Update README.md

README.md

@@ -8,7 +8,28 @@ metrics:
 - f1
 model-index:
 - name: distilbert-base-multilingual-cased-finetuned
-  results: []
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: emotone_ar 
+      type: emotion
+      config: split
+      split: validation
+      args: split
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.6643
+    - name: F1
+      type: f1
+      value: 0.6611
+datasets:
+- emotone-ar-cicling2017/emotone_ar
+language:
+- ar
+pipeline_tag: text-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -24,15 +45,33 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+The model has been trained to classify text inputs into distinct emotional categories based on the fine-tuned understanding of the emotions dataset. 
+The fine-tuned model has demonstrated high accuracy and F1 scores on the evaluation set.
 
 ## Intended uses & limitations
 
-More information needed
+#### Intended Uses
+- Sentiment analysis
+- Emotional classification in text
+- Emotion-based recommendation systems
+
+#### Limitations
+- May show biases based on the training dataset
+- Optimized for emotional classification and may not cover nuanced emotional subtleties
 
 ## Training and evaluation data
 
-More information needed
+Emotions dataset with labeled emotional categories [here](https://huggingface.co/datasets/emotone-ar-cicling2017/emotone_ar).
+
+#### The emotional categories are as follows:
+  - LABEL_0 :  none
+  - LABEL_1 :  anger
+  - LABEL_2 :  joy
+  - LABEL_3 :  sadness
+  - LABEL_4 :  love
+  - LABEL_5 :  sympathy
+  - LABEL_6 :  surprise
+  - LABEL_7 :  fear
 
 ## Training procedure
 
@@ -68,4 +107,4 @@ The following hyperparameters were used during training:
 - Transformers 4.42.4
 - Pytorch 2.4.0+cu121
 - Datasets 2.21.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1