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bert-named-entity-recognition

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metadata
library_name: transformers
tags:
  - bert
  - ner
license: apache-2.0
datasets:
  - eriktks/conll2003
base_model:
  - google-bert/bert-base-uncased
pipeline_tag: token-classification
language:
  - en
results:
  - task: null
    type: token-classification
    name: Token Classification
dataset:
  name: conll2003
  type: conll2003
  config: conll2003
  split: test
metrics:
  - name: Precision
    type: precision
    value: 0.8992
    verified: true
  - name: Recall
    type: recall
    value: 0.9115
    verified: true
  - name: F1
    type: f1
    value: 0.0.9053
    verified: true
  - name: loss
    type: loss
    value: 0.040937
    verified: true

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

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

Abbreviation Description
O Outside of a named entity
B-MISC Beginning of a miscellaneous entity right after another miscellaneous entity
I-MISC Miscellaneous entity
B-PER Beginning of a person's name right after another person's name
I-PER Person's name
B-ORG Beginning of an organization right after another organization
I-ORG Organization
B-LOC Beginning of a location right after another location
I-LOC Location

Training Procedure

  • Full Model Finetune
  • Epochs : 5

Training Loss Curves

image/png

Trainer

  • global_step: 4390
  • training_loss: 0.040937909830132485
  • train_runtime: 206.3611
  • train_samples_per_second: 340.205
  • train_steps_per_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

Class Precision Recall F1-Score Support
LOC 0.91 0.93 0.92 1668
MISC 0.76 0.81 0.78 702
ORG 0.87 0.88 0.88 1661
PER 0.98 0.97 0.97 1617
Micro Avg 0.90 0.91 0.91 5648
Macro Avg 0.88 0.90 0.89 5648
Weighted Avg 0.90 0.91 0.91 5648
  • Evaluation Dataset : eriktks/conll2003