Update README.md

README.md

@@ -1,3 +1,61 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- obx0x3/empathy-dementia
+language:
+- en
+- fr
+metrics:
+- bleu
+---
+
+## Empathy Dementia 
+A fine‑tuned multilingual T5‑Base model for empathetic responses designed for individuals with dementia.
+
+## Training Corpus
+- **Data splits:**
+  - Training: 120 examples
+  - Validation: 30 examples
+  - Test: 30 examples
+- **Languages:** English (`"en"`) and French (`"fr"`)
+- **Data structure:** Each sample includes:
+  ```json
+  {
+    "input": "Where are my glasses?",
+    "target": "Let’s look together. Maybe near your chair.",
+    "lang": "en"
+  }
+## Training Results (Cross-Entropy Loss)
+
+| Epoch | Training Loss | Validation Loss |
+|-------|---------------|-----------------|
+| 1     | 0.191100      | 0.101121        |
+| 2     | 0.119100      | 0.061524        |
+| 3     | 0.103100      | 0.042741        |
+| 4     | 0.079900      | 0.038426        |
+
+# Usage:
+
+```
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+model = T5ForConditionalGeneration.from_pretrained("obx0x3/empathy-dementia")
+tokenizer = T5Tokenizer.from_pretrained("obx0x3/empathy-dementia")
+
+def empathetic_response(prompt, lang="en"):
+    prefix = "emotion: " if lang == "en" else "émotion: "
+    input_ids = tokenizer(prefix + prompt, return_tensors="pt").input_ids
+    output_ids = model.generate(input_ids, max_length=50)
+    return tokenizer.decode(output_ids[0], skip_special_tokens=True)
+```
+
+# Example
+```
+print(empathetic_response("I feel so lonely.", lang="en"))
+print(empathetic_response("Je me sens seul.", lang="fr"))
+```
+
+## Intended Use & Limitations
+MSc Dissertion Proof of concept and work for primary use case: Aid communication with individuals experiencing memory loss, confusion, or distress
+
+NOT for clinical decisions — designed as a supportive tool