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--- |
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license: apache-2.0 |
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base_model: google/bert_uncased_L-6_H-256_A-4 |
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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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model-index: |
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- name: bert_uncased_L-6_H-256_A-4_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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config: split |
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split: validation |
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args: split |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.938 |
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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_uncased_L-6_H-256_A-4_emotion |
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This model is a fine-tuned version of [google/bert_uncased_L-6_H-256_A-4](https://huggingface.co./google/bert_uncased_L-6_H-256_A-4) on the emotion dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1706 |
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- Accuracy: 0.938 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 33 |
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- distributed_type: multi-GPU |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.0209 | 1.0 | 250 | 0.4902 | 0.872 | |
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| 0.3573 | 2.0 | 500 | 0.2427 | 0.9235 | |
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| 0.2124 | 3.0 | 750 | 0.1885 | 0.9295 | |
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| 0.1605 | 4.0 | 1000 | 0.1815 | 0.9335 | |
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| 0.137 | 5.0 | 1250 | 0.1623 | 0.9355 | |
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| 0.1122 | 6.0 | 1500 | 0.1695 | 0.934 | |
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| 0.0968 | 7.0 | 1750 | 0.1671 | 0.935 | |
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| 0.0902 | 8.0 | 2000 | 0.1702 | 0.933 | |
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| 0.08 | 9.0 | 2250 | 0.1684 | 0.937 | |
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| 0.0724 | 10.0 | 2500 | 0.1706 | 0.938 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 1.14.0a0+410ce96 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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