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