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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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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: distilbert-base-uncased-finetuned-ner
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results: []
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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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# distilbert-base-uncased-finetuned-ner
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2988
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- Precision: 0.8066
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- Recall: 0.7644
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- F1: 0.7849
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- Accuracy: 0.9432
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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: 20
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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 | 13 | 1.7619 | 0.0 | 0.0 | 0.0 | 0.6185 |
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| No log | 2.0 | 26 | 1.4482 | 0.0526 | 0.0052 | 0.0095 | 0.6985 |
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| No log | 3.0 | 39 | 1.0747 | 0.0417 | 0.0105 | 0.0167 | 0.7429 |
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| No log | 4.0 | 52 | 0.8462 | 0.2262 | 0.0995 | 0.1382 | 0.7821 |
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| No log | 5.0 | 65 | 0.6852 | 0.3290 | 0.2670 | 0.2948 | 0.8172 |
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| No log | 6.0 | 78 | 0.5970 | 0.4346 | 0.4869 | 0.4593 | 0.8684 |
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| No log | 7.0 | 91 | 0.5108 | 0.5072 | 0.5497 | 0.5276 | 0.8880 |
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| No log | 8.0 | 104 | 0.4515 | 0.5882 | 0.6283 | 0.6076 | 0.9086 |
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| No log | 9.0 | 117 | 0.4105 | 0.6305 | 0.6702 | 0.6497 | 0.9169 |
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| No log | 10.0 | 130 | 0.3755 | 0.7120 | 0.6859 | 0.6987 | 0.9293 |
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| No log | 11.0 | 143 | 0.3661 | 0.7243 | 0.7016 | 0.7128 | 0.9293 |
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| No log | 12.0 | 156 | 0.3460 | 0.7273 | 0.7120 | 0.7196 | 0.9313 |
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| No log | 13.0 | 169 | 0.3287 | 0.7609 | 0.7330 | 0.7467 | 0.9355 |
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| No log | 14.0 | 182 | 0.3177 | 0.7701 | 0.7539 | 0.7619 | 0.9370 |
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| No log | 15.0 | 195 | 0.3133 | 0.7705 | 0.7382 | 0.7540 | 0.9360 |
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| No log | 16.0 | 208 | 0.3028 | 0.7826 | 0.7539 | 0.7680 | 0.9406 |
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| No log | 17.0 | 221 | 0.3062 | 0.7944 | 0.7487 | 0.7709 | 0.9391 |
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| No log | 18.0 | 234 | 0.3015 | 0.8011 | 0.7592 | 0.7796 | 0.9411 |
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| No log | 19.0 | 247 | 0.2997 | 0.7935 | 0.7644 | 0.7787 | 0.9432 |
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| No log | 20.0 | 260 | 0.2988 | 0.8066 | 0.7644 | 0.7849 | 0.9432 |
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### Framework versions
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- Transformers 4.40.2
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- Pytorch 2.3.0+cpu
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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