alvarobartt/span-marker-roberta-base-bne-conll-2002-es

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.")

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}
}