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