Update README.md

README.md

@@ -3,36 +3,80 @@ license: apache-2.0
 base_model: distilbert-base-uncased
 tags:
 - generated_from_trainer
+datasets:
+- emotion
 metrics:
 - accuracy
 - f1
+widget:
+- text: on a boat trip to denmark
+  example_title: Example 1
+- text: i was feeling listless from the need of new things something different
+  example_title: Example 2
+- text: i know im feeling agitated as it is from a side effect of the too high dose
+  example_title: Example 3
 model-index:
-- name: distilbert-base-uncased-finetuned-depi2-emotion-2
-  results: []
+- name: distilbert-base-uncased-finetuned-emotions-dataset
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: emotion
+      type: emotion
+      config: split
+      split: validation
+      args: split
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9395
+    - name: F1
+      type: f1
+      value: 0.9396359245863207
+pipeline_tag: text-classification
+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-depi2-emotion-2
+# distilbert-base-uncased-finetuned-emotions-dataset
 
-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 the emotion dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1739
-- Accuracy: 0.9315
-- F1: 0.9318
+- Loss: 0.2428
+- Accuracy: 0.9395
+- F1: 0.9396
 
 ## 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
+Emotions dataset with labeled emotional categories [here](https://huggingface.co/datasets/dair-ai/emotion).
 
-More information needed
+#### The emotional categories are as follows:
+- LABEL_0: sadness 
+- LABEL_1: joy
+- LABEL_2: love
+- LABEL_3: anger
+- LABEL_4: fear
+- LABEL_5: surprise
 
 ## Training procedure
 
@@ -45,19 +89,27 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 2
+- num_epochs: 10
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
-| 0.6293        | 1.0   | 500  | 0.2308          | 0.919    | 0.9199 |
-| 0.1746        | 2.0   | 1000 | 0.1739          | 0.9315   | 0.9318 |
+| 0.5929        | 1.0   | 500  | 0.2345          | 0.9185   | 0.9180 |
+| 0.1642        | 2.0   | 1000 | 0.1716          | 0.9335   | 0.9342 |
+| 0.1163        | 3.0   | 1500 | 0.1501          | 0.9405   | 0.9407 |
+| 0.0911        | 4.0   | 2000 | 0.1698          | 0.933    | 0.9331 |
+| 0.0741        | 5.0   | 2500 | 0.1926          | 0.932    | 0.9323 |
+| 0.0559        | 6.0   | 3000 | 0.2033          | 0.935    | 0.9353 |
+| 0.0464        | 7.0   | 3500 | 0.2156          | 0.935    | 0.9353 |
+| 0.0335        | 8.0   | 4000 | 0.2354          | 0.9405   | 0.9408 |
+| 0.0257        | 9.0   | 4500 | 0.2410          | 0.9395   | 0.9396 |
+| 0.0214        | 10.0  | 5000 | 0.2428          | 0.9395   | 0.9396 |
 
 
 ### Framework versions
 
-- Transformers 4.44.0
-- Pytorch 2.4.0+cu121
-- Datasets 2.21.0
-- Tokenizers 0.19.1
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0