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ullahhafeez321
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Model Details
Model Provider
ullahhafeez321
Model Tree
Qwen/Qwen2.5-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Qwen-NER-Decoder is a fine-tuned decoder-only Named Entity Recognition (NER) model based on 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:
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.
| 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 |
The model was fine-tuned using the CoNLL-2003 English NER dataset.
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 |
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.
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.
Input:
text
Elon Musk founded SpaceX.Output:
json
{ "entities": [ { "text": "Elon Musk", "type": "PER" }, { "text": "SpaceX", "type": "ORG" } ]}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.
bash
pip install torch transformers peft accelerate safetensorspython
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftConfig, PeftModel ADAPTER_MODEL = "ullahhafeez321/Qwen-NER-Decoder" # Read the original base model from the adapter configurationadapter_config = PeftConfig.from_pretrained(ADAPTER_MODEL)BASE_MODEL = adapter_config.base_model_name_or_path # Load tokenizer from the base modeltokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) if tokenizer.pad_token_id is None: tokenizer.pad_token = tokenizer.eos_token # Load the original Qwen base modelbase_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 adaptermodel = PeftModel.from_pretrained( base_model, ADAPTER_MODEL) model.eval() print("Qwen NER model loaded successfully!")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 = PersonORG = OrganizationLOC = LocationMISC = 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.deviceinputs = { 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.
This model is intended for:
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.
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.
{ "entities": [ { "text": "Elon Musk", "type": "PER" }, { "text": "SpaceX", "type": "ORG" } ]}{ "entities": [ { "text": "Barack Obama", "type": "PER" }, { "text": "Microsoft", "type": "ORG" }, { "text": "Seattle", "type": "LOC" } ]}pip install torch transformers peft accelerate safetensorsimport torchfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftConfig, PeftModel ADAPTER_MODEL = "ullahhafeez321/Qwen-NER-Decoder" # Read the original base model from the adapter configurationadapter_config = PeftConfig.from_pretrained(ADAPTER_MODEL)BASE_MODEL = adapter_config.base_model_name_or_path # Load tokenizer from the base modeltokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) if tokenizer.pad_token_id is None: tokenizer.pad_token = tokenizer.eos_token # Load the original Qwen base modelbase_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 adaptermodel = PeftModel.from_pretrained( base_model, ADAPTER_MODEL) model.eval() print("Qwen NER model loaded successfully!")sentence = "Albert Einstein worked at Princeton University." prompt = f"""Extract named entities from the following sentence. Entity types:PER = PersonORG = OrganizationLOC = LocationMISC = 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.deviceinputs = { 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)