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--- |
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license: mit |
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base_model: microsoft/MiniLM-L12-H384-uncased |
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tags: |
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- Language |
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- image-Emotion |
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- miniLM |
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- PyTorch |
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- Trainer |
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- SequenceClassification |
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- WeightedLoss |
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- CrossEntropyLoss |
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- F1Score |
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- HuggingFaceHub |
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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_2024_06_17 |
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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: F1 |
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type: f1 |
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value: 0.9349971922956838 |
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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_2024_06_17 |
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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: 0.4059 |
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- F1: 0.9350 |
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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: 50 |
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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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| 1.3684 | 1.0 | 250 | 1.0416 | 0.5803 | |
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| 0.8635 | 2.0 | 500 | 0.6225 | 0.8729 | |
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| 0.5165 | 3.0 | 750 | 0.3755 | 0.9130 | |
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| 0.3319 | 4.0 | 1000 | 0.2792 | 0.9256 | |
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| 0.2494 | 5.0 | 1250 | 0.2474 | 0.9252 | |
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| 0.1914 | 6.0 | 1500 | 0.2182 | 0.9290 | |
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| 0.156 | 7.0 | 1750 | 0.2140 | 0.9307 | |
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| 0.1435 | 8.0 | 2000 | 0.1807 | 0.9351 | |
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| 0.1258 | 9.0 | 2250 | 0.1830 | 0.9353 | |
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| 0.1128 | 10.0 | 2500 | 0.1655 | 0.9404 | |
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| 0.1023 | 11.0 | 2750 | 0.1968 | 0.9339 | |
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| 0.0967 | 12.0 | 3000 | 0.1816 | 0.9333 | |
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| 0.0914 | 13.0 | 3250 | 0.1840 | 0.9338 | |
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| 0.0818 | 14.0 | 3500 | 0.2094 | 0.9316 | |
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| 0.0755 | 15.0 | 3750 | 0.1945 | 0.9345 | |
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| 0.0718 | 16.0 | 4000 | 0.2040 | 0.9325 | |
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| 0.0641 | 17.0 | 4250 | 0.2230 | 0.9369 | |
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| 0.0613 | 18.0 | 4500 | 0.2349 | 0.9332 | |
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| 0.0556 | 19.0 | 4750 | 0.2530 | 0.9249 | |
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| 0.0521 | 20.0 | 5000 | 0.2334 | 0.9376 | |
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| 0.0526 | 21.0 | 5250 | 0.2531 | 0.9306 | |
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| 0.0423 | 22.0 | 5500 | 0.2336 | 0.9383 | |
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| 0.039 | 23.0 | 5750 | 0.2848 | 0.9352 | |
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| 0.0435 | 24.0 | 6000 | 0.2955 | 0.9363 | |
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| 0.0371 | 25.0 | 6250 | 0.3075 | 0.9362 | |
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| 0.0338 | 26.0 | 6500 | 0.2910 | 0.9339 | |
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| 0.0319 | 27.0 | 6750 | 0.3133 | 0.9343 | |
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| 0.0305 | 28.0 | 7000 | 0.3106 | 0.9344 | |
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| 0.0254 | 29.0 | 7250 | 0.3155 | 0.9370 | |
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| 0.0288 | 30.0 | 7500 | 0.3310 | 0.9339 | |
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| 0.0228 | 31.0 | 7750 | 0.3463 | 0.9364 | |
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| 0.0224 | 32.0 | 8000 | 0.3618 | 0.9353 | |
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| 0.0207 | 33.0 | 8250 | 0.3720 | 0.9347 | |
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| 0.022 | 34.0 | 8500 | 0.3672 | 0.9374 | |
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| 0.0222 | 35.0 | 8750 | 0.3525 | 0.9388 | |
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| 0.0197 | 36.0 | 9000 | 0.3848 | 0.9384 | |
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| 0.0196 | 37.0 | 9250 | 0.3722 | 0.9369 | |
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| 0.0175 | 38.0 | 9500 | 0.3490 | 0.9350 | |
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| 0.0168 | 39.0 | 9750 | 0.3539 | 0.9365 | |
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| 0.0167 | 40.0 | 10000 | 0.3590 | 0.9391 | |
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| 0.0144 | 41.0 | 10250 | 0.3824 | 0.9382 | |
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| 0.0164 | 42.0 | 10500 | 0.3973 | 0.9322 | |
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| 0.0124 | 43.0 | 10750 | 0.3892 | 0.9372 | |
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| 0.012 | 44.0 | 11000 | 0.4102 | 0.9333 | |
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| 0.0142 | 45.0 | 11250 | 0.3921 | 0.9366 | |
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| 0.012 | 46.0 | 11500 | 0.3925 | 0.9361 | |
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| 0.0097 | 47.0 | 11750 | 0.3924 | 0.9360 | |
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| 0.0107 | 48.0 | 12000 | 0.3952 | 0.9330 | |
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| 0.0093 | 49.0 | 12250 | 0.4067 | 0.9360 | |
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| 0.0104 | 50.0 | 12500 | 0.4059 | 0.9350 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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