| | --- |
| | library_name: span-marker |
| | tags: |
| | - span-marker |
| | - token-classification |
| | - ner |
| | - named-entity-recognition |
| | - generated_from_span_marker_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | widget: |
| | - text: The Bengal tiger is the most common subspecies of tiger, constituting approximately |
| | 80% of the entire tiger population, and is found in Bangladesh, Bhutan, Myanmar, |
| | Nepal, and India. |
| | - text: In other countries, it is a non-commissioned rank (e.g. Spain, Italy, France, |
| | the Netherlands and the Indonesian Police ranks). |
| | - text: The filling consists of fish, pork and bacon, and is seasoned with salt (unless |
| | the pork is already salted). |
| | - text: This stood until August 20, 1993 when it was beaten by one 1 / 100th of a |
| | second by Colin Jackson of Great Britain in Stuttgart, Germany, a subsequent record |
| | that stood for 13 years. |
| | - text: Ann Patchett ’s novel " Bel Canto ", was another creative influence that helped |
| | her manage a plentiful cast of characters. |
| | pipeline_tag: token-classification |
| | model-index: |
| | - name: SpanMarker |
| | results: |
| | - task: |
| | type: token-classification |
| | name: Named Entity Recognition |
| | dataset: |
| | name: Unknown |
| | type: unknown |
| | split: eval |
| | metrics: |
| | - type: f1 |
| | value: 0.9130661114003124 |
| | name: F1 |
| | - type: precision |
| | value: 0.9148758606300855 |
| | name: Precision |
| | - type: recall |
| | value: 0.9112635078969243 |
| | name: Recall |
| | --- |
| | |
| | # SpanMarker |
| |
|
| | This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. |
| |
|
| | ## Model Details |
| |
|
| | ### Model Description |
| | - **Model Type:** SpanMarker |
| | <!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) --> |
| | - **Maximum Sequence Length:** 256 tokens |
| | - **Maximum Entity Length:** 6 words |
| | <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
| | <!-- - **Language:** Unknown --> |
| | <!-- - **License:** Unknown --> |
| |
|
| | ### Model Sources |
| |
|
| | - **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER) |
| | - **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf) |
| |
|
| | ### Model Labels |
| | | Label | Examples | |
| | |:------|:-------------------------------------------------------------------------| |
| | | ANIM | "vertebrate", "moth", "G. firmus" | |
| | | BIO | "Aspergillus", "Cladophora", "Zythiostroma" | |
| | | CEL | "pulsar", "celestial bodies", "neutron star" | |
| | | DIS | "social anxiety disorder", "insulin resistance", "Asperger syndrome" | |
| | | EVE | "Spanish Civil War", "National Junior Angus Show", "French Revolution" | |
| | | FOOD | "Neera", "Bellini ( cocktail )", "soju" | |
| | | INST | "Apple II", "Encyclopaedia of Chess Openings", "Android" | |
| | | LOC | "Kīlauea", "Hungary", "Vienna" | |
| | | MEDIA | "CSI : Crime Scene Investigation", "Big Comic Spirits", "American Idol" | |
| | | MYTH | "Priam", "Oźwiena", "Odysseus" | |
| | | ORG | "San Francisco Giants", "Arm Holdings", "RTÉ One" | |
| | | PER | "Amelia Bence", "Tito Lusiardo", "James Cameron" | |
| | | PLANT | "vernal squill", "Sarracenia purpurea", "Drosera rotundifolia" | |
| | | TIME | "prehistory", "Age of Enlightenment", "annual paid holiday" | |
| | | VEHI | "Short 360", "Ferrari 355 Challenge", "Solution F / Chretien Helicopter" | |
| |
|
| | ## Uses |
| |
|
| | ### Direct Use for Inference |
| |
|
| | ```python |
| | from span_marker import SpanMarkerModel |
| | |
| | # Download from the 🤗 Hub |
| | model = SpanMarkerModel.from_pretrained("span_marker_model_id") |
| | # Run inference |
| | entities = model.predict("Ann Patchett ’s novel \" Bel Canto \", was another creative influence that helped her manage a plentiful cast of characters.") |
| | ``` |
| |
|
| | ### Downstream Use |
| | You can finetune this model on your own dataset. |
