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--- |
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language: es |
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license: gpl-3.0 |
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tags: |
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- PyTorch |
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- Transformers |
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- Token Classification |
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- xlm-roberta |
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- xlm-roberta-large |
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widget: |
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- text: "Fue antes de llegar a Sigüeiro, en el Camino de Santiago." |
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- text: "Si te metes en el Franco desde la Alameda, vas hacia la Catedral." |
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- text: "Y allí precisamente es Santiago el patrón del pueblo." |
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model-index: |
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- name: es_trf_ner_cds_xlm-large |
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results: [] |
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--- |
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# Introduction |
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) for Named-Entity Recognition, in the domain of tourism related to the Way of Saint Jacques. It recognizes four types of entities: location (LOC), organizations (ORG), person (PER) and miscellaneous (MISC). |
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## Usage |
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You can use this model with Transformers *pipeline* for NER. |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("es_trf_ner_cds_xlm-large") |
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model = AutoModelForTokenClassification.from_pretrained("es_trf_ner_cds_xlm-large") |
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example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. Si te metes en el Franco desde la Alameda, vas hacia la Catedral. Y allí precisamente es Santiago el patrón del pueblo." |
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ner_pipe = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple") |
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for ent in ner_pipe(example): |
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print(ent) |
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``` |
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## Dataset |
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ToDo |
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## Model performance |
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entity|precision|recall|f1 |
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-|-|-|- |
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LOC|0.973|0.983|0.978 |
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MISC|0.760|0.788|0.773 |
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ORG|0.885|0.701|0.783 |
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PER|0.937|0.878|0.906 |
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micro avg|0.953|0.958|0.955 |
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macro avg|0.889|0.838|0.860 |
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weighted avg|0.953|0.958|0.955 |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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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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- num_epochs: 3.0 |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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