Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use deathperminutV2/NLP_sequence_clasiffication with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deathperminutV2/NLP_sequence_clasiffication with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deathperminutV2/NLP_sequence_clasiffication")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deathperminutV2/NLP_sequence_clasiffication") model = AutoModelForSequenceClassification.from_pretrained("deathperminutV2/NLP_sequence_clasiffication", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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update model card README.md
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end model
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update model card README.md
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Model save
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Training in progress, step 1000
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Training in progress, step 500
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initial commit
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