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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: albert/albert-base-v2 |
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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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model-index: |
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- name: albert-base-v2-grammar-ner |
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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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# albert-base-v2-grammar-ner |
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This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1134 |
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- Accuracy: 0.9870 |
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- F1 Macro: 0.7941 |
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- F1 Micro: 0.9008 |
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- Precision Macro: 0.8789 |
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- Precision Micro: 0.9569 |
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- Recall Macro: 0.7518 |
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- Recall Micro: 0.8510 |
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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: 24 |
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- eval_batch_size: 24 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 18 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro | Precision Macro | Precision Micro | Recall Macro | Recall Micro | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|:---------------:|:------------:|:------------:| |
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| 0.4297 | 1.0 | 93 | 0.2896 | 0.9313 | 0.1318 | 0.4462 | 0.1897 | 0.5163 | 0.1281 | 0.3928 | |
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| 0.2521 | 2.0 | 186 | 0.2192 | 0.9452 | 0.2315 | 0.5160 | 0.3282 | 0.6752 | 0.1962 | 0.4176 | |
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| 0.167 | 3.0 | 279 | 0.1630 | 0.9662 | 0.3546 | 0.7198 | 0.4142 | 0.8358 | 0.3295 | 0.6321 | |
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| 0.1026 | 4.0 | 372 | 0.1343 | 0.9733 | 0.4185 | 0.7769 | 0.5241 | 0.8732 | 0.3797 | 0.6998 | |
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| 0.0718 | 5.0 | 465 | 0.1231 | 0.9738 | 0.4644 | 0.7794 | 0.5584 | 0.8525 | 0.4382 | 0.7178 | |
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| 0.0483 | 6.0 | 558 | 0.1269 | 0.9778 | 0.4778 | 0.8204 | 0.6262 | 0.9415 | 0.4164 | 0.7269 | |
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| 0.0335 | 7.0 | 651 | 0.1162 | 0.9804 | 0.6028 | 0.8416 | 0.6985 | 0.8834 | 0.5846 | 0.8036 | |
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| 0.0233 | 8.0 | 744 | 0.1203 | 0.9813 | 0.5736 | 0.8475 | 0.7429 | 0.9496 | 0.4988 | 0.7652 | |
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| 0.0171 | 9.0 | 837 | 0.1052 | 0.9836 | 0.6502 | 0.8671 | 0.7023 | 0.8964 | 0.6490 | 0.8397 | |
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| 0.01 | 10.0 | 930 | 0.1125 | 0.9805 | 0.6681 | 0.8477 | 0.6854 | 0.8535 | 0.6875 | 0.8420 | |
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| 0.0084 | 11.0 | 1023 | 0.1058 | 0.9862 | 0.7195 | 0.8894 | 0.8004 | 0.9287 | 0.6870 | 0.8533 | |
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| 0.0051 | 12.0 | 1116 | 0.1092 | 0.9870 | 0.8015 | 0.9015 | 0.8810 | 0.95 | 0.7612 | 0.8578 | |
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| 0.0031 | 13.0 | 1209 | 0.1131 | 0.9865 | 0.8006 | 0.8983 | 0.8827 | 0.9429 | 0.7592 | 0.8578 | |
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| 0.0017 | 14.0 | 1302 | 0.1106 | 0.9873 | 0.8058 | 0.9039 | 0.8748 | 0.9525 | 0.7749 | 0.8600 | |
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| 0.0012 | 15.0 | 1395 | 0.1111 | 0.9875 | 0.7985 | 0.9058 | 0.8818 | 0.9596 | 0.7576 | 0.8578 | |
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| 0.0009 | 16.0 | 1488 | 0.1128 | 0.9870 | 0.7941 | 0.9008 | 0.8789 | 0.9569 | 0.7518 | 0.8510 | |
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| 0.0008 | 17.0 | 1581 | 0.1133 | 0.9870 | 0.7941 | 0.9008 | 0.8789 | 0.9569 | 0.7518 | 0.8510 | |
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| 0.0008 | 18.0 | 1674 | 0.1134 | 0.9870 | 0.7941 | 0.9008 | 0.8789 | 0.9569 | 0.7518 | 0.8510 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.20.3 |
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