Update README.md

README.md

@@ -1,3 +1,95 @@
 ---
 license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: distilbert-base-uncased-finetuned-emotion-balanced
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: emotion-balanced
+      type: emotion
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9521
+    - name: Loss
+      type: loss
+      value: 0.1216
+    - name: F1
+      type: f1
+      value: 0.9520944952964783
+widget:
+- text: Your actions were very caring.
+  example_title: Test sentence
 ---
+
+<!-- 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-emotion
+
+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: 0.1216
+- Accuracy: 0.9521
+
+## Model description
+
+This emotion classifier has been trained on 89_754 examples split into train, validation and test. Each label was perfectly balanced in each split.
+
+## 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: 3e-05
+- train_batch_size: 32
+- eval_batch_size: 64
+- seed: 1270
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 150
+- num_epochs: 1
+- weight_decay: 0.01
+
+### Training results
+
+              precision    recall  f1-score   support
+
+     sadness     0.9882    0.9485    0.9679      1496
+         joy     0.9956    0.9057    0.9485      1496
+        love     0.9256    0.9980    0.9604      1496
+       anger     0.9628    0.9519    0.9573      1496
+        fear     0.9348    0.9098    0.9221      1496
+    surprise     0.9160    0.9987    0.9555      1496
+
+    accuracy                         0.9521      8976
+   macro avg     0.9538    0.9521    0.9520      8976
+weighted avg     0.9538    0.9521    0.9520      8976
+
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃        Test metric        ┃       DataLoader 0        ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│         test_acc          │    0.9520944952964783     │
+│         test_loss         │     0.121663898229599     │
+└───────────────────────────┴───────────────────────────┘
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch lightning 2.0.8
+- Tokenizers 0.13.3