Create README.md

README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- motivational-interviewing
+metrics:
+- f1
+widget:
+- text: >-
+    I'm planning on having tuna, ground tuna, chopped celery, and chopped black
+    pepper, and half a apple.
+  example_title: change_talk_goal_talk_and_opportunities
+---
+
+# Model Card for roberta-base-motivational-interviewing
+
+⚠ WARNING: This is a preliminary model that is still actively under development. ⚠
+
+This is a [roBERTa-base](https://huggingface.co/roberta-base) model fine-tuned on a small dataset of conversations between health coaches and cancer survivors.
+
+# How to Get Started with the Model
+
+You can use this model directly with a pipeline for text classification:
+
+```python
+>>> import transformers
+>>> model_name = "clulab/roberta-base-motivational-interviewing"
+>>> classifier = transformers.TextClassificationPipeline(
+...     tokenizer=transformers.AutoTokenizer.from_pretrained(model_name),
+...     model=transformers.AutoModelForSequenceClassification.from_pretrained(model_name))
+>>> classifier("I'm planning on having tuna, ground tuna, chopped celery, and chopped black pepper, and half a apple.")
+[{'label': 'change_talk_goal_talk_and_opportunities', 'score': 0.9995419979095459}]
+```
+
+# Model Details
+
+- **Developed by:** [Steven Bethard](https://bethard.github.io/)
+- **Parent Model:** [roBERTa-base](https://huggingface.co/roberta-base)
+- **GitHub Repo:** [LIvES repo](https://github.com/clulab/lives)
+
+# Uses
+
+The model is intended to be used for text classification, taking as input conversational utterances and predicting as output different categories of motivational interviewing behaviors.
+
+It is intended for use by health coaches to assist when reviewing their past calls with participants. Its predictions should not be used without manual review.
+
+# Training Details
+
+The model was trained on data annotated under the grant [Using Natural Language Processing to Determine Predictors of Healthy Diet and Physical Activity Behavior Change in Ovarian Cancer Survivors (NIH NCI R21CA256680)](https://reporter.nih.gov/project-details/10510666). A [roberta-base](https://huggingface.co/roberta-base) model was fine-tuned on that dataset, with texts tokenized using the standard [roberta-base](https://huggingface.co/roberta-base) tokenizer.
+ 
+# Evaluation
+
+On the test partition of the R21CA256680 dataset, the model achieves 0.60 precision and 0.46 recall.