Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use SteveWCG/roberta-sentence-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SteveWCG/roberta-sentence-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SteveWCG/roberta-sentence-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SteveWCG/roberta-sentence-classifier") model = AutoModelForSequenceClassification.from_pretrained("SteveWCG/roberta-sentence-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Add pipeline tag, links to paper and code repository
Verified signatureHi! This PR improves the model card for `roberta-sentence-classifier` by: - Adding the `pipeline_tag: text-classification` to the YAML metadata. - Linking the model card to the paper [Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction](https://huggingface.co/papers/2606.28186). - Linking to the official GitHub repository [c-steve-wang/Epi2Diff](https://github.com/c-steve-wang/Epi2Diff). - Describing the 8 cognitive problem-solving episodes that this model is fine-tuned to classify.9c513e9
-
End of training
Verified signature566f09c -
initial commit
Verified signature02bb83e