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tomaarsen/
span-marker-xlm-roberta-large-verbs

Name: span-marker-xlm-roberta-large-verbs
Rating: 2.5 (17 reviews)

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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.

Note that this model is an experiment about the feasibility of SpanMarker as a POS tagger. I would generally recommend using spaCy or NLTK instead, as these are more computationally efficient approaches.

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:

python

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

Limitations

Warning: This model works best when punctuation is separated from the prior words, so

python

# ✅
model.predict("He plays J. Robert Oppenheimer , an American theoretical physicist .")
# ❌
model.predict("He plays J. Robert Oppenheimer, an American theoretical physicist.")

# You can also supply a list of words directly: ✅
model.predict(["He", "plays", "J.", "Robert", "Oppenheimer", ",", "an", "American", "theoretical", "physicist", "."])

The same may be beneficial for some languages, such as splitting "l'ocean Atlantique" into "l' ocean Atlantique".

Framework versions

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