metadata

language:
  - en
license: apache-2.0
library_name: span-marker
tags:
  - span-marker
  - token-classification
  - ner
  - named-entity-recognition
  - generated_from_span_marker_trainer
base_model: roberta-large
datasets:
  - Jerado/enron_intangibles_ner
metrics:
  - precision
  - recall
  - f1
widget:
  - text: >-
      Negotiated rates in these types of deals (basis for new builds) have been
      allowed to stand for the life of the contracts, in the case of Kern River
      and Mojave.
  - text: >-
      It seems that there is a single significant policy concern for the ASIC
      policy committee.
  - text: >-
      The appropriate price is in Enpower, but the revenue has never appeared
      (Deal #590753).
  - text: >-
      FYI, to me, a prepayment for a service contract would generally be
      amortized over the life of the contract.
  - text: >-
      From: d..steffes @ enron.com To: john.shelk @ enron.com, l..nicolay @
      enron.com, richard.shapiro @ enron.com, sarah.novosel @ enron.com Subject:
      Southern Co.'s Testimony The first order of business is getting the cost /
      benefit analysis done.
pipeline_tag: token-classification
model-index:
  - name: SpanMarker with roberta-large on Jerado/enron_intangibles_ner
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          name: Unknown
          type: Jerado/enron_intangibles_ner
          split: test
        metrics:
          - type: f1
            value: 0.4390243902439024
            name: F1
          - type: precision
            value: 0.42857142857142855
            name: Precision
          - type: recall
            value: 0.45
            name: Recall

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}
}