README.md

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: 0.9346783529022656
          - name: Recall
            type: recall
            value: 0.9511948838774823
          - name: F1
            type: f1
            value: 0.9428642922679124
          - name: Accuracy
            type: accuracy
            value: 0.9863572143403779

bert-finetuned-ner

Model Description

This model is a Named Entity Recognition (NER) model built using PyTorch and trained on the CoNLL-2003 dataset. The model is designed to identify and classify named entities in text into categories such as persons (PER), organizations (ORG), locations (LOC), and miscellaneous entities (MISC).

Intended Uses & Limitations

Intended Uses:

• Text Analysis: This model can be used for extracting named entities from unstructured text data, which is useful in various NLP tasks such as information retrieval, content categorization, and automated summarization.
• NER Task: Specifically designed for NER tasks in English.

Limitations:

• Language Dependency: The model is trained on English data and may not perform well on texts in other languages.
• Domain Specificity: Performance may degrade on text from domains significantly different from the training data.
• Error Propagation: Incorrect predictions may propagate to downstream tasks, affecting overall performance.

How to Use

To use this model, load it through the Hugging Face Transformers library. Below is a basic example:

from transformers import pipeline

# Load the NER pipeline
ner_pipeline = pipeline("ner", model="Ashaduzzaman/bert-finetuned-ner")

# Example text
text = "Hugging Face Inc. is based in New York City."

# Perform NER
entities = ner_pipeline(text)

print(entities)

Limitations and Bias

• Bias in Data: The model is trained on the CoNLL-2003 dataset, which may contain biases related to the sources of the text. The model might underperform on entities not well represented in the training data.
• Overfitting: The model may overfit to the specific entities present in the CoNLL-2003 dataset, affecting its generalization to new entities or text styles.

Training Data

The model was trained on the CoNLL-2003 dataset, a widely used benchmark dataset for NER tasks. The dataset contains annotated text from news articles, with labels for persons, organizations, locations, and miscellaneous entities.

Training Procedure

The model was fine-tuned using a pre-trained transformer model (e.g., BERT) with a token classification head for NER. The training involved:

• Optimizer: AdamW optimizer
• Learning Rate: Learning rate scheduler was employed
• Batch Size: Defined in the notebook based on available resources
• Epochs: The model was trained for a specified number of epochs until convergence
• Evaluation: Model performance was evaluated on a validation set, with metrics like F1-score, precision, and recall.

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

Evaluation Results

This model is a fine-tuned version of bert-base-cased on the CoNLL-2003 test set, with performance measured using standard NER metrics:

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.076	1.0	1756	0.0657	0.9076	0.9337	0.9204	0.9819
0.0359	2.0	3512	0.0693	0.9265	0.9418	0.9341	0.9847
0.0222	3.0	5268	0.0599	0.9347	0.9512	0.9429	0.9864

These results indicate the model's ability to correctly identify and classify named entities in text.

Framework versions

• Transformers 4.42.4
• Pytorch 2.3.1+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1