metadata

license: apache-2.0
library_name: span-marker
tags:
  - span-marker
  - token-classification
  - pos
  - part-of-speech
pipeline_tag: token-classification

SpanMarker for Named Entity Recognition

This is a SpanMarker model that can be used for identifying verbs in text. In particular, this SpanMarker model uses xlm-roberta-large as the underlying encoder. See span_marker_verbs_train.ipynb for the training script used to create this model.

Usage

To use this model for inference, first install the span_marker library:

pip install span_marker

You can then run inference with this model like so:

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-large-verbs")
# Run inference
entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")

See the SpanMarker repository for documentation and additional information on this library.

Performance

It achieves the following results on the evaluation set:

Loss: 0.0152
Overall Precision: 0.9845
Overall Recall: 0.9849
Overall F1: 0.9847
Overall Accuracy: 0.9962

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.036	0.61	1000	0.0151	0.9911	0.9733	0.9821	0.9956
0.0126	1.22	2000	0.0131	0.9856	0.9864	0.9860	0.9965
0.0175	1.83	3000	0.0154	0.9735	0.9894	0.9814	0.9953
0.0115	2.45	4000	0.0172	0.9821	0.9871	0.9845	0.9962

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3
SpanMarker 1.2.3