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--- |
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license: apache-2.0 |
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base_model: google-bert/bert-base-multilingual-cased |
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tags: |
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- generated_from_trainer |
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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: sentiment_deberta |
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results: [] |
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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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# sentiment_deberta |
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This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7123 |
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- Accuracy: 0.6938 |
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- F1: 0.6401 |
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- Precision: 0.6262 |
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- Recall: 0.6854 |
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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: 1e-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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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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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| 1.087 | 1.0 | 47 | 1.1008 | 0.2551 | 0.3042 | 0.4734 | 0.4956 | |
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| 0.9933 | 2.0 | 94 | 0.9692 | 0.5545 | 0.5098 | 0.5126 | 0.5496 | |
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| 0.8709 | 3.0 | 141 | 0.9352 | 0.5003 | 0.5003 | 0.5301 | 0.5804 | |
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| 0.8444 | 4.0 | 188 | 0.8729 | 0.5874 | 0.5602 | 0.5671 | 0.6204 | |
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| 0.7833 | 5.0 | 235 | 0.9394 | 0.4778 | 0.4980 | 0.5643 | 0.6353 | |
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| 0.7003 | 6.0 | 282 | 0.7279 | 0.6834 | 0.6306 | 0.6150 | 0.6828 | |
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| 0.6383 | 7.0 | 329 | 0.7808 | 0.6390 | 0.6123 | 0.6073 | 0.7007 | |
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| 0.5996 | 8.0 | 376 | 0.7379 | 0.6802 | 0.6367 | 0.6231 | 0.6993 | |
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| 0.5514 | 9.0 | 423 | 0.7846 | 0.6745 | 0.6204 | 0.6015 | 0.6901 | |
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| 0.4837 | 10.0 | 470 | 0.7123 | 0.6938 | 0.6401 | 0.6262 | 0.6854 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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