| --- |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| datasets: |
| - szeged_ner |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: test-train-model |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: szeged_ner |
| type: szeged_ner |
| config: business |
| split: validation |
| args: business |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.9325044404973357 |
| - name: Recall |
| type: recall |
| value: 0.9308510638297872 |
| - name: F1 |
| type: f1 |
| value: 0.9316770186335402 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9925327242378986 |
| --- |
| |
| <!-- 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. --> |
|
|
| # test-train-model |
|
|
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the szeged_ner dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0319 |
| - Precision: 0.9325 |
| - Recall: 0.9309 |
| - F1: 0.9317 |
| - Accuracy: 0.9925 |
| |
| ## 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: 5 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.2029 | 1.0 | 511 | 0.0493 | 0.8734 | 0.8564 | 0.8648 | 0.9873 | |
| | 0.0756 | 2.0 | 1022 | 0.0381 | 0.8930 | 0.9025 | 0.8977 | 0.9897 | |
| | 0.0489 | 3.0 | 1533 | 0.0327 | 0.925 | 0.9184 | 0.9217 | 0.9921 | |
| | 0.0339 | 4.0 | 2044 | 0.0323 | 0.9385 | 0.9202 | 0.9293 | 0.9926 | |
| | 0.0258 | 5.0 | 2555 | 0.0319 | 0.9325 | 0.9309 | 0.9317 | 0.9925 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.32.0 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.4 |
| - Tokenizers 0.13.3 |
| |
