Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use ThirstBloody/students_scores_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThirstBloody/students_scores_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ThirstBloody/students_scores_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ThirstBloody/students_scores_model") model = AutoModelForSequenceClassification.from_pretrained("ThirstBloody/students_scores_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a9c9f75ddc6dc915ec3574f5688f62861f77e8bbe080f0209dde40ed221eb9a
- Size of remote file:
- 5.3 kB
- SHA256:
- 38a393be6ba74acca9403ce592ce8118f5a0c0437e92ab5061149c0994915244
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