Update README.md

README.md

@@ -10,75 +10,38 @@ base_model:
 pipeline_tag: text-classification
 ---
 
-# Model Card for Model ID
+# OphtaBERT Glaucoma Classifier
 
-<!-- Provide a quick summary of what the model is/does. -->
+Binary classification for glaucoma diagnosis extraction from unstructured clinical notes.
 
 
 
 ## Model Details
 
 ### Model Description
+This model is a fine tuned variant of OphthaBERT, which was pretrained on over 2 million clinical notes. This model has been fine tuned for binary classification on labeled clinical notes from Massachusetts Eye and Ear Infirmary.
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Finetuned from model [OphahtBERT-v2]:** 
 
 ## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
+We suggest utlizing this model zero-shot for generating binary glaucoma labels for each clininical notes. For continued training on limited data, we suggest freezing the first 10 layers of the model.
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+All variants of BERT are known to struggle with negations, but this model has been fine tuned to handle both cases and negations. The context window of the note is 512 tokens, so we suggest chunking notes that are longer than 512 tokens for inference.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+All pretrainig and fine tuning was done on anonymized notes from the Massachusetts Eye and Ear Infirmary
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```
+model = AutoModelForSequenceClassification.from_pretrained("ShahRishi/OphthaBERT-v2-glaucoma-binary"
+```
 
 ## Training Details