update model card README.md

README.md

@@ -7,22 +7,9 @@ metrics:
 - f1
 - precision
 - recall
-datasets:
-- amazon_us_reviews
 model-index:
 - name: distilbert-amazon-shoe-reviews
-  results:
-  - task:
-      type: text-classification
-      name: Text Classification
-    dataset:
-      type: amazon_us_reviews
-      name: Amazon US reviews
-      split: Shoes
-    metrics:
-    - type: accuracy
-      value: 0.6819221967963387
-      name: Accuracy
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.9536
-- Accuracy: 0.5767
-- F1: [0.62380713 0.45806452 0.5077951  0.56106774 0.73541247]
-- Precision: [0.62537764 0.45920398 0.49326923 0.58508403 0.72376238]
-- Recall: [0.62224449 0.45693069 0.52320245 0.53894533 0.74744376]
+- Loss: 0.9532
+- Accuracy: 0.5779
+- F1: [0.62616119 0.46456105 0.50993865 0.55755123 0.734375  ]
+- Precision: [0.62757927 0.46676662 0.49148534 0.58430541 0.72415507]
+- Recall: [0.6247495  0.46237624 0.52983172 0.53313982 0.74488753]
 
 ## Model description
 
@@ -67,12 +54,12 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1                                                       | Precision                                                | Recall                                                   |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------------------------------------------------:|:--------------------------------------------------------:|:--------------------------------------------------------:|
-| 0.9704        | 1.0   | 2813 | 0.9536          | 0.5767   | [0.62380713 0.45806452 0.5077951  0.56106774 0.73541247] | [0.62537764 0.45920398 0.49326923 0.58508403 0.72376238] | [0.62224449 0.45693069 0.52320245 0.53894533 0.74744376] |
+| 0.9713        | 1.0   | 2813 | 0.9532          | 0.5779   | [0.62616119 0.46456105 0.50993865 0.55755123 0.734375  ] | [0.62757927 0.46676662 0.49148534 0.58430541 0.72415507] | [0.6247495  0.46237624 0.52983172 0.53313982 0.74488753] |
 
 
 ### Framework versions
 
-- Transformers 4.19.2
-- Pytorch 1.11.0+cu102
-- Datasets 2.2.2
+- Transformers 4.20.1
+- Pytorch 1.12.0+cu102
+- Datasets 2.3.2
 - Tokenizers 0.12.1