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--- |
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license: mit |
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base_model: FacebookAI/xlm-roberta-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cnec |
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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: CNEC2_0_extended_xlm-roberta-large |
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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: cnec |
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type: cnec |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8492292870905588 |
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- name: Recall |
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type: recall |
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value: 0.8749379652605459 |
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- name: F1 |
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type: f1 |
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value: 0.8618919579564899 |
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- name: Accuracy |
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type: accuracy |
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value: 0.973155737704918 |
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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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# CNEC2_0_extended_xlm-roberta-large |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1467 |
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- Precision: 0.8492 |
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- Recall: 0.8749 |
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- F1: 0.8619 |
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- Accuracy: 0.9732 |
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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: 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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.508 | 0.56 | 500 | 0.2177 | 0.6604 | 0.6928 | 0.6762 | 0.9423 | |
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| 0.2268 | 1.12 | 1000 | 0.1923 | 0.7158 | 0.7960 | 0.7538 | 0.9512 | |
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| 0.183 | 1.68 | 1500 | 0.1580 | 0.7825 | 0.8303 | 0.8057 | 0.9636 | |
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| 0.1558 | 2.24 | 2000 | 0.1548 | 0.8077 | 0.8382 | 0.8227 | 0.9676 | |
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| 0.1371 | 2.8 | 2500 | 0.1278 | 0.8233 | 0.8511 | 0.8370 | 0.9701 | |
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| 0.1225 | 3.36 | 3000 | 0.1430 | 0.8128 | 0.8531 | 0.8324 | 0.9667 | |
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| 0.1166 | 3.92 | 3500 | 0.1389 | 0.8307 | 0.8501 | 0.8403 | 0.9681 | |
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| 0.101 | 4.48 | 4000 | 0.1323 | 0.8277 | 0.8655 | 0.8462 | 0.9708 | |
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| 0.0928 | 5.04 | 4500 | 0.1332 | 0.8434 | 0.8660 | 0.8546 | 0.9715 | |
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| 0.0848 | 5.6 | 5000 | 0.1273 | 0.8382 | 0.8665 | 0.8521 | 0.9727 | |
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| 0.0798 | 6.16 | 5500 | 0.1281 | 0.8447 | 0.8774 | 0.8608 | 0.9716 | |
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| 0.0688 | 6.72 | 6000 | 0.1340 | 0.8482 | 0.8734 | 0.8606 | 0.9728 | |
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| 0.0638 | 7.28 | 6500 | 0.1346 | 0.8549 | 0.8744 | 0.8646 | 0.9746 | |
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| 0.0585 | 7.84 | 7000 | 0.1415 | 0.8442 | 0.8764 | 0.8600 | 0.9730 | |
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| 0.0565 | 8.4 | 7500 | 0.1487 | 0.8377 | 0.8809 | 0.8587 | 0.9730 | |
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| 0.0497 | 8.96 | 8000 | 0.1416 | 0.8473 | 0.8784 | 0.8626 | 0.9740 | |
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| 0.0484 | 9.52 | 8500 | 0.1467 | 0.8492 | 0.8749 | 0.8619 | 0.9732 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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