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--- |
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license: cc-by-sa-4.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- lex_glue |
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model-index: |
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- name: ECHR_test_Merged |
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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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# ECHR_test_Merged |
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This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the lex_glue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2162 |
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- Macro-f1: 0.5607 |
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- Micro-f1: 0.6726 |
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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: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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 | Macro-f1 | Micro-f1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| |
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| 0.2278 | 0.44 | 500 | 0.3196 | 0.2394 | 0.4569 | |
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| 0.1891 | 0.89 | 1000 | 0.2827 | 0.3255 | 0.5112 | |
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| 0.1803 | 1.33 | 1500 | 0.2603 | 0.3961 | 0.5698 | |
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| 0.1676 | 1.78 | 2000 | 0.2590 | 0.4251 | 0.6003 | |
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| 0.1635 | 2.22 | 2500 | 0.2489 | 0.4186 | 0.6030 | |
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| 0.1784 | 2.67 | 3000 | 0.2445 | 0.4627 | 0.6159 | |
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| 0.1556 | 3.11 | 3500 | 0.2398 | 0.4757 | 0.6170 | |
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| 0.151 | 3.56 | 4000 | 0.2489 | 0.4725 | 0.6163 | |
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| 0.1564 | 4.0 | 4500 | 0.2289 | 0.5019 | 0.6416 | |
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| 0.1544 | 4.44 | 5000 | 0.2406 | 0.5013 | 0.6408 | |
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| 0.1516 | 4.89 | 5500 | 0.2351 | 0.5145 | 0.6510 | |
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| 0.1487 | 5.33 | 6000 | 0.2354 | 0.5164 | 0.6394 | |
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| 0.1385 | 5.78 | 6500 | 0.2385 | 0.5205 | 0.6486 | |
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| 0.145 | 6.22 | 7000 | 0.2337 | 0.5197 | 0.6529 | |
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| 0.1332 | 6.67 | 7500 | 0.2294 | 0.5421 | 0.6526 | |
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| 0.1293 | 7.11 | 8000 | 0.2167 | 0.5576 | 0.6652 | |
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| 0.1475 | 7.56 | 8500 | 0.2218 | 0.5676 | 0.6649 | |
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| 0.1376 | 8.0 | 9000 | 0.2203 | 0.5565 | 0.6709 | |
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| 0.1408 | 8.44 | 9500 | 0.2178 | 0.5541 | 0.6716 | |
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| 0.133 | 8.89 | 10000 | 0.2212 | 0.5692 | 0.6640 | |
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| 0.1363 | 9.33 | 10500 | 0.2148 | 0.5642 | 0.6736 | |
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| 0.1344 | 9.78 | 11000 | 0.2162 | 0.5607 | 0.6726 | |
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### Framework versions |
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- Transformers 4.19.4 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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