Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use ducatte/sequence_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ducatte/sequence_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ducatte/sequence_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ducatte/sequence_classification") model = AutoModelForSequenceClassification.from_pretrained("ducatte/sequence_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e95770ac598dfac119876fa21be88ffa02ef9f13430e54b7a70c7734da9ed94a
- Size of remote file:
- 268 MB
- SHA256:
- cfd29bd4cb10542f96e9e4e6e3475f9e2c2aae47cdbc26c8870f437a45c10c73
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.