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--- |
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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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base_model: distilbert-base-uncased |
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model-index: |
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- name: bertweet-base-finetuned-emotion |
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results: |
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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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args: default |
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metrics: |
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- type: accuracy |
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value: 0.9365 |
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name: Accuracy |
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- type: f1 |
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value: 0.9371 |
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name: F1 |
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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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- type: accuracy |
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value: 0.923 |
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name: Accuracy |
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verified: true |
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- type: precision |
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value: 0.8676576686813523 |
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name: Precision Macro |
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verified: true |
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- type: precision |
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value: 0.923 |
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name: Precision Micro |
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verified: true |
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- type: precision |
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value: 0.9268406401714973 |
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name: Precision Weighted |
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verified: true |
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- type: recall |
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value: 0.8945488803260702 |
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name: Recall Macro |
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verified: true |
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- type: recall |
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value: 0.923 |
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name: Recall Micro |
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verified: true |
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- type: recall |
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value: 0.923 |
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name: Recall Weighted |
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verified: true |
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- type: f1 |
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value: 0.8798961895301041 |
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name: F1 Macro |
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verified: true |
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- type: f1 |
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value: 0.923 |
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name: F1 Micro |
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verified: true |
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- type: f1 |
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value: 0.9241278880972197 |
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name: F1 Weighted |
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verified: true |
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- type: loss |
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value: 0.24626904726028442 |
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name: loss |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1995 |
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- Accuracy: 0.9365 |
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- F1: 0.9371 |
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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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| 0.475 | 1.0 | 503 | 0.2171 | 0.928 | 0.9292 | |
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| 0.1235 | 2.0 | 1006 | 0.1764 | 0.9365 | 0.9372 | |
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| 0.0802 | 3.0 | 1509 | 0.1788 | 0.938 | 0.9388 | |
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| 0.0531 | 4.0 | 2012 | 0.2005 | 0.938 | 0.9388 | |
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| 0.0367 | 5.0 | 2515 | 0.1995 | 0.9365 | 0.9371 | |
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### Framework versions |
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- Transformers 4.13.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 1.16.1 |
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- Tokenizers 0.10.3 |
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