| --- |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| datasets: |
| - ner |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: Bert-NER |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: ner |
| type: ner |
| config: indian_names |
| split: train |
| args: indian_names |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.9860607282009942 |
| - name: Recall |
| type: recall |
| value: 0.9693364297742606 |
| - name: F1 |
| type: f1 |
| value: 0.9776270584382788 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9882459717748076 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # Bert-NER |
|
|
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0372 |
| - Precision: 0.9861 |
| - Recall: 0.9693 |
| - F1: 0.9776 |
| - Accuracy: 0.9882 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 3 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.0461 | 1.0 | 858 | 0.0450 | 0.9853 | 0.9602 | 0.9725 | 0.9859 | |
| | 0.0408 | 2.0 | 1716 | 0.0400 | 0.9836 | 0.9679 | 0.9757 | 0.9873 | |
| | 0.0391 | 3.0 | 2574 | 0.0372 | 0.9861 | 0.9693 | 0.9776 | 0.9882 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.34.1 |
| - Pytorch 2.1.0+cu118 |
| - Datasets 2.14.6 |
| - Tokenizers 0.14.1 |
|
|
