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--- |
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license: mit |
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base_model: dslim/bert-base-NER |
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tags: |
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- generated_from_trainer |
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datasets: |
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- conll2003job |
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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: my_xlm-roberta-large-finetuned-conlljob01 |
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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: conll2003job |
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type: conll2003job |
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config: conll2003job |
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split: test |
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args: conll2003job |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9057427125152732 |
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- name: Recall |
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type: recall |
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value: 0.9187322946175638 |
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- name: F1 |
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type: f1 |
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value: 0.9121912630746243 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9825347259610208 |
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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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# my_xlm-roberta-large-finetuned-conlljob01 |
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This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the conll2003job dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1690 |
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- Precision: 0.9057 |
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- Recall: 0.9187 |
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- F1: 0.9122 |
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- Accuracy: 0.9825 |
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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: 4 |
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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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| 0.0372 | 1.0 | 896 | 0.1439 | 0.8943 | 0.9184 | 0.9062 | 0.9816 | |
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| 0.0043 | 2.0 | 1792 | 0.1532 | 0.9047 | 0.9209 | 0.9127 | 0.9824 | |
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| 0.0019 | 3.0 | 2688 | 0.1652 | 0.9102 | 0.9186 | 0.9143 | 0.9828 | |
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| 0.0013 | 4.0 | 3584 | 0.1690 | 0.9057 | 0.9187 | 0.9122 | 0.9825 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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