Update README.md

README.md

@@ -1,15 +1,16 @@
 ---
 license: apache-2.0
-base_model: distilbert-base-uncased
+base_model: Distilbert-finetuned-emotion
 tags:
 - generated_from_trainer
+- Pytorch
 datasets:
 - emotion
 metrics:
 - accuracy
 - f1
 model-index:
-- name: distilbert-base-uncased-finetuned-emotion
+- name: Distilbert-finetuned-emotion
   results:
   - task:
       name: Text Classification
@@ -27,14 +28,17 @@ model-index:
     - name: F1
       type: f1
       value: 0.923296474937779
+language:
+- en
+library_name: transformers
 ---
 
 <!-- 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. -->
 
-# distilbert-base-uncased-finetuned-emotion
+# Distilbert-finetuned-emotion
 
-distilbert is a variant of bert model(one of LLM models). This model with a classification head is used to classify the emotions of the input tweet. 
+Distilbert is a variant of bert model(one of LLM models). This model with a classification head is used to classify the emotions of the input tweet. 
 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: 0.2195
@@ -82,4 +86,4 @@ The following hyperparameters were used during training:
 - Transformers 4.37.2
 - Pytorch 2.1.0+cu121
 - Datasets 2.16.1
-- Tokenizers 0.15.1
+- Tokenizers 0.15.1