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--- |
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language: |
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- es |
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license: cc-by-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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datasets: |
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- conll2002 |
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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: George Washington estuvo en Washington. |
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pipeline_tag: token-classification |
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base_model: PlanTL-GOB-ES/roberta-base-bne |
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model-index: |
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- name: SpanMarker with PlanTL-GOB-ES/roberta-base-bne on conll2002 |
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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: conll2002 |
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type: conll2002 |
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split: eval |
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metrics: |
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- type: f1 |
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value: 0.871172868582195 |
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name: F1 |
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- type: precision |
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value: 0.888328530259366 |
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name: Precision |
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- type: recall |
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value: 0.8546672828096118 |
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name: Recall |
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--- |
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# SpanMarker with PlanTL-GOB-ES/roberta-base-bne on conll2002 |
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This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [conll2002](https://huggingface.co/datasets/conll2002) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) 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:** [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) |
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- **Maximum Sequence Length:** 256 tokens |
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- **Maximum Entity Length:** 8 words |
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- **Training Dataset:** [conll2002](https://huggingface.co/datasets/conll2002) |
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- **Languages:** es |
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- **License:** cc-by-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 | "Australia", "Victoria", "Melbourne" | |
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| MISC | "Ley", "Ciudad", "CrimeNet" | |
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| ORG | "Commonwealth", "EFE", "Tribunal Supremo" | |
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| PER | "Abogado General del Estado", "Daryl Williams", "Abogado General" | |
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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("alvarobartt/span-marker-roberta-base-bne-conll-2002-es") |
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# Run inference |
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entities = model.predict("George Washington estuvo en Washington.") |
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``` |
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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 | 31.8052 | 1238 | |
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| Entities per sentence | 0 | 2.2586 | 160 | |
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### Training Hyperparameters |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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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: 2 |
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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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| 0.1188 | 100 | 0.0704 | 0.0 | 0.0 | 0.0 | 0.8608 | |
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| 0.2375 | 200 | 0.0279 | 0.8765 | 0.4034 | 0.5525 | 0.9025 | |
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| 0.3563 | 300 | 0.0158 | 0.8381 | 0.7211 | 0.7752 | 0.9524 | |
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| 0.4751 | 400 | 0.0134 | 0.8525 | 0.7463 | 0.7959 | 0.9576 | |
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| 0.5938 | 500 | 0.0130 | 0.8844 | 0.7549 | 0.8145 | 0.9560 | |
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| 0.7126 | 600 | 0.0119 | 0.8480 | 0.8006 | 0.8236 | 0.9650 | |
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| 0.8314 | 700 | 0.0098 | 0.8794 | 0.8408 | 0.8597 | 0.9695 | |
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| 0.9501 | 800 | 0.0091 | 0.8842 | 0.8360 | 0.8594 | 0.9722 | |
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| 1.0689 | 900 | 0.0093 | 0.8976 | 0.8387 | 0.8672 | 0.9698 | |
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| 1.1876 | 1000 | 0.0094 | 0.8880 | 0.8517 | 0.8694 | 0.9739 | |
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| 1.3064 | 1100 | 0.0086 | 0.8920 | 0.8530 | 0.8721 | 0.9737 | |
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| 1.4252 | 1200 | 0.0092 | 0.8896 | 0.8452 | 0.8668 | 0.9728 | |
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| 1.5439 | 1300 | 0.0094 | 0.8765 | 0.8313 | 0.8533 | 0.9720 | |
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| 1.6627 | 1400 | 0.0089 | 0.8805 | 0.8445 | 0.8621 | 0.9720 | |
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| 1.7815 | 1500 | 0.0088 | 0.8834 | 0.8581 | 0.8706 | 0.9747 | |
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| 1.9002 | 1600 | 0.0088 | 0.8883 | 0.8547 | 0.8712 | 0.9747 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SpanMarker: 1.3.1.dev |
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- Transformers: 4.33.2 |
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- PyTorch: 2.0.1+cu118 |
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- Datasets: 2.14.5 |
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- Tokenizers: 0.13.3 |
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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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