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---
language:
- tl
license: gpl-3.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
datasets:
- ljvmiranda921/tlunified-ner
metrics:
- precision
- recall
- f1
widget:
- text: MANILA - Binalewala ng Philippine National Police (PNP) nitong Sabado ang
posibleng paglulunsad ng tinatawag na " sympathy attacks " ng Moro National Liberation
Front (MNLF) at Abu Sayyaf matapos arestuhin si Indanan, Sulu Mayor Alvarez Isnaji.
- text: Pinatawan din ng apat na buwang suspensyon si Herma Gonzales - Escudero, chief
revenue officer III ng BIR - Cotabato City, dahil sa kasong dishonesty at limang
kaso ng perjury sa Municipal Trial Court ng Cotabato City . Bunga ito ng kanyang
kabiguan na ideklara sa kanyang SALN noong 2002 - 2004 ang 200 metro kwadradong
lote sa South Cotabato at Toyota Revo noong 2001 SALN at undervaluation ng kanyang
mga ari - arian sa lalawigan noong 2000 - 2004 SALN.
- text: Sa tila pagpapabaya sa mga magsasaka, sinabi ni Escudero na hindi mangyayari
ang pangarap ng Department of Agriculture (DA) na maging self - sufficient ang
Pilipinas sa bigas.
- text: MANILA - Tiniyak ng pinuno ng Government Service Insurance System (GSIS) na
tatapatan nito ang pro - Meralco advertisement ni Judy Ann Santos upang isulong
ang kanyang posisyon na dapat ibaba ang singil sa kuryente.
- text: Idinagdag ni South Cotabato Rep Darlene Antonino - Custodio, na illegal na
ipagpaliban ang halalan sa ARMM kung ang gagamitin lamang basehan ay ang ipapasang
panukala ng Kongreso.
pipeline_tag: token-classification
co2_eq_emissions:
emissions: 22.090476722294312
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.238
hardware_used: 1 x NVIDIA GeForce RTX 3090
base_model: bert-base-multilingual-cased
model-index:
- name: SpanMarker with bert-base-multilingual-cased on TLUnified
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: TLUnified
type: ljvmiranda921/tlunified-ner
split: test
metrics:
- type: f1
value: 0.8886810102899907
name: F1
- type: precision
value: 0.8736971183323115
name: Precision
- type: recall
value: 0.9041878172588832
name: Recall
---
# SpanMarker with bert-base-multilingual-cased on TLUnified
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [TLUnified](https://huggingface.co/datasets/ljvmiranda921/tlunified-ner) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) as the underlying encoder.
## Model Details
### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [TLUnified](https://huggingface.co/datasets/ljvmiranda921/tlunified-ner)
- **Language:** tl
- **License:** gpl-3.0
### Model Sources
- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
### Model Labels
| Label | Examples |
|:------|:----------------------------------------------------------------------------------------------------|
| LOC | "Israel", "Batasan", "United States" |
| ORG | "MMDA", "International Monitoring Team", "Coordinating Committees for the Cessation of Hostilities" |
| PER | "Puno", "Fernando", "Villavicencio" |
## Evaluation
### Metrics
| Label | Precision | Recall | F1 |
|:--------|:----------|:-------|:-------|
| **all** | 0.8737 | 0.9042 | 0.8887 |
| LOC | 0.8830 | 0.9084 | 0.8955 |
| ORG | 0.7579 | 0.8587 | 0.8052 |
| PER | 0.9264 | 0.9220 | 0.9242 |
## Uses
### Direct Use for Inference
```python
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-mbert-base-tlunified")
# Run inference
entities = model.predict("Idinagdag ni South Cotabato Rep Darlene Antonino - Custodio, na illegal na ipagpaliban ang halalan sa ARMM kung ang gagamitin lamang basehan ay ang ipapasang panukala ng Kongreso.")
```
### Downstream Use
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
```python
from span_marker import SpanMarkerModel, Trainer
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-mbert-base-tlunified")
# Specify a Dataset with "tokens" and "ner_tag" columns
dataset = load_dataset("conll2003") # For example CoNLL2003
# Initialize a Trainer using the pretrained model & dataset
trainer = Trainer(
model=model,
train_dataset=dataset["train"],
eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("tomaarsen/span-marker-mbert-base-tlunified-finetuned")
```
</details>
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:----------------------|:----|:--------|:----|
| Sentence length | 1 | 31.7625 | 150 |
| Entities per sentence | 0 | 2.0661 | 38 |
### Training Hyperparameters
- learning_rate: 5e-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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training Results
| Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.6803 | 400 | 0.0074 | 0.8552 | 0.8835 | 0.8691 | 0.9774 |
| 1.3605 | 800 | 0.0072 | 0.8709 | 0.9034 | 0.8869 | 0.9798 |
| 2.0408 | 1200 | 0.0070 | 0.8753 | 0.9053 | 0.8900 | 0.9812 |
| 2.7211 | 1600 | 0.0065 | 0.8876 | 0.9003 | 0.8939 | 0.9807 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.022 kg of CO2
- **Hours Used**: 0.238 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.9.16
- SpanMarker: 1.5.1.dev
- Transformers: 4.30.0
- PyTorch: 2.0.1+cu118
- Datasets: 2.14.0
- Tokenizers: 0.13.3
## Citation
### BibTeX
```
@software{Aarsen_SpanMarker,
author = {Aarsen, Tom},
license = {Apache-2.0},
title = {{SpanMarker for Named Entity Recognition}},
url = {https://github.com/tomaarsen/SpanMarkerNER}
}
```
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