Bert Large Uncased Finetuned Ner

🚀 bert-large-uncased-finetuned-ner

This model is a fine - tuned version of bert-large-uncased on the conll2003 dataset, which can be used for token classification tasks.

🚀 Quick Start

This model is a fine - tuned version of bert-large-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0778
• Precision: 0.9505
• Recall: 0.9575
• F1: 0.9540
• Accuracy: 0.9886

✨ Features

• Trained on the conll2003 dataset, suitable for token classification tasks.
• Achieved high precision, recall, F1 - score, and accuracy on the evaluation set.

💻 Usage Examples

Basic Usage

You can use this model with Transformers pipeline for NER.

from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/bert-large-uncased-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("Jorgeutd/bert-large-uncased-finetuned-ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Scott and I live in Ohio"
ner_results = nlp(example)
print(ner_results)

📚 Documentation

Model description

More information needed

Limitations and bias

This model is limited by its training dataset of entity - annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post - processing of results may be necessary to handle those cases.

How to use

The basic usage example is provided above. You can adjust the input text according to your needs to perform NER tasks.

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e - 05
• train_batch_size: 16
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.16.2
• Pytorch 1.8.1+cu111
• Datasets 1.18.3
• Tokenizers 0.11.0

📄 License

This model is licensed under the apache - 2.0 license.