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--- |
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license: mit |
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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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- f1 |
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model-index: |
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- name: minilm-finetuned-emotion_nm |
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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: F1 |
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type: f1 |
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value: 0.9322805793931607 |
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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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# minilm-finetuned-emotion_nm |
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This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the emotion dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1918 |
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- F1: 0.9323 |
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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: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 1.3627 | 1.0 | 250 | 1.0048 | 0.5936 | |
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| 0.8406 | 2.0 | 500 | 0.6477 | 0.8608 | |
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| 0.5344 | 3.0 | 750 | 0.4025 | 0.9099 | |
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| 0.3619 | 4.0 | 1000 | 0.3142 | 0.9188 | |
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| 0.274 | 5.0 | 1250 | 0.2489 | 0.9277 | |
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| 0.2225 | 6.0 | 1500 | 0.2320 | 0.9303 | |
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| 0.191 | 7.0 | 1750 | 0.2083 | 0.9298 | |
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| 0.1731 | 8.0 | 2000 | 0.1969 | 0.9334 | |
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| 0.1606 | 9.0 | 2250 | 0.1928 | 0.9362 | |
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| 0.1462 | 10.0 | 2500 | 0.1918 | 0.9323 | |
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### Framework versions |
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- Transformers 4.15.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.17.0 |
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- Tokenizers 0.10.3 |
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