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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: CNEC1_1_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: 0.8521036974075649 |
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- name: Recall |
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type: recall |
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value: 0.8721183123096998 |
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- name: F1 |
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type: f1 |
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value: 0.8619948409286329 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9512518524296076 |
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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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# CNEC1_1_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: 0.3816 |
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- Precision: 0.8521 |
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- Recall: 0.8721 |
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- F1: 0.8620 |
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- Accuracy: 0.9513 |
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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: 4 |
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- eval_batch_size: 4 |
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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: 15 |
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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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| 0.4004 | 1.0 | 1174 | 0.2747 | 0.7598 | 0.7876 | 0.7735 | 0.9381 | |
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| 0.2765 | 2.0 | 2348 | 0.2268 | 0.8181 | 0.8340 | 0.8260 | 0.9506 | |
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| 0.2104 | 3.0 | 3522 | 0.2400 | 0.8318 | 0.8561 | 0.8438 | 0.9524 | |
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| 0.1713 | 4.0 | 4696 | 0.2285 | 0.8353 | 0.8645 | 0.8496 | 0.9552 | |
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| 0.1241 | 5.0 | 5870 | 0.2278 | 0.8458 | 0.8715 | 0.8584 | 0.9585 | |
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| 0.0997 | 6.0 | 7044 | 0.2717 | 0.8372 | 0.8653 | 0.8511 | 0.9559 | |
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| 0.0878 | 7.0 | 8218 | 0.2599 | 0.8439 | 0.8830 | 0.8630 | 0.9583 | |
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| 0.0585 | 8.0 | 9392 | 0.2868 | 0.8415 | 0.8764 | 0.8586 | 0.9564 | |
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| 0.0489 | 9.0 | 10566 | 0.2900 | 0.8594 | 0.8795 | 0.8693 | 0.9568 | |
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| 0.0416 | 10.0 | 11740 | 0.3061 | 0.8646 | 0.8852 | 0.8748 | 0.9598 | |
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| 0.0316 | 11.0 | 12914 | 0.3240 | 0.8567 | 0.8843 | 0.8703 | 0.9576 | |
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| 0.0264 | 12.0 | 14088 | 0.3329 | 0.8546 | 0.8795 | 0.8668 | 0.9588 | |
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| 0.0184 | 13.0 | 15262 | 0.3475 | 0.8628 | 0.8804 | 0.8715 | 0.9584 | |
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| 0.0156 | 14.0 | 16436 | 0.3472 | 0.8654 | 0.8826 | 0.8739 | 0.9592 | |
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| 0.0125 | 15.0 | 17610 | 0.3539 | 0.8670 | 0.8861 | 0.8764 | 0.9593 | |
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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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