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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- emotion |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: distilbert-base-uncased-finetuned-emotion |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: emotion |
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type: emotion |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.937 |
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- name: F1 |
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type: f1 |
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value: 0.9372331942198677 |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: emotion |
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type: emotion |
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config: default |
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split: test |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.924 |
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verified: true |
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- name: Precision Macro |
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type: precision |
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value: 0.8811256547088461 |
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verified: true |
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- name: Precision Micro |
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type: precision |
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value: 0.924 |
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verified: true |
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- name: Precision Weighted |
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type: precision |
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value: 0.9250809835160841 |
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verified: true |
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- name: Recall Macro |
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type: recall |
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value: 0.8882276452967225 |
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verified: true |
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- name: Recall Micro |
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type: recall |
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value: 0.924 |
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verified: true |
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- name: Recall Weighted |
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type: recall |
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value: 0.924 |
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verified: true |
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- name: F1 Macro |
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type: f1 |
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value: 0.8844059421244559 |
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verified: true |
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- name: F1 Micro |
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type: f1 |
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value: 0.924 |
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verified: true |
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- name: F1 Weighted |
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type: f1 |
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value: 0.9243911585312775 |
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verified: true |
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- name: loss |
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type: loss |
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value: 0.15944455564022064 |
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verified: true |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilbert-base-uncased-finetuned-emotion |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1413 |
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- Accuracy: 0.937 |
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- F1: 0.9372 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.7628 | 1.0 | 250 | 0.2489 | 0.9155 | 0.9141 | |
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| 0.2014 | 2.0 | 500 | 0.1716 | 0.928 | 0.9283 | |
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| 0.1351 | 3.0 | 750 | 0.1456 | 0.937 | 0.9374 | |
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| 0.1046 | 4.0 | 1000 | 0.1440 | 0.9355 | 0.9349 | |
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| 0.0877 | 5.0 | 1250 | 0.1413 | 0.937 | 0.9372 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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