🏷️ Named Entity Recognition — Fine-Tuned DistilBERT

This is the fine-tuned cased DistilBERT model for English Named Entity Recognition (NER) trained on the CoNLL-2003 corpus. It detects and classifies tokens into four entity categories:

• PER (Person)
• ORG (Organization)
• LOC (Location / Place)
• MISC (Miscellaneous, e.g. nationalities, events)

It achieved the winning score in an architectural comparison against LSTM, BiLSTM, and BiLSTM + CRF models.

📊 Performance (Strict CoNLL-2003 Evaluation)

Evaluated with seqeval in strict IOB2 mode (requiring exact boundary and category match):

• Validation Macro F1: 93.92%
• Test Strict F1: 90.18%

🚀 Quick Usage

Using Hugging Face Pipeline

from transformers import pipeline

ner = pipeline("ner", model="Solayman-Sameh/named-entity-recognition", aggregation_strategy="simple")

text = "Sara works at Google in Cairo."
entities = ner(text)

for ent in entities:
    print(f"{ent['word']} -> {ent['entity_group']} ({ent['score']:.2%})")

Using PyTorch Directly

import torch
from transformers import AutoTokenizer, AutoModelForTokenClassification

model_id = "Solayman-Sameh/named-entity-recognition"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id).eval()

text = "Mohamed studies at MIT in USA."
inputs = tokenizer(text, return_tensors="pt")

with torch.inference_mode():
    outputs = model(**inputs)
    predictions = outputs.logits.argmax(dim=-1)[0].tolist()

tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
for token, pred in zip(tokens, predictions):
    if token not in [tokenizer.cls_token, tokenizer.sep_token, tokenizer.pad_token]:
        print(f"{token}: {model.config.id2label[pred]}")

💻 Full Project & Web App

The complete source code, exploratory data analysis, architectural comparison (LSTM, BiLSTM, BiLSTM+CRF vs. DistilBERT), test reports, and an interactive Gradio web demo are available at:

👉 GitHub: Solayman-Sameh/named-entity-recognition