Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Johannes/distilbert-base-uncased-finetuned-code-snippet-quality-scoring with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Johannes/distilbert-base-uncased-finetuned-code-snippet-quality-scoring with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Johannes/distilbert-base-uncased-finetuned-code-snippet-quality-scoring")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Johannes/distilbert-base-uncased-finetuned-code-snippet-quality-scoring") model = AutoModelForSequenceClassification.from_pretrained("Johannes/distilbert-base-uncased-finetuned-code-snippet-quality-scoring") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5137fc3de2ac0da187ac7f3cdbaa2c1f16826c8a5f59fe7cddde5b9e194ff389
- Size of remote file:
- 268 MB
- SHA256:
- 4122f30b7df79488fe7e2c09e6f6e72af7d138cc4a38dd0f4a6fb7f497196059
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