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--- |
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license: mit |
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base_model: FacebookAI/xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- lener_br |
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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-base-finetuned-ner-lenerBr |
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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: lener_br |
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type: lener_br |
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config: lener_br |
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split: validation |
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args: lener_br |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7397260273972602 |
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- name: Recall |
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type: recall |
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value: 0.9211682605324373 |
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- name: F1 |
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type: f1 |
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value: 0.8205364337515828 |
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- name: Accuracy |
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type: accuracy |
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value: 0.970340819101409 |
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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-base-finetuned-ner-lenerBr |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the lener_br dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1294 |
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- Precision: 0.7397 |
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- Recall: 0.9212 |
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- F1: 0.8205 |
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- Accuracy: 0.9703 |
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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: 32 |
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- eval_batch_size: 32 |
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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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| No log | 1.0 | 245 | 0.1569 | 0.7358 | 0.7788 | 0.7567 | 0.9534 | |
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| No log | 2.0 | 490 | 0.1310 | 0.6909 | 0.8927 | 0.7790 | 0.9632 | |
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| 0.1674 | 3.0 | 735 | 0.1148 | 0.7174 | 0.9119 | 0.8030 | 0.9677 | |
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| 0.1674 | 4.0 | 980 | 0.1550 | 0.7209 | 0.8979 | 0.7997 | 0.9658 | |
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| 0.0276 | 5.0 | 1225 | 0.1441 | 0.7183 | 0.9173 | 0.8057 | 0.9682 | |
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| 0.0276 | 6.0 | 1470 | 0.1482 | 0.7326 | 0.8752 | 0.7976 | 0.9665 | |
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| 0.0154 | 7.0 | 1715 | 0.1209 | 0.7418 | 0.9284 | 0.8247 | 0.9710 | |
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| 0.0154 | 8.0 | 1960 | 0.1266 | 0.7375 | 0.9243 | 0.8204 | 0.9708 | |
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| 0.0096 | 9.0 | 2205 | 0.1394 | 0.7356 | 0.9147 | 0.8154 | 0.9690 | |
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| 0.0096 | 10.0 | 2450 | 0.1294 | 0.7397 | 0.9212 | 0.8205 | 0.9703 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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