Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use ThirstBloody/students_scores_model_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThirstBloody/students_scores_model_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ThirstBloody/students_scores_model_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ThirstBloody/students_scores_model_2") model = AutoModelForSequenceClassification.from_pretrained("ThirstBloody/students_scores_model_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4584059340e4f6f214ab7320f8ffdf4b0328071fee8abdca45feaf6d0ba1a881
- Size of remote file:
- 5.3 kB
- SHA256:
- 6521ffe674775346a1ddf4c2cc9c035f373fc683eb43b5dd18554d16fa111881
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