Update README.md

README.md

@@ -4,7 +4,7 @@ base_model: roberta-large
 tags:
 - generated_from_trainer
 datasets:
-- open_question_type
+- launch/open_question_type
 metrics:
 - f1
 model-index:
@@ -14,38 +14,52 @@ model-index:
       name: Text Classification
       type: text-classification
     dataset:
-      name: open_question_type
-      type: open_question_type
+      name: launch/open_question_type
+      type: launch/open_question_type
       config: default
       split: validation
       args: default
     metrics:
-    - name: F1
+    - name: F1 (macro avg.)
       type: f1
-      value: 0.7954091951908298
+      value: 0.8123190611646329
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: launch/open_question_type
+      type: launch/open_question_type
+      config: default
+      split: test
+      args: default
+    metrics:
+    - name: F1 (macro avg.)
+      type: f1
+      value: 0.80
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 
 # roberta-large-question-classifier
 
-This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the open_question_type dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.9002
-- F1: 0.7954
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
+This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [open_question_type](https://huggingface.co/datasets/launch/open_question_type) dataset.
+It achieves the following results on the test set:
+
+```
+              precision    recall  f1-score   support
+       cause       0.91      0.93      0.92        91
+  comparison       0.62      0.83      0.71        30
+     concept       0.85      0.65      0.74        54
+ consequence       0.80      0.73      0.76        11
+ disjunction       0.80      0.78      0.79        36
+     example       0.83      0.85      0.84       139
+      extent       0.82      0.94      0.87        48
+  judgmental       0.68      0.56      0.62        94
+  procedural       0.86      0.88      0.87        85
+verification       0.79      0.86      0.83        72
+    accuracy                           0.81       660
+   macro avg       0.80      0.80      0.80       660
+weighted avg       0.81      0.81      0.81       660
+```
 
 ## Training procedure
 
@@ -102,4 +116,4 @@ The following hyperparameters were used during training:
 - Transformers 4.33.2
 - Pytorch 2.1.0+cu118
 - Datasets 2.14.5
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3