README.md

---
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:
  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](https://huggingface.co/sartajbhuvaji)
- **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](https://huggingface.co/google-bert/bert-base-uncased)
- **Paper:** [BERT-paper](https://huggingface.co/papers/1810.04805)

## Uses

Model can be used to recognize Named Entities in text.

## Usage

```python
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)

```

```json
[
  {
    "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](https://huggingface.co/datasets/eriktks/conll2003)

| 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](https://cdn-uploads.huggingface.co/production/uploads/6354695712edd0ed5dc46b04/vVra4giLk3EPjXo48Sbax.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