| |
|
| | <details><summary>Click to expand</summary> |
| |
|
| | ```python |
| | from span_marker import SpanMarkerModel, Trainer |
| | |
| | # Download from the 🤗 Hub |
| | model = SpanMarkerModel.from_pretrained("span_marker_model_id") |
| | |
| | # Specify a Dataset with "tokens" and "ner_tag" columns |
| | dataset = load_dataset("conll2003") # For example CoNLL2003 |
| | |
| | # Initialize a Trainer using the pretrained model & dataset |
| | trainer = Trainer( |
| | model=model, |
| | train_dataset=dataset["train"], |
| | eval_dataset=dataset["validation"], |
| | ) |
| | trainer.train() |
| | trainer.save_model("span_marker_model_id-finetuned") |
| | ``` |
| | </details> |
| |
|
| | <!-- |
| | ### Out-of-Scope Use |
| |
|
| | *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| | --> |
| |
|
| | <!-- |
| | ## Bias, Risks and Limitations |
| |
|
| | *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| | --> |
| |
|
| | <!-- |
| | ### Recommendations |
| |
|
| | *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| | --> |
| |
|
| | ## Training Details |
| |
|
| | ### Training Set Metrics |
| | | Training set | Min | Median | Max | |
| | |:----------------------|:----|:--------|:----| |
| | | Sentence length | 2 | 21.6493 | 237 | |
| | | Entities per sentence | 0 | 1.5369 | 36 | |
| |
|
| | ### Training Hyperparameters |
| | - learning_rate: 1e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_ratio: 0.1 |
| | - num_epochs: 1 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training Results |
| | | Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy | |
| | |:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:| |
| | | 0.0576 | 1000 | 0.0142 | 0.8714 | 0.7729 | 0.8192 | 0.9698 | |
| | | 0.1153 | 2000 | 0.0107 | 0.8316 | 0.8815 | 0.8558 | 0.9744 | |
| | | 0.1729 | 3000 | 0.0092 | 0.8717 | 0.8797 | 0.8757 | 0.9780 | |
| | | 0.2306 | 4000 | 0.0082 | 0.8811 | 0.8886 | 0.8848 | 0.9798 | |
| | | 0.2882 | 5000 | 0.0084 | 0.8523 | 0.9163 | 0.8831 | 0.9790 | |
| | | 0.3459 | 6000 | 0.0079 | 0.8700 | 0.9113 | 0.8902 | 0.9802 | |
| | | 0.4035 | 7000 | 0.0070 | 0.9107 | 0.8859 | 0.8981 | 0.9822 | |
| | | 0.4611 | 8000 | 0.0069 | 0.9259 | 0.8797 | 0.9022 | 0.9827 | |
| | | 0.5188 | 9000 | 0.0067 | 0.9061 | 0.8965 | 0.9013 | 0.9829 | |
| | | 0.5764 | 10000 | 0.0066 | 0.9034 | 0.8996 | 0.9015 | 0.9829 | |
| | | 0.6341 | 11000 | 0.0064 | 0.9160 | 0.8996 | 0.9077 | 0.9839 | |
| | | 0.6917 | 12000 | 0.0066 | 0.8952 | 0.9121 | 0.9036 | 0.9832 | |
| | | 0.7494 | 13000 | 0.0062 | 0.9165 | 0.9009 | 0.9086 | 0.9841 | |
| | | 0.8070 | 14000 | 0.0062 | 0.9010 | 0.9121 | 0.9065 | 0.9835 | |
| | | 0.8647 | 15000 | 0.0062 | 0.9084 | 0.9127 | 0.9105 | 0.9842 | |
| | | 0.9223 | 16000 | 0.0060 | 0.9151 | 0.9098 | 0.9125 | 0.9846 | |
| | | 0.9799 | 17000 | 0.0060 | 0.9149 | 0.9113 | 0.9131 | 0.9848 | |
| | |
| | ### Framework Versions |
| | - Python: 3.8.16 |
| | - SpanMarker: 1.5.0 |
| | - Transformers: 4.29.0.dev0 |
| | - PyTorch: 1.10.1 |
| | - Datasets: 2.15.0 |
| | - Tokenizers: 0.13.2 |
| | |
| | ## Citation |
| | |
| | ### BibTeX |
| | ``` |
| | @software{Aarsen_SpanMarker, |
| | author = {Aarsen, Tom}, |
| | license = {Apache-2.0}, |
| | title = {{SpanMarker for Named Entity Recognition}}, |
| | url = {https://github.com/tomaarsen/SpanMarkerNER} |
| | } |
| | ``` |
| | |
| | <!-- |
| | ## Glossary |
| |
|
| | *Clearly define terms in order to be accessible across audiences.* |
| | --> |
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| | ## Model Card Authors |
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| | *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| | --> |
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| | ## Model Card Contact |
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| | *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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