Update README.md

README.md

@@ -1,3 +1,84 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- uzw/PlainFact
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- biology
+- medical
+- classification
+---
+
+> This plain language summary classification model is a part of the [PlainQAFact](https://github.com/zhiwenyou103/PlainQAFact) factuality evaluation framework. 
+
+
+## Classify the Input into Either Elaborative Explanation or Simplification
+We fine-tuned [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) model using our curated sentence-level [PlainFact](https://huggingface.co/datasets/uzw/PlainFact) dataset.
+
+## Model Overview
+[PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) is a BERT model pre-trained from scratch on PubMed abstracts and full-text articles. It's optimized for biomedical text understanding and can be fine-tuned for various classification tasks such as:
+
+- Medical document classification
+- Disease/symptom categorization
+- Clinical note classification
+- Biomedical relation extraction
+
+
+## How to use
+Here is how to use this model in PyTorch:
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load tokenizer and model
+model_name = "uzw/plainqafact-pls-classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+num_labels = 2  # e.g., binary classification
+model = AutoModelForSequenceClassification.from_pretrained(
+    model_name,
+    num_labels=num_labels
+)
+
+# Example text
+text = "Patient presents with acute myocardial infarction and elevated troponin levels."
+
+inputs = tokenizer(
+    text,
+    padding=True,
+    truncation=True,
+    max_length=512,
+    return_tensors="pt"
+)
+
+# Get predictions
+model.eval()
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(predictions, dim=-1)
+
+print(f"Predicted class: {predicted_class.item()}")
+print(f"Confidence scores: {predictions}")
+```
+
+
+## Citation
+If you use this QG model in your research, please cite with the following BibTex entry:
+```
+@misc{you2025plainqafactretrievalaugmentedfactualconsistency,
+      title={PlainQAFact: Retrieval-augmented Factual Consistency Evaluation Metric for Biomedical Plain Language Summarization}, 
+      author={Zhiwen You and Yue Guo},
+      year={2025},
+      eprint={2503.08890},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2503.08890}, 
+}
+```
+
+> Code: https://github.com/zhiwenyou103/PlainQAFact