SpanMarker with PlanTL-GOB-ES/roberta-base-bne on conll2002
This is a SpanMarker model trained on the conll2002 dataset that can be used for Named Entity Recognition. This SpanMarker model uses PlanTL-GOB-ES/roberta-base-bne as the underlying encoder.
Model Details
Model Description
- Model Type: SpanMarker
- Encoder: PlanTL-GOB-ES/roberta-base-bne
- Maximum Sequence Length: 256 tokens
- Maximum Entity Length: 8 words
- Training Dataset: conll2002
- Languages: es
- License: cc-by-4.0
Model Sources
- Repository: SpanMarker on GitHub
- Thesis: SpanMarker For Named Entity Recognition
Model Labels
| Label | Examples |
|---|---|
| LOC | "Australia", "Victoria", "Melbourne" |
| MISC | "Ley", "Ciudad", "CrimeNet" |
| ORG | "Commonwealth", "EFE", "Tribunal Supremo" |
| PER | "Abogado General del Estado", "Daryl Williams", "Abogado General" |
Uses
Direct Use for Inference
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("alvarobartt/span-marker-roberta-base-bne-conll-2002-es")
# Run inference
entities = model.predict("George Washington estuvo en Washington.")
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Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Sentence length | 1 | 31.8052 | 1238 |
| Entities per sentence | 0 | 2.2586 | 160 |
Training Hyperparameters
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- 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: 2
Training Results
| Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|---|---|---|---|---|---|---|
| 0.1188 | 100 | 0.0704 | 0.0 | 0.0 | 0.0 | 0.8608 |
| 0.2375 | 200 | 0.0279 | 0.8765 | 0.4034 | 0.5525 | 0.9025 |
| 0.3563 | 300 | 0.0158 | 0.8381 | 0.7211 | 0.7752 | 0.9524 |
| 0.4751 | 400 | 0.0134 | 0.8525 | 0.7463 | 0.7959 | 0.9576 |
| 0.5938 | 500 | 0.0130 | 0.8844 | 0.7549 | 0.8145 | 0.9560 |
| 0.7126 | 600 | 0.0119 | 0.8480 | 0.8006 | 0.8236 | 0.9650 |
| 0.8314 | 700 | 0.0098 | 0.8794 | 0.8408 | 0.8597 | 0.9695 |
| 0.9501 | 800 | 0.0091 | 0.8842 | 0.8360 | 0.8594 | 0.9722 |
| 1.0689 | 900 | 0.0093 | 0.8976 | 0.8387 | 0.8672 | 0.9698 |
| 1.1876 | 1000 | 0.0094 | 0.8880 | 0.8517 | 0.8694 | 0.9739 |
| 1.3064 | 1100 | 0.0086 | 0.8920 | 0.8530 | 0.8721 | 0.9737 |
| 1.4252 | 1200 | 0.0092 | 0.8896 | 0.8452 | 0.8668 | 0.9728 |
| 1.5439 | 1300 | 0.0094 | 0.8765 | 0.8313 | 0.8533 | 0.9720 |
| 1.6627 | 1400 | 0.0089 | 0.8805 | 0.8445 | 0.8621 | 0.9720 |
| 1.7815 | 1500 | 0.0088 | 0.8834 | 0.8581 | 0.8706 | 0.9747 |
| 1.9002 | 1600 | 0.0088 | 0.8883 | 0.8547 | 0.8712 | 0.9747 |
Framework Versions
- Python: 3.10.12
- SpanMarker: 1.3.1.dev
- Transformers: 4.33.2
- PyTorch: 2.0.1+cu118
- Datasets: 2.14.5
- 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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