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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: bert-base-cased
model-index:
- name: bert-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- type: precision
value: 0.9371173258315406
name: Precision
- type: recall
value: 0.9530461124200605
name: Recall
- type: f1
value: 0.945014601585315
name: F1
- type: accuracy
value: 0.9865338199799847
name: Accuracy
---
<!-- 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-finetuned-ner
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: 0.0599
- Precision: 0.9371
- Recall: 0.9530
- F1: 0.9450
- 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0883 | 1.0 | 1756 | 0.0690 | 0.9181 | 0.9320 | 0.9250 | 0.9821 |
| 0.0334 | 2.0 | 3512 | 0.0623 | 0.9279 | 0.9504 | 0.9390 | 0.9858 |
| 0.0189 | 3.0 | 5268 | 0.0599 | 0.9371 | 0.9530 | 0.9450 | 0.9865 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|