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--- |
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license: mit |
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base_model: facebook/xlm-v-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- massive |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: scenario-TCR-XLMV_data-AmazonScience_massive_all_1_1_beta2 |
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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: massive |
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type: massive |
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config: all_1.1 |
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split: validation |
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args: all_1.1 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8495213591130955 |
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- name: F1 |
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type: f1 |
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value: 0.8257523979629272 |
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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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# scenario-TCR-XLMV_data-AmazonScience_massive_all_1_1_beta2 |
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This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the massive dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8678 |
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- Accuracy: 0.8495 |
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- F1: 0.8258 |
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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: 67 |
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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: 500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| |
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| 0.6252 | 0.27 | 5000 | 0.7387 | 0.8183 | 0.7743 | |
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| 0.4497 | 0.53 | 10000 | 0.6721 | 0.8363 | 0.7908 | |
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| 0.3806 | 0.8 | 15000 | 0.6702 | 0.8451 | 0.8090 | |
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| 0.303 | 1.07 | 20000 | 0.7162 | 0.8457 | 0.8130 | |
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| 0.2732 | 1.34 | 25000 | 0.7250 | 0.8475 | 0.8178 | |
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| 0.2574 | 1.6 | 30000 | 0.7626 | 0.8449 | 0.8188 | |
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| 0.2565 | 1.87 | 35000 | 0.7255 | 0.8506 | 0.8251 | |
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| 0.2074 | 2.14 | 40000 | 0.7439 | 0.8524 | 0.8268 | |
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| 0.2139 | 2.41 | 45000 | 0.8088 | 0.8478 | 0.8233 | |
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| 0.2007 | 2.67 | 50000 | 0.7556 | 0.8476 | 0.8223 | |
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| 0.2012 | 2.94 | 55000 | 0.7599 | 0.8505 | 0.8250 | |
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| 0.1698 | 3.21 | 60000 | 0.8283 | 0.8481 | 0.8255 | |
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| 0.1728 | 3.47 | 65000 | 0.7996 | 0.8521 | 0.8320 | |
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| 0.1711 | 3.74 | 70000 | 0.7974 | 0.8520 | 0.8292 | |
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| 0.1623 | 4.01 | 75000 | 0.8819 | 0.8485 | 0.8223 | |
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| 0.1502 | 4.28 | 80000 | 0.8330 | 0.8534 | 0.8320 | |
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| 0.1605 | 4.54 | 85000 | 0.8250 | 0.8499 | 0.8264 | |
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| 0.1659 | 4.81 | 90000 | 0.8318 | 0.8493 | 0.8237 | |
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| 0.1241 | 5.08 | 95000 | 0.9368 | 0.8518 | 0.8191 | |
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| 0.1361 | 5.34 | 100000 | 0.9396 | 0.8510 | 0.8237 | |
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| 0.1481 | 5.61 | 105000 | 0.8678 | 0.8495 | 0.8258 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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