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-ner4
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: 0.9264802631578948
- name: Recall
type: recall
value: 0.947997307303938
- name: F1
type: f1
value: 0.9371152886374979
- name: Accuracy
type: accuracy
value: 0.9859304173779949
bert-finetuned-ner4
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: 0.0599
- Precision: 0.9265
- Recall: 0.9480
- F1: 0.9371
- Accuracy: 0.9859
Usage
from transformers import pipeline
import json
model_checkpoint = "./bert-finetuned-ner4"
token_classifier = pipeline(
"token-classification", model=model_checkpoint, aggregation_strategy="simple"
)
with open('./assets/test2.json', 'r') as json_file:
data = json.load(json_file)
for item in data:
print(item)
print(token_classifier(item))
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0765 | 1.0 | 1756 | 0.0752 | 0.9082 | 0.9344 | 0.9211 | 0.9795 |
| 0.0432 | 2.0 | 3512 | 0.0577 | 0.9257 | 0.9480 | 0.9367 | 0.9859 |
| 0.0243 | 3.0 | 5268 | 0.0599 | 0.9265 | 0.9480 | 0.9371 | 0.9859 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1