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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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- text-classification |
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- emotion |
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- pytorch |
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language: |
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- en |
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datasets: |
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- emotion |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: distilbert-base-cased-emotion |
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results: |
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- task: |
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type: text-classification |
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name: text-classification |
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dataset: |
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name: emotion |
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type: emotion |
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config: default |
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split: validation |
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metrics: |
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- name: accuracy |
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type: accuracy |
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value: 0.9235 |
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verified: true |
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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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# distilbert-base-cased-emotion |
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**Training:** The model has been trained using the script provided in the following repository https://github.com/MorenoLaQuatra/transformers-tasks-templates |
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on [emotion](https://huggingface.co/datasets/emotion) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3272 |
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- Accuracy: 0.9235 |
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- F1: 0.9217 |
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- Precision: 0.9224 |
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- Recall: 0.9235 |
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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: 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: linear |
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- lr_scheduler_warmup_steps: 500 |
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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 | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.2776 | 1.0 | 500 | 0.2954 | 0.9 | 0.8957 | 0.9031 | 0.9 | |
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| 0.1887 | 2.0 | 1000 | 0.1716 | 0.934 | 0.9344 | 0.9370 | 0.934 | |
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| 0.119 | 3.0 | 1500 | 0.1614 | 0.9345 | 0.9342 | 0.9377 | 0.9345 | |
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| 0.1001 | 4.0 | 2000 | 0.2018 | 0.936 | 0.9353 | 0.9359 | 0.936 | |
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| 0.0704 | 5.0 | 2500 | 0.1925 | 0.935 | 0.9349 | 0.9354 | 0.935 | |
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| 0.0471 | 6.0 | 3000 | 0.2369 | 0.938 | 0.9373 | 0.9377 | 0.938 | |
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| 0.0322 | 7.0 | 3500 | 0.2693 | 0.938 | 0.9382 | 0.9392 | 0.938 | |
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| 0.0137 | 8.0 | 4000 | 0.2926 | 0.937 | 0.9371 | 0.9372 | 0.937 | |
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| 0.0099 | 9.0 | 4500 | 0.2964 | 0.9365 | 0.9362 | 0.9362 | 0.9365 | |
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| 0.0114 | 10.0 | 5000 | 0.3044 | 0.935 | 0.9349 | 0.9350 | 0.935 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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