|
--- |
|
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 |
|
|
