---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
- bert-finetuned
- Named Entity Recognition
- NER
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
pipeline_tag: token-classification
language:
- en
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# bert-finetuned-ner

## Model Description
This model is a Named Entity Recognition (NER) model built using PyTorch and fine-tuned 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, you can load it using the Hugging Face Transformers library. Below is an example of how to perform inference using the model:

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("Ashaduzzaman/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("Ashaduzzaman/bert-finetuned-ner")

# Create a pipeline for NER
ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)

# Example inference
text = "Hugging Face Inc. is based in New York City."
entities = ner_pipeline(text)

print(entities)
```

### Troubleshooting
If the model isn't performing as expected, consider checking the following:
- Ensure that the input text is in English, as the model was trained on English data.
- Adjust the model's confidence threshold for entity detection to filter out less confident predictions.

## 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 the pre-trained BERT model (`bert-base-cased`) with a token classification head for NER. The training process involved:
- **Optimizer:** AdamW optimizer with betas=(0.9, 0.999) and epsilon=1e-08
- **Learning Rate:** A linear learning rate scheduler was employed starting from 2e-05
- **Batch Size:** 8 for both training and evaluation
- **Epochs:** The model was trained for 3 epochs
- **Evaluation:** Model performance was evaluated on a validation set with metrics like F1-score, precision, recall, and accuracy.

### Training Hyperparameters
- **Learning Rate:** 2e-05
- **Batch Size (train/eval):** 8/8
- **Seed:** 42
- **Optimizer:** Adam with betas=(0.9, 0.999) and epsilon=1e-08
- **LR Scheduler Type:** Linear
- **Number of Epochs:** 3

## Evaluation Results
This model was evaluated 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   |

## Framework Versions
- **Transformers:** 4.42.4
- **PyTorch:** 2.3.1+cu121
- **Datasets:** 2.21.0
- **Tokenizers:** 0.19.1
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