SpanMarker with roberta-large on Jerado/enron_intangibles_ner
This is a SpanMarker model trained on the Jerado/enron_intangibles_ner dataset that can be used for Named Entity Recognition. This SpanMarker model uses roberta-large as the underlying encoder.
Model Details
Model Description
- Model Type: SpanMarker
- Encoder: roberta-large
- Maximum Sequence Length: 256 tokens
- Maximum Entity Length: 6 words
- Training Dataset: Jerado/enron_intangibles_ner
- Language: en
- License: apache-2.0
Model Sources
- Repository: SpanMarker on GitHub
- Thesis: SpanMarker For Named Entity Recognition
Model Labels
Label | Examples |
---|---|
Intangible | "deal", "sample EES deal", "Enpower system" |
Evaluation
Metrics
Label | Precision | Recall | F1 |
---|---|---|---|
all | 0.4286 | 0.45 | 0.4390 |
Intangible | 0.4286 | 0.45 | 0.4390 |
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("It seems that there is a single significant policy concern for the ASIC policy committee.")
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 | 1 | 19.8706 | 216 |
Entities per sentence | 0 | 0.1865 | 6 |
Training Hyperparameters
- learning_rate: 1e-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: 11
- mixed_precision_training: Native AMP
Training Results
Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
---|---|---|---|---|---|---|
3.3557 | 500 | 0.0075 | 0.4444 | 0.1667 | 0.2424 | 0.9753 |
6.7114 | 1000 | 0.0084 | 0.5714 | 0.3333 | 0.4211 | 0.9793 |
10.0671 | 1500 | 0.0098 | 0.6111 | 0.4583 | 0.5238 | 0.9815 |
Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.40.0
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
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 Jerado/span-marker-roberta-large-enron
Base model
FacebookAI/roberta-largeEvaluation results
- F1 on Unknowntest set self-reported0.439
- Precision on Unknowntest set self-reported0.429
- Recall on Unknowntest set self-reported0.450