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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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- ner |
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- named-entity-recognition |
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pipeline_tag: token-classification |
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widget: |
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- text: "Amelia Earthart voló su Lockheed Vega 5B monomotor a través del Océano Atlántico hasta París ." |
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example_title: "Spanish" |
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- text: "Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris ." |
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example_title: "English" |
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- text: "Amelia Earthart a fait voler son monomoteur Lockheed Vega 5B à travers l'ocean Atlantique jusqu'à Paris ." |
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example_title: "French" |
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- text: "Amelia Earthart flog mit ihrer einmotorigen Lockheed Vega 5B über den Atlantik nach Paris ." |
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example_title: "German" |
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- text: "Амелия Эртхарт перелетела на своем одномоторном самолете Lockheed Vega 5B через Атлантический океан в Париж ." |
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example_title: "Russian" |
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- text: "Amelia Earthart vloog met haar één-motorige Lockheed Vega 5B over de Atlantische Oceaan naar Parijs ." |
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example_title: "Dutch" |
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- text: "Amelia Earthart przeleciała swoim jednosilnikowym samolotem Lockheed Vega 5B przez Ocean Atlantycki do Paryża ." |
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example_title: "Polish" |
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- text: "Amelia Earthart flaug eins hreyfils Lockheed Vega 5B yfir Atlantshafið til Parísar ." |
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example_title: "Icelandic" |
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- text: "Η Amelia Earthart πέταξε το μονοκινητήριο Lockheed Vega 5B της πέρα από τον Ατλαντικό Ωκεανό στο Παρίσι ." |
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example_title: "Greek" |
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model-index: |
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- name: SpanMarker w. xlm-roberta-base on MultiNERD by Tom Aarsen |
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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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type: Babelscape/multinerd |
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name: MultiNERD |
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split: test |
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revision: 2814b78e7af4b5a1f1886fe7ad49632de4d9dd25 |
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metrics: |
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- type: f1 |
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value: 0.91314 |
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name: F1 |
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- type: precision |
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value: 0.91994 |
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name: Precision |
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- type: recall |
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value: 0.90643 |
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name: Recall |
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datasets: |
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- Babelscape/multinerd |
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language: |
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- multilingual |
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metrics: |
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- f1 |
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- recall |
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- precision |
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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 Named Entity Recognition. In particular, this SpanMarker model uses [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) as the underlying encoder. See [train.py](train.py) for the training script. |
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## Metrics |
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| **Language** | **F1** | **Precision** | **Recall** | |
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|--------------|--------|---------------|------------| |
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| all | 91.31 | 91.99 | 90.64 | |
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| **de** | 93.77 | 93.56 | 93.87 | |
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| **en** | 94.55 | 94.01 | 95.10 | |
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| **es** | 90.82 | 92.58 | 89.13 | |
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| **fr** | 90.90 | 93.23 | 88.68 | |
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| **it** | 93.40 | 90.23 | 92.60 | |
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| **nl** | 92.47 | 93.61 | 91.36 | |
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| **pl** | 91.66 | 92.51 | 90.81 | |
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| **pt** | 91.73 | 93.29 | 90.22 | |
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| **ru** | 92.64 | 92.37 | 92.91 | |
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| **zh** | 82.38 | 83.23 | 81.55 | |
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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-base-multinerd") |
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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. |
