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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: token-classification-bert-base-uncased
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
metrics:
- name: Precision
type: precision
value: 0.9465865464863963
- name: Recall
type: recall
value: 0.9543924604510265
- name: F1
type: f1
value: 0.9504734769127628
- name: Accuracy
type: accuracy
value: 0.9898757836532845
---
<!-- 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. -->
# token-classification-bert-base-uncased
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: 0.0480
- Precision: 0.9466
- Recall: 0.9544
- F1: 0.9505
- Accuracy: 0.9899
## 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: 5e-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: 3.0
### Training results
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.13.3
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