---
language:
  - en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
  - named-entity-recognition
  - ner
  - qwen
  - decoder-only
  - lora
  - conll2003
  - information-extraction
base_model: Qwen/Qwen2.5-0.5B-Instruct
datasets:
  - lhoestq/conll2003
---

# English Named Entity Recognition (NER) – Qwen2.5-0.5B-Instruct + LoRA

## Model Description

**Qwen-NER-Decoder** is a fine-tuned decoder-only Named Entity Recognition (NER) model based on [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).

The model identifies and extracts named entities from English text and generates structured JSON output.

It is fine-tuned on the CoNLL-2003 English Named Entity Recognition dataset and recognizes four entity categories:

- **PER** – Person
- **ORG** – Organization
- **LOC** – Location
- **MISC** – Miscellaneous

The model uses **Low-Rank Adaptation (LoRA)** for parameter-efficient fine-tuning. The Hugging Face repository contains the LoRA adapter, so it must be loaded together with its original base model.

## Model Details

| Property | Details |
|---|---|
| Model Name | Qwen-NER-Decoder |
| Base Model | Qwen/Qwen2.5-0.5B-Instruct |
| Architecture | Decoder-only Transformer |
| Fine-tuning Method | LoRA |
| LoRA Rank | 8 |
| LoRA Alpha | 16 |
| LoRA Dropout | 0.05 |
| Training Epochs | 1 |
| Language | English |
| Task | Named Entity Recognition |
| Output Format | JSON |
| Training Framework | PyTorch, Transformers, PEFT |

## Dataset

The model was fine-tuned using the [CoNLL-2003 English NER dataset](https://huggingface.co/datasets/lhoestq/conll2003).

Dataset repository: `lhoestq/conll2003`

The dataset contains English sentences annotated with BIO-style named entity labels.

The four entity categories are:

| Label | Description |
|---|---|
| PER | Person |
| ORG | Organization |
| LOC | Location |
| MISC | Miscellaneous |

## Training Configuration

The model was fine-tuned using LoRA with the following configuration:

| Configuration | Value |
|---|---|
| Base Model | Qwen2.5-0.5B-Instruct |
| LoRA Rank | 8 |
| LoRA Alpha | 16 |
| LoRA Dropout | 0.05 |
| Target Modules | q_proj, k_proj, v_proj, o_proj |
| Training Epochs | 1 |
| Training Samples | Up to 8,000 |
| Validation Samples | 1,000 |
| Training Precision | FP16 |
| Optimizer | AdamW |
| Training Environment | Kaggle GPU |

LoRA adapts selected attention projection layers without updating all the parameters of the original base model.

## Evaluation Results

The model was evaluated on a quick test subset of 100 English sentences using exact entity-level matching.

| Metric | Score |
|---|---:|
| Precision | 90.40% |
| Recall | 81.63% |
| F1-score | 85.79% |
| True Positives | 160 |
| False Positives | 17 |
| False Negatives | 36 |

**Evaluation note:** These results represent the 100-sample evaluation subset, not the complete test dataset. They should not be interpreted as a full-dataset benchmark.

Additional manually selected English sentences were used to inspect entity extraction behavior.

## Example Predictions

### Example 1

**Input:**

```text
Elon Musk founded SpaceX.
```

**Output:**

```json
{
  "entities": [
    {
      "text": "Elon Musk",
      "type": "PER"
    },
    {
      "text": "SpaceX",
      "type": "ORG"
    }
  ]
}
```

### Example 2

**Input:**

```text
Barack Obama visited Microsoft headquarters in Seattle.
```

**Output:**

```json
{
  "entities": [
    {
      "text": "Barack Obama",
      "type": "PER"
    },
    {
      "text": "Microsoft",
      "type": "ORG"
    },
    {
      "text": "Seattle",
      "type": "LOC"
    }
  ]
}
```

*Examples illustrate the intended output structure.*

## How to Use

### 1. Install Dependencies

```bash
pip install torch transformers peft accelerate safetensors
```

### 2. Load the Base Model and LoRA Adapter

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftConfig, PeftModel

ADAPTER_MODEL = "ullahhafeez321/Qwen-NER-Decoder"

