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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SteveWCG/roberta-sentence-classifier") model = AutoModelForSequenceClassification.from_pretrained("SteveWCG/roberta-sentence-classifier") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag, links to paper and code repository
#1
by nielsr HF Staff - opened
Hi! This PR improves the model card for roberta-sentence-classifier by:
- Adding the
pipeline_tag: text-classificationto the YAML metadata. - Linking the model card to the paper Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction.
- Linking to the official GitHub repository c-steve-wang/Epi2Diff.
- Describing the 8 cognitive problem-solving episodes that this model is fine-tuned to classify.