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

```
{}
region:us
TAGS
#region-us 

Mapping of labels:



Evaluation w/ Gold Standard (10%, 5000 messages)


* Running Prediction \*
Num examples = 5000
Batch size = 16




TAGS
#region-us 

TAGS
#region-us 
