update model card README.md

README.md

@@ -1,12 +1,37 @@
 ---
-license: apache-2.0
+license: mit
 tags:
 - generated_from_trainer
 datasets:
 - go_emotions
+metrics:
+- accuracy
+- precision
+- recall
+- f1
 model-index:
 - name: text-classification-goemotions
-  results: []
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: go_emotions
+      type: go_emotions
+      args: simplified
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.4327312937707335
+    - name: Precision
+      type: precision
+      value: 0.5160135896476034
+    - name: Recall
+      type: recall
+      value: 0.5555752222497288
+    - name: F1
+      type: f1
+      value: 0.5226068504396464
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -14,7 +39,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # text-classification-goemotions
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the go_emotions dataset.
+This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the go_emotions dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0830
+- Accuracy: 0.4327
+- Precision: 0.5160
+- Recall: 0.5556
+- F1: 0.5226
 
 ## Model description
 
@@ -35,14 +66,20 @@ More information needed
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
 - train_batch_size: 128
-- eval_batch_size: 16
+- eval_batch_size: 128
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 15
+- num_epochs: 4
 
 ### Training results
 
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| No log        | 1.0   | 340  | 0.0884          | 0.3782   | 0.4798    | 0.4643 | 0.4499 |
+| 0.1042        | 2.0   | 680  | 0.0829          | 0.4093   | 0.4766    | 0.5272 | 0.4879 |
+| 0.1042        | 3.0   | 1020 | 0.0821          | 0.4202   | 0.5103    | 0.5531 | 0.5092 |
+| 0.0686        | 4.0   | 1360 | 0.0830          | 0.4327   | 0.5160    | 0.5556 | 0.5226 |
 
 
 ### Framework versions