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--- |
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license: mit |
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base_model: xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- universalner/universal_ner |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: UNER_subword_tk_en_lora_alpha_64_drop_0.3_rank_32_seed_42 |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: universalner/universal_ner en_ewt |
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type: universalner/universal_ner |
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config: en_ewt |
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split: validation |
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args: en_ewt |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7735665694849369 |
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- name: Recall |
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type: recall |
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value: 0.8240165631469979 |
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- name: F1 |
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type: f1 |
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value: 0.7979949874686717 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9840550320092251 |
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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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# UNER_subword_tk_en_lora_alpha_64_drop_0.3_rank_32_seed_42 |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the universalner/universal_ner en_ewt dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0607 |
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- Precision: 0.7736 |
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- Recall: 0.8240 |
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- F1: 0.7980 |
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- Accuracy: 0.9841 |
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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: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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: 20.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 392 | 0.0899 | 0.5755 | 0.7143 | 0.6374 | 0.9740 | |
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| 0.1782 | 2.0 | 784 | 0.0651 | 0.6961 | 0.7919 | 0.7409 | 0.9799 | |
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| 0.0539 | 3.0 | 1176 | 0.0664 | 0.7144 | 0.8209 | 0.7640 | 0.9815 | |
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| 0.0435 | 4.0 | 1568 | 0.0581 | 0.7170 | 0.8209 | 0.7654 | 0.9821 | |
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| 0.0435 | 5.0 | 1960 | 0.0584 | 0.7321 | 0.8261 | 0.7763 | 0.9820 | |
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| 0.0385 | 6.0 | 2352 | 0.0571 | 0.7409 | 0.8230 | 0.7798 | 0.9827 | |
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| 0.0342 | 7.0 | 2744 | 0.0580 | 0.7433 | 0.8333 | 0.7857 | 0.9829 | |
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| 0.0313 | 8.0 | 3136 | 0.0578 | 0.7744 | 0.8282 | 0.8004 | 0.9846 | |
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| 0.0295 | 9.0 | 3528 | 0.0566 | 0.7588 | 0.8271 | 0.7915 | 0.9835 | |
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| 0.0295 | 10.0 | 3920 | 0.0564 | 0.7756 | 0.8302 | 0.8020 | 0.9848 | |
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| 0.0272 | 11.0 | 4312 | 0.0557 | 0.7597 | 0.8344 | 0.7953 | 0.9835 | |
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| 0.0256 | 12.0 | 4704 | 0.0585 | 0.7787 | 0.8157 | 0.7968 | 0.9841 | |
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| 0.0248 | 13.0 | 5096 | 0.0574 | 0.7812 | 0.8240 | 0.8020 | 0.9845 | |
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| 0.0248 | 14.0 | 5488 | 0.0577 | 0.7604 | 0.8344 | 0.7957 | 0.9836 | |
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| 0.023 | 15.0 | 5880 | 0.0583 | 0.7812 | 0.8282 | 0.8040 | 0.9845 | |
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| 0.0222 | 16.0 | 6272 | 0.0595 | 0.7733 | 0.8333 | 0.8022 | 0.9841 | |
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| 0.0205 | 17.0 | 6664 | 0.0603 | 0.7755 | 0.8261 | 0.8 | 0.9839 | |
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| 0.0207 | 18.0 | 7056 | 0.0605 | 0.7744 | 0.8282 | 0.8004 | 0.9840 | |
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| 0.0207 | 19.0 | 7448 | 0.0611 | 0.7770 | 0.8333 | 0.8042 | 0.9842 | |
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| 0.0203 | 20.0 | 7840 | 0.0607 | 0.7736 | 0.8240 | 0.7980 | 0.9841 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
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