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---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9398762157382847
- name: Recall
type: recall
value: 0.9513368385725472
- name: F1
type: f1
value: 0.9455718018568967
- name: Accuracy
type: accuracy
value: 0.9865442356267972
---
<!-- 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-uncased-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0727
- Precision: 0.9399
- Recall: 0.9513
- F1: 0.9456
- Accuracy: 0.9865
## 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: 32
- eval_batch_size: 32
- 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 | 1.0 | 439 | 0.0697 | 0.8960 | 0.9187 | 0.9072 | 0.9799 |
| 0.185 | 2.0 | 878 | 0.0607 | 0.9227 | 0.9384 | 0.9304 | 0.9837 |
| 0.0471 | 3.0 | 1317 | 0.0560 | 0.9341 | 0.9433 | 0.9387 | 0.9858 |
| 0.0263 | 4.0 | 1756 | 0.0610 | 0.9300 | 0.9447 | 0.9373 | 0.9853 |
| 0.0161 | 5.0 | 2195 | 0.0629 | 0.9361 | 0.9516 | 0.9437 | 0.9859 |
| 0.0112 | 6.0 | 2634 | 0.0676 | 0.9372 | 0.9490 | 0.9431 | 0.9860 |
| 0.0076 | 7.0 | 3073 | 0.0697 | 0.9348 | 0.9487 | 0.9417 | 0.9859 |
| 0.0056 | 8.0 | 3512 | 0.0706 | 0.9364 | 0.9497 | 0.9430 | 0.9862 |
| 0.0056 | 9.0 | 3951 | 0.0719 | 0.9381 | 0.9497 | 0.9439 | 0.9864 |
| 0.0038 | 10.0 | 4390 | 0.0727 | 0.9399 | 0.9513 | 0.9456 | 0.9865 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1
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