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--- |
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base_model: microsoft/mdeberta-v3-base |
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datasets: |
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- eriktks/conll2003 |
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language: |
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- en |
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library_name: transformers |
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license: mit |
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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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pipeline_tag: token-classification |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: mdeberta |
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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: eriktks/conll2003 |
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type: eriktks/conll2003 |
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config: conll2003 |
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split: validation |
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args: conll2003 |
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metrics: |
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- type: precision |
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value: 0.9566232899566233 |
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name: Precision |
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- type: recall |
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value: 0.9649949511948839 |
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name: Recall |
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- type: f1 |
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value: 0.9607908847184986 |
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name: F1 |
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- type: accuracy |
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value: 0.9929130485572991 |
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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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# mdeberta-v3-base-conll2003-en |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the [eriktks/conll2003](https://huggingface.co/datasets/eriktks/conll2003) dataset (English split of the CONLL 2003). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0342 |
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- Precision: 0.9566 |
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- Recall: 0.9650 |
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- F1: 0.9608 |
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- Accuracy: 0.9929 |
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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: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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: 5.0 |
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- mixed_precision_training: Native AMP |
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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 | 439 | 0.0509 | 0.9303 | 0.9456 | 0.9379 | 0.9890 | |
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| 0.1482 | 2.0 | 878 | 0.0359 | 0.9501 | 0.9583 | 0.9542 | 0.9918 | |
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| 0.0335 | 3.0 | 1317 | 0.0338 | 0.9530 | 0.9615 | 0.9572 | 0.9924 | |
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| 0.0191 | 4.0 | 1756 | 0.0346 | 0.9538 | 0.9635 | 0.9586 | 0.9926 | |
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| 0.0137 | 5.0 | 2195 | 0.0342 | 0.9566 | 0.9650 | 0.9608 | 0.9929 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.19.1 |
