|
--- |
|
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 |
|
|