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--- |
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license: mit |
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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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model-index: |
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- name: microsoft-deberta-v3-large_ner_wnut_17 |
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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: wnut_17 |
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type: wnut_17 |
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args: wnut_17 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7670623145400594 |
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- name: Recall |
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type: recall |
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value: 0.618421052631579 |
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- name: F1 |
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type: f1 |
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value: 0.6847682119205298 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9666942096230853 |
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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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# microsoft-deberta-v3-large_ner_wnut_17 |
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the wnut_17 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2199 |
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- Precision: 0.7671 |
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- Recall: 0.6184 |
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- F1: 0.6848 |
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- Accuracy: 0.9667 |
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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: cosine |
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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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| No log | 1.0 | 213 | 0.1751 | 0.6884 | 0.5682 | 0.6225 | 0.9601 | |
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| No log | 2.0 | 426 | 0.1702 | 0.7351 | 0.6208 | 0.6732 | 0.9655 | |
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| 0.1003 | 3.0 | 639 | 0.1954 | 0.7360 | 0.6136 | 0.6693 | 0.9656 | |
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| 0.1003 | 4.0 | 852 | 0.2113 | 0.7595 | 0.6232 | 0.6846 | 0.9669 | |
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| 0.015 | 5.0 | 1065 | 0.2199 | 0.7671 | 0.6184 | 0.6848 | 0.9667 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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