bert-finetuned-ner

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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

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1