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--- |
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language: en |
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license: cc-by-sa-4.0 |
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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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- generated_from_span_marker_trainer |
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base_model: FacebookAI/xlm-roberta-base |
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datasets: |
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- norne |
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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: Av Boethius hand förelåg De institutione arithmetica (" Om aritmetikens grunder |
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") i två böcker. |
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- text: Hans hovedmotstander var lederen for opposisjonspartiet Movement for Democratic |
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Change, Morgan Tsvangirai. |
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- text: Roddarn blir proffs efter OS. |
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- text: Han blev dog diskvalificeret for at have trådt på banelinjen, og bronzemedaljen |
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gik i stedet til landsmanden Walter Dix. |
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- text: Stillingen var på dette tidspunkt 1-1, men Almunias redning banede vejen for |
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et sejrsmål af danske Nicklas Bendtner. |
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pipeline_tag: token-classification |
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model-index: |
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- name: SpanMarker with FacebookAI/xlm-roberta-base on norne |
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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: norne |
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type: norne |
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split: test |
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metrics: |
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- type: f1 |
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value: 0.9181825779313034 |
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name: F1 |
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- type: precision |
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value: 0.9217689611454993 |
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name: Precision |
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- type: recall |
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value: 0.9146239940801036 |
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name: Recall |
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--- |
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# SpanMarker with xlm-roberta-base |
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Trained on various nordic lang. datasets: see https://huggingface.co/datasets/tollefj/nordic-ner |
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This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [norne](https://huggingface.co/datasets/norne) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) as the underlying encoder. |
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## Model Details |
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### Model Description |
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- **Model Type:** SpanMarker |
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- **Encoder:** [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) |
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- **Maximum Sequence Length:** 256 tokens |
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- **Maximum Entity Length:** 8 words |
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- **Training Dataset:** [norne](https://huggingface.co/datasets/norne) |
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- **Language:** en |
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- **License:** cc-by-sa-4.0 |
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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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| LOC | "Gran", "Leicestershire", "Den tyske antarktisekspedisjonen" | |
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| MISC | "socialdemokratiske", "nationalist", "Living Legend" | |
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| ORG | "Stabæk", "Samlaget", "Marillion" | |
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| PER | "Fish", "Dmitrij Medvedev", "Guru Ardjan Dev" | |
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## Evaluation |
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### Metrics |
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| Label | Precision | Recall | F1 | |
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|:--------|:----------|:-------|:-------| |
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| **all** | 0.9218 | 0.9146 | 0.9182 | |
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| LOC | 0.9284 | 0.9433 | 0.9358 | |
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| MISC | 0.6515 | 0.6047 | 0.6272 | |
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| ORG | 0.8951 | 0.8547 | 0.8745 | |
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| PER | 0.9513 | 0.9526 | 0.9520 | |
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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("Roddarn blir proffs efter OS.") |
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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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## 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 | 1 | 12.8175 | 331 | |
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| Entities per sentence | 0 | 1.0055 | 54 | |
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### Training Hyperparameters |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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: 3 |
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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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| 0.5711 | 3000 | 0.0146 | 0.8650 | 0.8725 | 0.8687 | 0.9722 | |
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| 1.1422 | 6000 | 0.0123 | 0.8994 | 0.8920 | 0.8957 | 0.9778 | |
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| 1.7133 | 9000 | 0.0101 | 0.9184 | 0.8984 | 0.9083 | 0.9805 | |
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| 2.2844 | 12000 | 0.0101 | 0.9198 | 0.9110 | 0.9154 | 0.9818 | |
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| 2.8555 | 15000 | 0.0089 | 0.9245 | 0.9150 | 0.9197 | 0.9830 | |
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### Framework Versions |
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- Python: 3.12.2 |
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- SpanMarker: 1.5.0 |
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- Transformers: 4.38.2 |
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- PyTorch: 2.2.1+cu121 |
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- Datasets: 2.18.0 |
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- Tokenizers: 0.15.2 |
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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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