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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: CNEC_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: 0.8556554661618552 |
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- name: Recall |
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type: recall |
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value: 0.8972704714640198 |
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- name: F1 |
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type: f1 |
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value: 0.8759689922480619 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9759953161592506 |
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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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# CNEC_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: 0.1541 |
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- Precision: 0.8557 |
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- Recall: 0.8973 |
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- F1: 0.8760 |
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- Accuracy: 0.9760 |
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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: 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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| 0.2518 | 1.12 | 500 | 0.1312 | 0.7219 | 0.8427 | 0.7777 | 0.9649 | |
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| 0.0996 | 2.24 | 1000 | 0.1222 | 0.8003 | 0.8511 | 0.8249 | 0.9677 | |
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| 0.0652 | 3.36 | 1500 | 0.1259 | 0.8137 | 0.8734 | 0.8425 | 0.9730 | |
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| 0.0421 | 4.47 | 2000 | 0.1293 | 0.8306 | 0.8859 | 0.8573 | 0.9739 | |
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| 0.0277 | 5.59 | 2500 | 0.1519 | 0.8320 | 0.8799 | 0.8553 | 0.9742 | |
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| 0.0169 | 6.71 | 3000 | 0.1342 | 0.8516 | 0.8968 | 0.8736 | 0.9756 | |
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| 0.0116 | 7.83 | 3500 | 0.1496 | 0.8540 | 0.8973 | 0.8751 | 0.9760 | |
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| 0.0065 | 8.95 | 4000 | 0.1541 | 0.8557 | 0.8973 | 0.8760 | 0.9760 | |
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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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