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--- |
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license: apache-2.0 |
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library_name: span-marker |
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tags: |
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- span-marker |
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- token-classification |
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- pos |
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- part-of-speech |
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pipeline_tag: token-classification |
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--- |
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# SpanMarker for Named Entity Recognition |
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This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for identifying verbs in text. |
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In particular, this SpanMarker model uses [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) as the underlying encoder. |
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See [span_marker_verbs_train.ipynb](span_marker_verbs_train.ipynb) for the training script used to create this model. |
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## Usage |
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To use this model for inference, first install the `span_marker` library: |
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```bash |
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pip install span_marker |
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``` |
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You can then run inference with this model like so: |
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```python |
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from span_marker import SpanMarkerModel |
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# Download from the 🤗 Hub |
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model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-large-verbs") |
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# Run inference |
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entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.") |
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``` |
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See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library. |
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### Performance |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0152 |
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- Overall Precision: 0.9845 |
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- Overall Recall: 0.9849 |
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- Overall F1: 0.9847 |
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- Overall Accuracy: 0.9962 |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.036 | 0.61 | 1000 | 0.0151 | 0.9911 | 0.9733 | 0.9821 | 0.9956 | |
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| 0.0126 | 1.22 | 2000 | 0.0131 | 0.9856 | 0.9864 | 0.9860 | 0.9965 | |
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| 0.0175 | 1.83 | 3000 | 0.0154 | 0.9735 | 0.9894 | 0.9814 | 0.9953 | |
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| 0.0115 | 2.45 | 4000 | 0.0172 | 0.9821 | 0.9871 | 0.9845 | 0.9962 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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- SpanMarker 1.2.3 |
