Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use sulpha/oxml_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sulpha/oxml_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sulpha/oxml_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sulpha/oxml_1") model = AutoModelForSequenceClassification.from_pretrained("sulpha/oxml_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93df624dc659835323aa64da8603be2a6a91016ab693090e1d45cc058ae01ac4
- Size of remote file:
- 3.96 kB
- SHA256:
- bde84a0cc5c33e863b1b39623ac2d934baed56b541d3c940233895848ea66c91
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