# Read the original base model from the adapter configuration
adapter_config = PeftConfig.from_pretrained(ADAPTER_MODEL)
BASE_MODEL = adapter_config.base_model_name_or_path

# Load tokenizer from the base model
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)

if tokenizer.pad_token_id is None:
    tokenizer.pad_token = tokenizer.eos_token

# Load the original Qwen base model
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    torch_dtype=(
        torch.float16 if torch.cuda.is_available()
        else torch.float32
    ),
    device_map="auto"
)

# Attach the fine-tuned LoRA adapter
model = PeftModel.from_pretrained(
    base_model,
    ADAPTER_MODEL
)

model.eval()

print("Qwen NER model loaded successfully!")
```

### 3. Run Inference

The prompt used during inference should follow the same instruction and output format used during fine-tuning.

The following is an illustrative JSON extraction example:

```python
sentence = "Albert Einstein worked at Princeton University."

prompt = f"""
Extract named entities from the following sentence.

Entity types:
PER = Person
ORG = Organization
LOC = Location
MISC = Miscellaneous

Return only a JSON array:

[
  {{"text": "entity name", "type": "PER"}}
]

If no entities are found, return [].

Sentence: {sentence}

Entities:
"""

inputs = tokenizer(
    prompt,
    return_tensors="pt",
    truncation=True,
    max_length=1024
)

input_device = model.get_input_embeddings().weight.device
inputs = {
    key: value.to(input_device)
    for key, value in inputs.items()
}

with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        max_new_tokens=150,
        do_sample=False,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id
    )

generated_tokens = outputs[0][inputs["input_ids"].shape[1]:]

result = tokenizer.decode(
    generated_tokens,
    skip_special_tokens=True
)

print(result)
```

**Note:** This example demonstrates the inference process. For the best results, use the exact prompt template and target format used to train the uploaded adapter.

## Intended Use

This model is intended for:

- English named entity recognition.
- Natural Language Processing applications.
- Information extraction systems.
- Text analysis and document processing.
- Educational and research projects.
- Integration with sentiment classification systems.
- Applications that require structured entity extraction.

## Limitations

- The model may occasionally miss named entities.
- Some entities may receive incorrect classifications.
- Entity boundaries and spelling may occasionally be incorrect.
- Performance can vary across different domains and writing styles.
- The reported evaluation score is based on a limited 100-sample test subset.
- The model is intended for English text and may not perform reliably on other languages.
- The model is not intended to replace human verification in critical applications.

## Model Architecture

This project uses a decoder-only Transformer architecture based on Qwen2.5-0.5B-Instruct.

The model generates structured entity extraction results from an English input sentence.

LoRA fine-tuning adapts selected attention projection layers while preserving the original base model weights.

The uploaded Hugging Face repository contains the fine-tuned adapter rather than a complete standalone model checkpoint.

## Future Improvements

- Increase training data and training epochs.
- Improve recall and entity coverage.
- Evaluate performance on the complete test dataset.
- Improve entity boundary detection.
- Expand evaluation across different English text domains.
- Improve inference consistency and structured JSON generation.
- Integrate with an encoder-only BERT sentiment classification model.

## Acknowledgements

- [Qwen Team](https://huggingface.co/Qwen) for the Qwen2.5 base model.
- [CoNLL-2003](https://huggingface.co/datasets/lhoestq/conll2003) dataset contributors.
- [Hugging Face](https://huggingface.co/) for the Transformers, Datasets, and PEFT ecosystem.

## License

The adapter repository declares the Apache-2.0 license. Please also review the original base model and dataset licenses and their respective usage conditions before redistribution or commercial use.

---

**Project:** English Named Entity Recognition using a Decoder-only Transformer with LoRA Fine-tuning.

**Model Repository:** `ullahhafeez321/Qwen-NER-Decoder`

**Base Model:** `Qwen/Qwen2.5-0.5B-Instruct`

**Frameworks:** PyTorch, Transformers, PEFT, Hugging Face Datasets.