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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wnut_17 |
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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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base_model: distilbert-base-multilingual-cased |
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model-index: |
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- name: distilbert-base-multilingual-cased-WNUT-ner |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: wnut_17 |
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type: wnut_17 |
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config: wnut_17 |
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split: test |
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args: wnut_17 |
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metrics: |
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- type: precision |
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value: 0.5496503496503496 |
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name: Precision |
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- type: recall |
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value: 0.36422613531047265 |
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name: Recall |
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- type: f1 |
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value: 0.4381270903010034 |
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name: F1 |
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- type: accuracy |
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value: 0.9468667179618706 |
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name: Accuracy |
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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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# distilbert-base-multilingual-cased-WNUT-ner |
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This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the wnut_17 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3516 |
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- Precision: 0.5497 |
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- Recall: 0.3642 |
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- F1: 0.4381 |
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- Accuracy: 0.9469 |
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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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| No log | 1.0 | 213 | 0.2727 | 0.6626 | 0.2530 | 0.3662 | 0.9402 | |
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| No log | 2.0 | 426 | 0.2636 | 0.5895 | 0.2715 | 0.3718 | 0.9429 | |
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| 0.1729 | 3.0 | 639 | 0.2933 | 0.5931 | 0.3040 | 0.4020 | 0.9447 | |
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| 0.1729 | 4.0 | 852 | 0.2861 | 0.5437 | 0.3457 | 0.4227 | 0.9453 | |
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| 0.0503 | 5.0 | 1065 | 0.3270 | 0.5627 | 0.3494 | 0.4311 | 0.9455 | |
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| 0.0503 | 6.0 | 1278 | 0.3277 | 0.5451 | 0.3531 | 0.4286 | 0.9463 | |
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| 0.0503 | 7.0 | 1491 | 0.3471 | 0.5828 | 0.3457 | 0.4340 | 0.9467 | |
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| 0.0231 | 8.0 | 1704 | 0.3594 | 0.5801 | 0.3457 | 0.4332 | 0.9464 | |
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| 0.0231 | 9.0 | 1917 | 0.3550 | 0.5567 | 0.3503 | 0.4300 | 0.9467 | |
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| 0.0121 | 10.0 | 2130 | 0.3516 | 0.5497 | 0.3642 | 0.4381 | 0.9469 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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