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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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- accuracy |
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- f1 |
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model-index: |
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- name: base-vanilla-target-tweet |
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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: Accuracy |
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type: accuracy |
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value: 0.7780748663101604 |
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- name: F1 |
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type: f1 |
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value: 0.7772664883136655 |
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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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# base-vanilla-target-tweet |
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This model is a fine-tuned version of [google/bert_uncased_L-12_H-768_A-12](https://huggingface.co/google/bert_uncased_L-12_H-768_A-12) on the tweet_eval dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8380 |
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- Accuracy: 0.7781 |
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- F1: 0.7773 |
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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: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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: constant |
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- num_epochs: 200 |
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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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| 0.3831 | 4.9 | 500 | 0.9800 | 0.7807 | 0.7785 | |
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| 0.0414 | 9.8 | 1000 | 1.4175 | 0.7754 | 0.7765 | |
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| 0.015 | 14.71 | 1500 | 1.6411 | 0.7754 | 0.7708 | |
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| 0.0166 | 19.61 | 2000 | 1.5930 | 0.7941 | 0.7938 | |
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| 0.0175 | 24.51 | 2500 | 1.3934 | 0.7888 | 0.7852 | |
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| 0.0191 | 29.41 | 3000 | 1.9407 | 0.7647 | 0.7658 | |
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| 0.0137 | 34.31 | 3500 | 1.8380 | 0.7781 | 0.7773 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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