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--- |
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license: apache-2.0 |
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base_model: distilbert-base-cased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ner |
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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: Bert-NER |
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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: ner |
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type: ner |
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config: indian_names |
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split: test |
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args: indian_names |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9779481031086752 |
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- name: Recall |
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type: recall |
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value: 0.950199700449326 |
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- name: F1 |
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type: f1 |
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value: 0.96387423507069 |
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- name: Accuracy |
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type: accuracy |
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value: 0.977337411889879 |
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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-NER |
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0518 |
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- Precision: 0.9779 |
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- Recall: 0.9502 |
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- F1: 0.9639 |
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- Accuracy: 0.9773 |
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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: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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| No log | 1.0 | 438 | 0.0725 | 0.9691 | 0.9325 | 0.9505 | 0.9693 | |
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| 0.0435 | 2.0 | 876 | 0.0635 | 0.9687 | 0.9392 | 0.9537 | 0.9711 | |
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| 0.039 | 3.0 | 1314 | 0.0569 | 0.9790 | 0.9416 | 0.9599 | 0.9751 | |
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| 0.0392 | 4.0 | 1752 | 0.0542 | 0.9744 | 0.9490 | 0.9615 | 0.9758 | |
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| 0.0378 | 5.0 | 2190 | 0.0518 | 0.9779 | 0.9502 | 0.9639 | 0.9773 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
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