Update README.md

README.md

@@ -2,8 +2,12 @@
 license: cc-by-nc-4.0
 ---
 
+# **TrueTeacher**
+
 This is a **Factual Consistency Evaluation** model, introduced in the [TrueTeacher paper (Gekhman et al, 2023)](https://arxiv.org/pdf/2305.11171.pdf).
 
+## Model Details
+
 The model is optimized for evaluating factual consistency in **summarization**.
 
 It is the main model from the paper (see "T5-11B w. ANLI + TrueTeacher full" in Table 1) which is based on a **T5-11B** [(Raffel
@@ -18,8 +22,20 @@ To accomodate the input length of common summarization datasets we recommend set
 
 The model predicts a binary label ('1' - Factualy Consistent, '0' - Factualy Inconsistent).
 
+## Evaluation results
+
+This model achieves the following ROC AUC results on the summarization subset of the [TRUE benchmark (Honovich et al, 2022)](https://arxiv.org/pdf/2204.04991.pdf):
+
+| **MNBM** | **QAGS-X** | **FRANK** | **SummEval** | **QAGS-C** | **Average** |
+|----------|-----------|-----------|--------------|-----------|-------------|
+| 78.1     | 89.4      | 93.6      | 88.5         | 89.4      | 87.8        |
+
+
+
+
+## Usage examples
 
-## Usage example - classification:
+#### classification
 ```python
 from transformers import T5ForConditionalGeneration
 from transformers import T5Tokenizer
@@ -43,7 +59,7 @@ for hypothesis, expected in [('the sun is out in the sky', '1'),
   print(f'result: {result} (expected: {expected})\n')
 ```
 
-## Usage example - scoring:
+#### scoring
 ```python
 from transformers import T5ForConditionalGeneration
 from transformers import T5Tokenizer