README.md

---
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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