readme: Mapping of labels:
  
  |Class|Label|
  |-----|-----|
  |0|Ayuntamiento|
  |1|Ciencia|
  |2|Cultura|
  |3|Deporte|
  |4|Economía|
  |5|Educación|
  |6|Igualdad_Integración|
  |7|MedioAmbiente|
  |8|Movilidad|
  |9|Política|
  |10|Salud|
  |11|Sucesos|
  |12|Tecnología|
  |13|Turismo|
  
  
  Evaluation w/ Gold Standard (10%, 5000 messages)
  
  ***** Running Prediction *****
  Num examples = 5000
  Batch size = 16
  
  |              | precision | recall | f1-score | support |
  |--------------|-----------|--------|----------|---------|
  |      0       |    0.88   |  0.88  |   0.88   |   312   |
  |      1       |    0.77   |  0.80  |   0.78   |   296   |
  |      2       |    0.90   |  0.88  |   0.89   |   335   |
  |      3       |    0.95   |  0.97  |   0.96   |   361   |
  |      4       |    0.92   |  0.88  |   0.90   |   992   |
  |      5       |    0.88   |  0.91  |   0.90   |   326   |
  |      6       |    0.78   |  0.74  |   0.76   |    54   |
  |      7       |    0.84   |  0.82  |   0.83   |   326   |
  |      8       |    0.79   |  0.92  |   0.85   |   296   |
  |      9       |    0.89   |  0.82  |   0.85   |   382   |
  |     10       |    0.85   |  0.85  |   0.85   |   326   |
  |     11       |    0.89   |  0.89  |   0.89   |   301   |
  |     12       |    0.90   |  0.84  |   0.87   |   378   |
  |     13       |    0.83   |  0.91  |   0.87   |   315   |
  |--------------|-----------|--------|----------|---------|
  |Accuracy|-----|-----|0.856|5000|
  |Macro avg|0.841|0.837|0.838|5000|
  |Weighted avg|0.856|0.856|0.855|5000|
  
  
  
  ```
  
  !pip install transformers
  
  from transformers.models.auto.modeling_auto import AutoModelForSequenceClassification
  from transformers import AutoTokenizer, AutoModel, utils, pipeline
  
  tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
  model = AutoModelForSequenceClassification.from_pretrained("Pafebla/XLMroBERTa_clasificacion_noticiasv2", output_attentions=True)
  
  mapping_labels = {0: 'Ayuntamiento', 1: 'Ciencia', 2: 'Cultura', 3: 'Deporte', 4: 'Economía', 5: 'Educación', 6: 'Igualdad_Integración', 7: 'MedioAmbiente', 8: 'Movilidad', 9: 'Política', 10: 'Salud', 11: 'Sucesos', 12: 'Tecnología', 13: 'Turismo'}
  
  text = "La bolsa está cayendo. El PIB está fatal."
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_input)
  label_idx = output[0].argmax().item()
  predicted_label = mapping_labels[label_idx]
  predicted_label
  
  ```