Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use xysmalobia/sequence_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xysmalobia/sequence_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xysmalobia/sequence_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xysmalobia/sequence_classification") model = AutoModelForSequenceClassification.from_pretrained("xysmalobia/sequence_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 625d5a852a0da538044a8a45b346893297485b5b518f9210b691f49f4bc0afbb
- Size of remote file:
- 438 MB
- SHA256:
- 6aae8a8b11e08a5aa243b6288151bdb7b78c0837547299c2184c744bbab285db
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