Update README.md

README.md

@@ -1,3 +1,47 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+---
+
+Welcome to **EmoCaliber**, an MLLM for reliable visual emotion comprehension.
+
+Given an image, EmoCaliber is trained to produce structured affective reasoning following this pipeline: (1) identifying prominent visual elements in the image; (2) providing detailed descriptions of human subjects, if present; (3) describing contextual elements beyond the subjects; (4) discussing how these elements interact; and (5) deriving an emotional conclusion based on the preceding observations. The final emotion prediction integrates these visual cues. After outputting the prediction, EmoCaliber also emits a confidence score wrapped in a \<confidence\> tag, which reflects the model’s self-assessed certainty about its answer.
+
+Standard prompt templates:
+
+**For emotion recognition**:
+
+```json
+{
+  "conversations": [
+    {
+      "role": "user",
+      "content": [
+        {"type": "image", "image": "IMAGE_PATH"},
+        {
+          "type": "text",
+          "text": "Which emotion might this image evoke? Choose the most likely one from ['EMOTION_CATEGORIES']. Think step by step. Respond in the format: <think>{your reasoning}</think><answer>{your final answer}</answer>."
+        }
+      ]
+    }
+  ]
+}
+```
+
+**For sentiment analysis**:
+
+```json
+{
+  "conversations": [
+    {
+      "role": "user",
+      "content": [
+        {"type": "image", "image": "IMAGE_PATH"},
+        {
+          "type": "text",
+          "text": "What sentiment might this image evoke? Choose the most likely one from ['positive', 'negative']. Think step by step. Respond in the format: <think>{your reasoning}</think><answer>{your final answer}</answer>."
+        }
+      ]
+    }
+  ]
+}
+```