This model is a fine-tuned version of dslim/bert-base-NER for detecting vessels (ships) and organizations in maritime news articles and documents.

Key Features

Vessel Detection: Identifies ship names in text (mapped to MISC slot)
Organization Detection: Identifies maritime organizations, ship owners, operators, and related entities (uses ORG slot)
Multilingual Support: Trained on English, Spanish, Chinese, French, Portuguese, Russian, and other languages
Preserves Base Model: Maintains original PER, LOC, and other entity detection capabilities

Model Architecture

Base Model: dslim/bert-base-NER
Task: Token Classification (Named Entity Recognition)
Labels: O, B-PER, I-PER, B-ORG, I-ORG, B-LOC, I-LOC, B-MISC, I-MISC
- VESSEL entities are mapped to MISC slot (B-MISC, I-MISC)
- Organization entities use ORG slot (B-ORG, I-ORG)

Model Performance

Evaluation Metrics

Precision: 1.0000
Recall: 1.0000
F1 Score: 1.0000

Note: Metrics reported on validation set. Real-world performance may vary.

Example Predictions

Text	Detected Entities
"The fishing vessel Hai Feng 718 was detained by authorities."	Hai Feng 718 (VESSEL: 1.00)
"Coast guard seized the trawler Thunder near disputed waters."	Thunder (VESSEL: 1.00)
"Pacific Seafood Inc. announced quarterly earnings today."	Pacific Seafood Inc (ORG: 0.95)
"The vessel Thunder owned by Pacific Seafood Inc. was seized."	Thunder (VESSEL: 1.00), Pacific Seafood Inc (ORG: 0.98)

Training Details

Training Data

Total Examples: ~60,000 synthetic multilingual examples
VESSEL Examples: ~20,000
ORG Examples: ~40,000 (ship owners, operators, brands, retailers, importers, fishmeal plants, etc.)
Languages: English, Spanish, Chinese, French, Portuguese, Russian, and others
Source: Synthetically generated from maritime entity databases

Training Procedure

Base Model: dslim/bert-base-NER
Training Epochs: 3
Batch Size: 32
Learning Rate: 2e-5
Max Sequence Length: 128 tokens
Optimizer: AdamW with weight decay 0.01
Mixed Precision: FP16 enabled

Training Configuration

python

TrainingArguments(
    num_train_epochs=3,
    per_device_train_batch_size=32,
    learning_rate=2e-5,
    weight_decay=0.01,
    max_length=128,
    fp16=True
)

How to Use

Direct Use

python

from transformers import pipeline

# Load the model
ner = pipeline("ner", model="your-username/bert-vessel-ner", aggregation_strategy="simple")

# Example 1: Vessel detection
text = "The fishing vessel Hai Feng 718 was detained by authorities."
entities = ner(text)
for entity in entities:
    print(f"{entity['word']} -> {entity['entity_group']} ({entity['score']:.2f})")
# Output: Hai Feng 718 -> MISC (1.00)  # MISC = VESSEL

# Example 2: Mixed entities
text = "The vessel Thunder owned by Pacific Seafood Inc. was seized."
entities = ner(text)
for entity in entities:
    print(f"{entity['word']} -> {entity['entity_group']} ({entity['score']:.2f})")
# Output: 
# Thunder -> MISC (1.00)  # VESSEL
# Pacific Seafood Inc -> ORG (0.98)  # Organization

Advanced Usage

python

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

# Load model and tokenizer
model = AutoModelForTokenClassification.from_pretrained("your-username/bert-vessel-ner")
tokenizer = AutoTokenizer.from_pretrained("your-username/bert-vessel-ner")

# Tokenize and predict
text = "The vessel Thunder was seized."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_ids = torch.argmax(predictions, dim=-1)

# Decode predictions
tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])
for token, pred_id in zip(tokens, predicted_ids[0]):
    if token not in ['[CLS]', '[SEP]', '[PAD]']:
        label = model.config.id2label[pred_id.item()]
        print(f"{token}: {label}")

Post-Processing

Note: The model outputs VESSEL entities as MISC labels. You may want to rename them for clarity:

python

entities = ner(text)
for entity in entities:
    # Rename MISC to VESSEL for clarity
    if entity['entity_group'] == 'MISC':
        entity['entity_group'] = 'VESSEL'
    print(f"{entity['word']} -> {entity['entity_group']}")

Limitations and Bias

Known Limitations

False Positives: May occasionally classify organization names as vessels if they resemble ship names (e.g., "Pacific Seafood Inc."). Use a higher threshold (0.98+) to reduce false positives.
Multilingual Performance: While trained on multiple languages, performance may vary by language. Best results on English, Spanish, and Chinese.
Domain Specificity: Trained primarily on maritime crime and enforcement contexts. Performance may vary in other domains (e.g., commercial shipping, recreational boating).
Synthetic Data: Model was trained on synthetically generated data. Real-world performance may differ from validation metrics.

Recommendations

Threshold Tuning: Adjust the confidence threshold based on your use case:
- High precision (fewer false positives): Use threshold ≥ 0.98
- High recall (catch more vessels): Use threshold ≥ 0.90
Post-Processing: Consider adding rules to filter obvious false positives (e.g., entities containing "Inc.", "Co.", "Ltd.")
Domain Adaptation: For best results in specific domains, consider fine-tuning on domain-specific data

Training Data Sources

The model was trained on synthetically generated data from:

Maritime vessel databases
Ship owner and operator registries
Brand and retailer information
Fishmeal plant and processor databases

All training data was synthetically generated using large language models (Gemini 2.5 Flash-Lite) to create realistic maritime news contexts.

Evaluation

Test Set

Size: ~2,280 examples (10% of total data)
Distribution: Balanced across languages and entity types
Metrics: Precision, Recall, F1 Score

Performance by Entity Type

Entity Type	Precision	Recall	F1
VESSEL (MISC)	1.0000	1.0000	1.0000
ORG	1.0000	1.0000	1.0000

Note: Metrics on validation set. Real-world performance may vary.

Environmental Impact

Hardware: GPU (CUDA)
Training Time: ~3 minutes per epoch (total ~10 minutes)
Carbon Emissions: Minimal (short training duration)

Citation

If you use this model, please cite:

bibtex

@misc{bert-vessel-ner,
  title={BERT-NER Vessel Detection Model},
  author={Your Name},
  year={2025},
  howpublished={\url{https://huggingface.co/your-username/bert-vessel-ner}}
}

Model Card Contact

For questions or issues, please open an issue on the model repository.

License

This model is licensed under Apache 2.0, same as the base model dslim/bert-base-NER.

gamaly/bert-vessel-ner

BERT-NER Vessel Detection Model

Model Description

Key Features

Model Architecture

Model Performance

Evaluation Metrics

Example Predictions

Training Details

Training Data

Training Procedure

Training Configuration

How to Use

Direct Use

Advanced Usage

Post-Processing

Limitations and Bias

Known Limitations

Recommendations

Training Data Sources

Evaluation

Test Set

Performance by Entity Type

Environmental Impact

Citation

Model Card Contact

License

gamaly/
bert-vessel-ner