SpanMarker with bert-base-cased on conll2002
This is a SpanMarker model trained on the conll2002 dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-cased as the underlying encoder.
Model Details
Model Description
- Model Type: SpanMarker
- Encoder: bert-base-cased
- Maximum Sequence Length: 256 tokens
- Maximum Entity Length: 8 words
- Training Dataset: conll2002
- Language: es
- License: cc-by-sa-4.0
Model Sources
- Repository: SpanMarker on GitHub
- Thesis: SpanMarker For Named Entity Recognition
Model Labels
Label | Examples |
---|---|
LOC | "Victoria", "Australia", "Melbourne" |
MISC | "Ley", "Ciudad", "CrimeNet" |
ORG | "Tribunal Supremo", "EFE", "Commonwealth" |
PER | "Abogado General del Estado", "Daryl Williams", "Abogado General" |
Evaluation
Metrics
Label | Precision | Recall | F1 |
---|---|---|---|
all | 0.8331 | 0.8074 | 0.8201 |
LOC | 0.8471 | 0.7759 | 0.8099 |
MISC | 0.7092 | 0.4264 | 0.5326 |
ORG | 0.7854 | 0.8558 | 0.8191 |
PER | 0.9471 | 0.9329 | 0.9400 |
Uses
Direct Use for Inference
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Run inference
entities = model.predict("(SV2147) PP: PROBLEMAS INTERNOS PSOE INTERFIEREN EN POLITICA DE LA JUNTA Córdoba (EFE).")
Downstream Use
You can finetune this model on your own dataset.
Click to expand
from span_marker import SpanMarkerModel, Trainer
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# 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("span_marker_model_id-finetuned")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Sentence length | 0 | 31.8014 | 1238 |
Entities per sentence | 0 | 2.2583 | 160 |
Training Hyperparameters
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP
Training Results
Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
---|---|---|---|---|---|---|
0.1164 | 200 | 0.0260 | 0.6907 | 0.5358 | 0.6035 | 0.9264 |
0.2328 | 400 | 0.0199 | 0.7567 | 0.6384 | 0.6925 | 0.9414 |
0.3491 | 600 | 0.0176 | 0.7773 | 0.7273 | 0.7515 | 0.9563 |
0.4655 | 800 | 0.0157 | 0.8066 | 0.7598 | 0.7825 | 0.9601 |
0.5819 | 1000 | 0.0158 | 0.8031 | 0.7413 | 0.7710 | 0.9605 |
0.6983 | 1200 | 0.0156 | 0.7975 | 0.7598 | 0.7782 | 0.9609 |
0.8147 | 1400 | 0.0139 | 0.8210 | 0.7615 | 0.7901 | 0.9625 |
0.9310 | 1600 | 0.0129 | 0.8426 | 0.7848 | 0.8127 | 0.9651 |
Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.18.0
- Tokenizers: 0.15.2
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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Model tree for sepulm01/span-marker-bert-base-conll2002-es
Base model
google-bert/bert-base-casedDataset used to train sepulm01/span-marker-bert-base-conll2002-es
Evaluation results
- F1 on Unknowntest set self-reported0.820
- Precision on Unknowntest set self-reported0.833
- Recall on Unknowntest set self-reported0.807