End of training

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- emotion
+metrics:
+- accuracy
+model-index:
+- name: distilbert-emotion-intent
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: emotion
+      type: emotion
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.937
+---
+
+<!-- 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-intent
+
+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: 0.1989
+- Accuracy: 0.937
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 33
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.3939        | 1.0   | 1000 | 0.2123          | 0.9285   |
+| 0.1539        | 2.0   | 2000 | 0.1635          | 0.936    |
+| 0.1213        | 3.0   | 3000 | 0.1820          | 0.931    |
+| 0.1016        | 4.0   | 4000 | 0.1989          | 0.937    |
+| 0.0713        | 5.0   | 5000 | 0.2681          | 0.935    |
+| 0.0462        | 6.0   | 6000 | 0.3034          | 0.9365   |
+| 0.027         | 7.0   | 7000 | 0.3538          | 0.937    |
+
+
+### Framework versions
+
+- Transformers 4.22.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.5.1
+- Tokenizers 0.12.1

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