_tc_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 628f432b939bd7d79bc9779e317d2be722d917d37eb46e1e665b5a480f1287d0
- Size of remote file:
- 3.52 kB
- SHA256:
- 0299a761ce18728021d9a3edcbf6ef44afbedc8247ea9f736c042d25f7b0f054
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