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---
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
## 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
|