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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-finetuned-conll2003
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9409771754636234
- name: Recall
type: recall
value: 0.946886775524852
- name: F1
type: f1
value: 0.9439227260531259
- name: Accuracy
type: accuracy
value: 0.9859745687878966
---
<!-- 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-cased-finetuned-conll2003
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0643
- Precision: 0.9410
- Recall: 0.9469
- F1: 0.9439
- Accuracy: 0.9860
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2349 | 0.57 | 500 | 0.0885 | 0.8957 | 0.8980 | 0.8968 | 0.9747 |
| 0.0822 | 1.14 | 1000 | 0.0774 | 0.9184 | 0.9219 | 0.9202 | 0.9802 |
| 0.0476 | 1.71 | 1500 | 0.0683 | 0.9345 | 0.9325 | 0.9335 | 0.9833 |
| 0.0368 | 2.28 | 2000 | 0.0653 | 0.9333 | 0.9430 | 0.9381 | 0.9847 |
| 0.028 | 2.85 | 2500 | 0.0670 | 0.9279 | 0.9342 | 0.9311 | 0.9835 |
| 0.0171 | 3.42 | 3000 | 0.0643 | 0.9410 | 0.9469 | 0.9439 | 0.9860 |
| 0.0149 | 3.99 | 3500 | 0.0667 | 0.9369 | 0.9477 | 0.9422 | 0.9856 |
| 0.0088 | 4.56 | 4000 | 0.0698 | 0.9360 | 0.9473 | 0.9416 | 0.9855 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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