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--- |
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license: mit |
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base_model: xlm-roberta-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- conll2003 |
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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: xlm-roberta-large-finetuned-conll2003 |
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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: conll2003 |
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type: conll2003 |
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config: conll2003 |
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split: validation |
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args: conll2003 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9620781824256599 |
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- name: Recall |
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type: recall |
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value: 0.9692022887916526 |
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- name: F1 |
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type: f1 |
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value: 0.9656270959087861 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9936723647833028 |
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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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# xlm-roberta-large-finetuned-conll2003 |
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0412 |
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- Precision: 0.9621 |
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- Recall: 0.9692 |
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- F1: 0.9656 |
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- Accuracy: 0.9937 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 6 |
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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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| 0.1591 | 1.0 | 896 | 0.0440 | 0.9388 | 0.9451 | 0.9420 | 0.9896 | |
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| 0.0335 | 2.0 | 1792 | 0.0361 | 0.9512 | 0.9586 | 0.9549 | 0.9924 | |
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| 0.0195 | 3.0 | 2688 | 0.0378 | 0.9570 | 0.9636 | 0.9603 | 0.9931 | |
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| 0.0104 | 4.0 | 3584 | 0.0396 | 0.9587 | 0.9613 | 0.9600 | 0.9928 | |
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| 0.0064 | 5.0 | 4480 | 0.0400 | 0.9617 | 0.9675 | 0.9646 | 0.9937 | |
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| 0.0032 | 6.0 | 5376 | 0.0412 | 0.9621 | 0.9692 | 0.9656 | 0.9937 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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