Update README.md

README.md

@@ -1,11 +1,22 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- medical
+- SOAP_notes_generation
+license: apache-2.0
+datasets:
+- SubashNeupane/dataset_SOAP_summary
+metrics:
+- bertscore
+- rouge
+base_model:
+- Qwen/Qwen2.5-14B-Instruct
+pipeline_tag: text-generation
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model is a LoRA fine-tuned version of base model Qwen/Qwen2.5-14B-Instruct to improve the SOAP (Subjective, Objective, Assessment, Plan) notes generation from an input doctor-patient dialog. This is an 8-bit quantized version. 
 
 
 
@@ -17,26 +28,35 @@ tags: []
 
 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]
+- **Developed by:** [Yehia Zakaria]
+- **Finetuned from model [Qwen/Qwen2.5-14B-Instruct]:**
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
 ## 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. -->
 
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("{HF_USERNAME}/{model_name}")
+model = AutoModelForCausalLM.from_pretrained(
+    "yehiazak/Qwen2.5-14B-Instruct-SOAP-tuned-Q8",
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Generate text
+prompt = "Your prompt here"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=512, temperature=0.2)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
@@ -78,12 +98,15 @@ Use the code below to get started with the model.
 ### Training Data
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+Dataset (total samples: 1473) was shuffled and slplit into:
+- Training samples: 1300
+- Validation samples: 173
 
-[More Information Needed]
 
 ### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+LoRA fine-tuning with 3 epochs.
 
 #### Preprocessing [optional]
 
@@ -120,13 +143,21 @@ Use the code below to get started with the model.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+BertScore computed using "microsoft/deberta-xlarge-mnli".
 
 [More Information Needed]
 
 ### Results
 
-[More Information Needed]
+===================
+EVALUATION RESULTS
+===================
+ROUGE-1: 0.7017
+ROUGE-2: 0.4914
+ROUGE-L: 0.6132
+BertScore Precision: 0.8494
+BertScore Recall: 0.8288
+BertScore F1: 0.8382
 
 #### Summary