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.
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:
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
# ✅
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