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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_2 |
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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_2 Task A |
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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: 0.1998 |
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- Macro-f1: 0.5295 |
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- Micro-f1: 0.6157 |
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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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| 0.2142 | 0.44 | 500 | 0.2887 | 0.2391 | 0.4263 | |
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| 0.172 | 0.89 | 1000 | 0.2672 | 0.2908 | 0.4628 | |
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| 0.1737 | 1.33 | 1500 | 0.2612 | 0.3657 | 0.5102 | |
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| 0.1581 | 1.78 | 2000 | 0.2412 | 0.3958 | 0.5468 | |
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| 0.1509 | 2.22 | 2500 | 0.2264 | 0.3950 | 0.5552 | |
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| 0.1606 | 2.67 | 3000 | 0.2342 | 0.4006 | 0.5511 | |
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| 0.1491 | 3.11 | 3500 | 0.2176 | 0.4558 | 0.5622 | |
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| 0.1392 | 3.56 | 4000 | 0.2454 | 0.4128 | 0.5596 | |
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| 0.15 | 4.0 | 4500 | 0.2113 | 0.4684 | 0.5874 | |
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| 0.1461 | 4.44 | 5000 | 0.2179 | 0.4631 | 0.5815 | |
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| 0.1457 | 4.89 | 5500 | 0.2151 | 0.4805 | 0.5949 | |
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| 0.1443 | 5.33 | 6000 | 0.2155 | 0.5123 | 0.5917 | |
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| 0.1279 | 5.78 | 6500 | 0.2131 | 0.4915 | 0.5998 | |
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| 0.1377 | 6.22 | 7000 | 0.2244 | 0.4705 | 0.5944 | |
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| 0.1242 | 6.67 | 7500 | 0.2150 | 0.5089 | 0.5918 | |
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| 0.1222 | 7.11 | 8000 | 0.2045 | 0.4801 | 0.5981 | |
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| 0.1372 | 7.56 | 8500 | 0.2074 | 0.5317 | 0.5962 | |
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| 0.1289 | 8.0 | 9000 | 0.2035 | 0.5323 | 0.6126 | |
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| 0.1295 | 8.44 | 9500 | 0.2058 | 0.5213 | 0.6073 | |
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| 0.123 | 8.89 | 10000 | 0.2027 | 0.5486 | 0.6135 | |
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| 0.1335 | 9.33 | 10500 | 0.1984 | 0.5442 | 0.6249 | |
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| 0.1258 | 9.78 | 11000 | 0.1998 | 0.5295 | 0.6157 | |
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### Framework versions |
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- Transformers 4.19.2 |
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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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