Update README.md

README.md

@@ -2,10 +2,16 @@
 license: apache-2.0
 base_model: distilbert-base-uncased
 tags:
-- generated_from_keras_callback
+- emotions
 model-index:
 - name: Distilbert-base-uncased_dair-ai_emotion
   results: []
+language:
+- en
+metrics:
+- accuracy
+- f1
+pipeline_tag: text-classification
 ---
 
 <!-- This model card has been generated automatically according to the information Keras had access to. You should
@@ -13,25 +19,37 @@ probably proofread and complete it, then remove this comment. -->
 
 # Distilbert-base-uncased_dair-ai_emotion
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an dair-ai/emotion dataset.
 It achieves the following results on the evaluation set:
 - Train Loss: 0.0896
 - Train Accuracy: 0.9582
 - Validation Loss: 0.1326
 - Validation Accuracy: 0.9375
-- Epoch: 3
+- Epoch: 4
 
 ## Model description
 
-More information needed
-
+Model takes text as input and outputs an predictions for one of the 6 emotions. 
+          { 0: 'sadness',
+            1:'joy',
+            2: "love",
+            3: "anger",
+            4: "fear",
+            5: "surprise"}
+            
 ## Intended uses & limitations
 
-More information needed
+Use to identify an emotion of a user from above mentioned emotions. 
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("Arjun4707/Distilbert-base-uncased_dair-ai_emotion")
+model = AutoModelForSequenceClassification.from_pretrained("Arjun4707/Distilbert-base-uncased_dair-ai_emotion", from_tf = True)
 
 ## Training and evaluation data
 
-More information needed
+Training data size = 16000, validation data = 2000, and test data = 2000
 
 ## Training procedure
 
@@ -48,12 +66,4 @@ The following hyperparameters were used during training:
 | 0.5820     | 0.8014         | 0.2002          | 0.9305              | 0     |
 | 0.1598     | 0.9366         | 0.1431          | 0.9355              | 1     |
 | 0.1101     | 0.9515         | 0.1390          | 0.9355              | 2     |
-| 0.0896     | 0.9582         | 0.1326          | 0.9375              | 3     |
-
-
-### Framework versions
-
-- Transformers 4.35.2
-- TensorFlow 2.15.0
-- Datasets 2.16.1
-- Tokenizers 0.15.1
+| 0.0896     | 0.9582         | 0.1326          | 0.9375              | 3     |