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:
- d46c4445a4eae97a1d3cdf144d864afaba7c2660fa036202c86ccf58510b0154
- Size of remote file:
- 268 MB
- SHA256:
- 6108407ffc76188dc3ea42f6d6499a17efd110fd7a65800ab3fad989f4f40293
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