update model card README.md

README.md

@@ -16,15 +16,14 @@ model-index:
     dataset:
       name: emotion
       type: emotion
-      args: default
+      args: split
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.927
+      value: 0.9245
     - name: F1
       type: f1
-      value: 0.9271664736493986
-duplicated_from: transformersbook/distilbert-base-uncased-finetuned-emotion
+      value: 0.9244610483889744
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,12 +31,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 # distilbert-base-uncased-finetuned-emotion
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. The model is trained in Chapter 2: Text Classification in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/02_classification.ipynb).
-
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2192
-- Accuracy: 0.927
-- F1: 0.9272
+- Loss: 0.2193
+- Accuracy: 0.9245
+- F1: 0.9245
 
 ## Model description
 
@@ -68,13 +66,13 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
-| 0.8569        | 1.0   | 250  | 0.3386          | 0.894    | 0.8888 |
-| 0.2639        | 2.0   | 500  | 0.2192          | 0.927    | 0.9272 |
+| 0.8598        | 1.0   | 250  | 0.3274          | 0.9005   | 0.8966 |
+| 0.2584        | 2.0   | 500  | 0.2193          | 0.9245   | 0.9245 |
 
 
 ### Framework versions
 
-- Transformers 4.11.3
-- Pytorch 1.9.1+cu102
-- Datasets 1.13.0
+- Transformers 4.13.0
+- Pytorch 2.0.0+cu118
+- Datasets 2.8.0
 - Tokenizers 0.10.3