Training Completed!

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- emotion
+metrics:
+- accuracy
+- f1
+- precision
+model-index:
+- name: distilbert-base-uncased_emotion_ft_0403
+  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: 0.9355
+    - name: F1
+      type: f1
+      value: 0.9355492121206211
+    - name: Precision
+      type: precision
+      value: 0.9171969908647357
+---
+
+<!-- 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_emotion_ft_0403
+
+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: 0.1551
+- Accuracy: 0.9355
+- F1: 0.9355
+- Precision: 0.9172
+
+## 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: 2e-05
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 4
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|
+| 0.8077        | 1.0   | 250  | 0.2865          | 0.9105   | 0.9107 | 0.8848    |
+| 0.2054        | 2.0   | 500  | 0.1811          | 0.9315   | 0.9322 | 0.9022    |
+| 0.1392        | 3.0   | 750  | 0.1686          | 0.933    | 0.9332 | 0.9103    |
+| 0.1111        | 4.0   | 1000 | 0.1551          | 0.9355   | 0.9355 | 0.9172    |
+
+
+### Framework versions
+
+- Transformers 4.34.0
+- Pytorch 2.1.2+cu118
+- Datasets 2.14.4
+- Tokenizers 0.14.1

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