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---
license: apache-2.0
tags:
- generated_from_trainer
base_model: bert-base-uncased
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER_Pittsburgh_TAA
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- type: precision
value: 0.9343718926085516
name: Precision
- type: recall
value: 0.9460789797516501
name: Recall
- type: f1
value: 0.940188993885492
name: F1
- type: accuracy
value: 0.9858293484995313
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. -->
# NER_Pittsburgh_TAA
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: 0.0599
- Precision: 0.9344
- Recall: 0.9461
- F1: 0.9402
- Accuracy: 0.9858
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 439 | 0.0604 | 0.9175 | 0.9290 | 0.9232 | 0.9829 |
| 0.0953 | 2.0 | 878 | 0.0545 | 0.9312 | 0.9412 | 0.9361 | 0.9850 |
| 0.0409 | 3.0 | 1317 | 0.0571 | 0.9357 | 0.9412 | 0.9384 | 0.9855 |
| 0.0234 | 4.0 | 1756 | 0.0593 | 0.9343 | 0.9482 | 0.9412 | 0.9858 |
| 0.0159 | 5.0 | 2195 | 0.0599 | 0.9344 | 0.9461 | 0.9402 | 0.9858 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|