Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Saidur49/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Saidur49/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Saidur49/my_awesome_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Saidur49/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("Saidur49/my_awesome_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 425803b4448f803709478138fc4b5c71f142f2e638fd6aa8ec4c914b1127dc35
- Size of remote file:
- 268 MB
- SHA256:
- d632a433b32686372d8eaecfdf1af3d0e0540f0e20390bae9c82307811304886
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