|
--- |
|
license: apache-2.0 |
|
base_model: bert-base-cased |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- conll2002 |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: bert-finetuned-ner |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: conll2002 |
|
type: conll2002 |
|
config: es |
|
split: validation |
|
args: es |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.7640546993705232 |
|
- name: Recall |
|
type: recall |
|
value: 0.8088235294117647 |
|
- name: F1 |
|
type: f1 |
|
value: 0.7858019868288871 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9676902769959431 |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# bert-finetuned-ner |
|
|
|
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2002 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1912 |
|
- Precision: 0.7641 |
|
- Recall: 0.8088 |
|
- F1: 0.7858 |
|
- Accuracy: 0.9677 |
|
|
|
## Model description |
|
|
|
El modelo base bert-base-cased es una versi贸n pre-entrenada del popular modelo de lenguaje BERT de Google. Inicialmente fue entrenado en grandes cantidades de texto para aprender representaciones densas de palabras y secuencias. |
|
Posteriormente, este modelo toma la arquitectura y pesos pre-entrenados de bert-base-cased y los ajusta a煤n m谩s en la tarea espec铆fica de Reconocimiento de Entidades Nombradas (NER por sus siglas en ingl茅s) utilizando el conjunto de datos conll2002. |
|
|
|
## How to Use |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("JoshuaAAX/bert-finetuned-ner") |
|
model = AutoModelForTokenClassification.from_pretrained("JoshuaAAX/bert-finetuned-ner") |
|
|
|
|
|
text = "La Federaci贸n nacional de cafeteros de Colombia es una entidad del estado. El primer presidente el Dr Augusto Guerra cont贸 con el aval de la Asociaci贸n Colombiana de Aviaci贸n." |
|
|
|
|
|
ner_pipeline= pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="max") |
|
ner_pipeline(text) |
|
``` |
|
|
|
## Training data |
|
|
|
| Abbreviation | Description | |
|
|:-------------:|:-------------:| |
|
| O | Outside of NE | |
|
| PER | Person鈥檚 name | |
|
| ORG | Organization | |
|
| LOC | Location | |
|
| MISC | Miscellaneous | |
|
|
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 2e-05 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.1713 | 1.0 | 521 | 0.1404 | 0.6859 | 0.7387 | 0.7114 | 0.9599 | |
|
| 0.0761 | 2.0 | 1042 | 0.1404 | 0.6822 | 0.7693 | 0.7231 | 0.9623 | |
|
| 0.05 | 3.0 | 1563 | 0.1304 | 0.7488 | 0.7937 | 0.7706 | 0.9672 | |
|
| 0.0355 | 4.0 | 2084 | 0.1454 | 0.7585 | 0.7960 | 0.7768 | 0.9664 | |
|
| 0.0253 | 5.0 | 2605 | 0.1501 | 0.7549 | 0.8095 | 0.7812 | 0.9677 | |
|
| 0.0184 | 6.0 | 3126 | 0.1726 | 0.7581 | 0.7992 | 0.7781 | 0.9662 | |
|
| 0.0138 | 7.0 | 3647 | 0.1743 | 0.7524 | 0.8042 | 0.7774 | 0.9676 | |
|
| 0.0112 | 8.0 | 4168 | 0.1853 | 0.7576 | 0.8022 | 0.7792 | 0.9674 | |
|
| 0.0082 | 9.0 | 4689 | 0.1914 | 0.7595 | 0.8061 | 0.7821 | 0.9667 | |
|
| 0.0073 | 10.0 | 5210 | 0.1912 | 0.7641 | 0.8088 | 0.7858 | 0.9677 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.0 |
|
- Pytorch 2.3.0+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
|
|