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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_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: 0.8533541341653667 |
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- name: Recall |
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type: recall |
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value: 0.8770710849812934 |
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- name: F1 |
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type: f1 |
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value: 0.8650500790722193 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9670664608320468 |
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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_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: 0.1498 |
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- Precision: 0.8534 |
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- Recall: 0.8771 |
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- F1: 0.8651 |
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- Accuracy: 0.9671 |
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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: 5 |
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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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| 0.3961 | 1.0 | 581 | 0.1800 | 0.8004 | 0.8231 | 0.8116 | 0.9560 | |
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| 0.1772 | 2.0 | 1162 | 0.1518 | 0.8357 | 0.8648 | 0.8500 | 0.9642 | |
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| 0.1266 | 3.0 | 1743 | 0.1545 | 0.8377 | 0.8717 | 0.8544 | 0.9680 | |
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| 0.1043 | 4.0 | 2324 | 0.1472 | 0.8473 | 0.8691 | 0.8580 | 0.9656 | |
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| 0.0804 | 5.0 | 2905 | 0.1498 | 0.8534 | 0.8771 | 0.8651 | 0.9671 | |
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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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