Add README

README.md

@@ -1,3 +1,68 @@
----
-license: apache-2.0
----
+---
+license: mit
+language:
+- en
+tags:
+- roberta
+- text-classification
+- ensemble
+- clarity
+- qevasion
+pipeline_tag: text-classification
+---
+
+# RoBERTa Clarity Ensemble
+
+This repository contains **3 RoBERTa-large models** fine-tuned for clarity classification (Clear Reply / Clear Non-Reply / Ambivalent).
+
+## Models
+
+| Model | Description |
+|-------|-------------|
+| `model-1/` | RoBERTa-large fine-tuned on clarity task |
+| `model-2/` | RoBERTa-large fine-tuned on clarity task (different seed/split) |
+| `model-3/` | RoBERTa-large fine-tuned on clarity task (different seed/split) |
+
+## Usage
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load one model
+model = AutoModelForSequenceClassification.from_pretrained("gigibot/ensemble-qeval", subfolder="model-1")
+tokenizer = AutoTokenizer.from_pretrained("gigibot/ensemble-qeval", subfolder="model-1")
+
+# Or load all 3 for ensemble voting
+models = []
+for i in [1, 2, 3]:
+    m = AutoModelForSequenceClassification.from_pretrained(
+        "gigibot/ensemble-qeval",
+        subfolder=f"model-{{i}}"
+    )
+    models.append(m)
+
+# Ensemble inference
+def ensemble_predict(text, models, tokenizer):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
+    logits_sum = None
+    for model in models:
+        model.eval()
+        with torch.no_grad():
+            out = model(**inputs)
+            if logits_sum is None:
+                logits_sum = out.logits
+            else:
+                logits_sum += out.logits
+    return torch.argmax(logits_sum, dim=-1).item()
+```
+
+## Labels
+
+- 0: Clear Reply
+- 1: Clear Non-Reply  
+- 2: Ambivalent
+
+## Training
+
+Each model was fine-tuned from `roberta-large` on the QEvasion clarity dataset.