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---
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](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for identifying verbs in text.
In particular, this SpanMarker model uses [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) as the underlying encoder.
See [span_marker_verbs_train.ipynb](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:
```bash
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](https://github.com/tomaarsen/SpanMarkerNER) 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 |