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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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- x_glue |
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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: dslim/bert-base-NER |
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model-index: |
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- name: bert-base-NER-finetuned-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: x_glue |
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type: x_glue |
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args: ner |
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metrics: |
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- type: precision |
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value: 0.2273838630806846 |
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name: Precision |
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- type: recall |
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value: 0.11185727172496743 |
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name: Recall |
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- type: f1 |
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value: 0.14994961370507223 |
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name: F1 |
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- type: accuracy |
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value: 0.8485324947589099 |
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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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# bert-base-NER-finetuned-ner |
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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 x_glue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4380 |
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- Precision: 0.2274 |
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- Recall: 0.1119 |
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- F1: 0.1499 |
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- Accuracy: 0.8485 |
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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: 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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| 0.0822 | 1.0 | 878 | 1.1648 | 0.2068 | 0.1101 | 0.1437 | 0.8471 | |
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| 0.0102 | 2.0 | 1756 | 1.2697 | 0.2073 | 0.1110 | 0.1445 | 0.8447 | |
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| 0.0049 | 3.0 | 2634 | 1.3945 | 0.2006 | 0.1073 | 0.1399 | 0.8368 | |
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| 0.0025 | 4.0 | 3512 | 1.3994 | 0.2243 | 0.1126 | 0.1499 | 0.8501 | |
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| 0.0011 | 5.0 | 4390 | 1.4380 | 0.2274 | 0.1119 | 0.1499 | 0.8485 | |
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### Framework versions |
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- Transformers 4.10.2 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.12.1 |
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- Tokenizers 0.10.3 |
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