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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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- tweet_eval |
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metrics: |
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- precision |
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- recall |
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model-index: |
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- name: bert-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: tweet_eval |
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type: tweet_eval |
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config: emotion |
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split: train |
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args: emotion |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.6916963308315679 |
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- name: Recall |
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type: recall |
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value: 0.7047852871456702 |
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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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# bert-emotion |
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3717 |
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- Precision: 0.6917 |
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- Recall: 0.7048 |
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- Fscore: 0.6955 |
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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: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| |
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| 0.8838 | 1.0 | 815 | 0.7944 | 0.7238 | 0.6662 | 0.6860 | |
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| 0.5708 | 2.0 | 1630 | 1.0606 | 0.6594 | 0.6139 | 0.6299 | |
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| 0.3045 | 3.0 | 2445 | 1.3717 | 0.6917 | 0.7048 | 0.6955 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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