Update README.md


README.md

@@ -1,49 +1,64 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: distilbert-base-uncased
-tags:
-- generated_from_trainer
-model-index:
-- name: results
-  results: []
----
-
-<!-- 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. -->
-
-# results
-
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 256
-- eval_batch_size: 256
-- seed: 42
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 5
-
-### Framework versions
-
-- Transformers 4.46.2
-- Pytorch 2.5.1+cpu
-- Datasets 3.1.0
-- Tokenizers 0.20.3
+---
+library_name: transformers
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- emotion-classification
+- text-classification
+- distilbert
+datasets:
+- dair-ai/emotion
+language:
+- en
+metrics:
+- accuracy
+---
+
+<!-- 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. -->
+
+# results
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an Emotion Dataset.
+
+## Model description
+
+This model fine-tunes DistilBERT for emotion classification. It can detect emotions in language and then classify them into: sadness, joy, love, anger, fear, surprise.
+
+## Intended uses & limitations
+
+Used to explain the inner emotions of simple sentences. This model may lack contextual reasoning ability and cannot understand connecting words such as transitions.
+
+## Training and evaluation data
+
+- Training Dataset: dair-ai/emotion (16,000 examples)
+- Validation set: 2,000 examples
+- Test set: 2,000 examples
+- Validation Accuracy:
+  - epoch1:0.893
+  - epoch2:0.9365
+  - epoch3:0.937
+  - epoch4:0.939
+  - epoch5:0.942
+- Test Accuracy: 0.942
+- Training Time: 1:57:01
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 256
+- eval_batch_size: 256
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.1+cpu
+- Datasets 3.1.0
+- Tokenizers 0.20.3