sartajbhuvaji/bert-named-entity-recognition

Model Card for Bert Named Entity Recognition

Model Description

This is a chat fine-tuned version of google-bert/bert-base-uncased, designed to perform Named Entity Recognition on a text sentence imput.

• —Developed by: Sartaj
• —Finetuned from model: google-bert/bert-base-uncased
• —Language(s): English
• —License: apache-2.0
• —Framework: Hugging Face Transformers

Model Sources

• —Repository: google-bert/bert-base-uncased
• —Paper: BERT-paper

Uses

Model can be used to recognize Named Entities in text.

Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("sartajbhuvaji/bert-named-entity-recognition")
model = AutoModelForTokenClassification.from_pretrained("sartajbhuvaji/bert-named-entity-recognition")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Wolfgang and I live in Berlin"

ner_results = nlp(example)
print(ner_results)

[
  {
    "end": 19,
    "entity": "B-PER",
    "index": 4,
    "score": 0.99633455,
    "start": 11,
    "word": "wolfgang"
  },
  {
    "end": 40,
    "entity": "B-LOC",
    "index": 9,
    "score": 0.9987465,
    "start": 34,
    "word": "berlin"
  }
]

Training Details

• —Dataset : eriktks/conll2003

Training Procedure

• —Full Model Finetune
• —Epochs : 5

Training Loss Curves

Trainer

• —global_step: 4390
• —training_loss: 0.040937909830132485
• —train_runtime: 206.3611
• —trainsamplesper_second: 340.205
• —trainstepsper_second: 21.273
• —total_flos: 1702317283240608.0
• —train_loss: 0.040937909830132485
• —epoch: 5.0

Evaluation

• —Precision: 0.8992
• —Recall: 0.9115
• —F1 Score: 0.9053

Classification Report

• —Evaluation Dataset : eriktks/conll2003