metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-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: 0.9341149273447821
- name: Recall
type: recall
value: 0.9520363513968361
- name: F1
type: f1
value: 0.9429904984164028
- name: Accuracy
type: accuracy
value: 0.9866515570730559
bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0741
- Precision: 0.9341
- Recall: 0.9520
- F1: 0.9430
- Accuracy: 0.9867
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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0775 | 1.0 | 1756 | 0.0694 | 0.8912 | 0.9273 | 0.9089 | 0.9817 |
| 0.0377 | 2.0 | 3512 | 0.0707 | 0.9245 | 0.9445 | 0.9344 | 0.9850 |
| 0.0243 | 3.0 | 5268 | 0.0671 | 0.9281 | 0.9465 | 0.9372 | 0.9855 |
| 0.0145 | 4.0 | 7024 | 0.0734 | 0.9353 | 0.9507 | 0.9429 | 0.9859 |
| 0.006 | 5.0 | 8780 | 0.0741 | 0.9341 | 0.9520 | 0.9430 | 0.9867 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
How to use and it's democase
from transformers import pipeline
model_checkpoint = "amannagrawall002/bert-finetuned-ner" token_classifier = pipeline( "token-classification", model=model_checkpoint, aggregation_strategy="simple" )
print(token_classifier("My name is Sylvain and I work at Hugging Face in Brooklyn."))