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---
license: mit
base_model: dslim/bert-base-NER
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-NER-finetuned-ner
results: []
---
<!-- 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-base-NER-finetuned-ner
This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2391
- Precision: 0.9245
- Recall: 0.9186
- F1: 0.9216
- Accuracy: 0.9168
## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.37 | 100 | 0.5115 | 0.8204 | 0.8719 | 0.8454 | 0.8200 |
| No log | 0.75 | 200 | 0.3808 | 0.8684 | 0.8766 | 0.8725 | 0.8600 |
| No log | 1.12 | 300 | 0.3315 | 0.8900 | 0.8865 | 0.8882 | 0.8799 |
| No log | 1.49 | 400 | 0.3069 | 0.9036 | 0.8917 | 0.8976 | 0.8921 |
| 0.5306 | 1.87 | 500 | 0.2908 | 0.9066 | 0.8978 | 0.9022 | 0.8980 |
| 0.5306 | 2.24 | 600 | 0.2783 | 0.9114 | 0.9061 | 0.9087 | 0.9048 |
| 0.5306 | 2.61 | 700 | 0.2729 | 0.9151 | 0.9123 | 0.9137 | 0.9096 |
| 0.5306 | 2.99 | 800 | 0.2628 | 0.9157 | 0.9086 | 0.9121 | 0.9077 |
| 0.5306 | 3.36 | 900 | 0.2600 | 0.9207 | 0.9123 | 0.9165 | 0.9107 |
| 0.3037 | 3.73 | 1000 | 0.2539 | 0.9188 | 0.9134 | 0.9161 | 0.9110 |
| 0.3037 | 4.1 | 1100 | 0.2488 | 0.9229 | 0.9178 | 0.9203 | 0.9148 |
| 0.3037 | 4.48 | 1200 | 0.2449 | 0.9225 | 0.9170 | 0.9198 | 0.9146 |
| 0.3037 | 4.85 | 1300 | 0.2466 | 0.9230 | 0.9177 | 0.9203 | 0.9155 |
| 0.3037 | 5.22 | 1400 | 0.2415 | 0.9229 | 0.9188 | 0.9208 | 0.9161 |
| 0.2668 | 5.6 | 1500 | 0.2413 | 0.9237 | 0.9189 | 0.9213 | 0.9164 |
| 0.2668 | 5.97 | 1600 | 0.2391 | 0.9245 | 0.9186 | 0.9216 | 0.9168 |
| 0.2668 | 6.34 | 1700 | 0.2399 | 0.9245 | 0.9178 | 0.9211 | 0.9162 |
| 0.2668 | 6.72 | 1800 | 0.2369 | 0.9239 | 0.9181 | 0.9210 | 0.9164 |
| 0.2668 | 7.09 | 1900 | 0.2390 | 0.9239 | 0.9183 | 0.9211 | 0.9164 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
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