readme: ---
  widget:
  - text: "El dólar se dispara tras la reunión de la Fed"
  ---
  
  
  # Spanish News Classification Headlines
  
  SNCH: this model was develop by [M47Labs](https://www.m47labs.com/es/)  the goal is text classification, the base model use was [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased), it was fine-tuned on 1000 example dataset.
  
  
  ## Dataset Sample
  
  Dataset size : 1000
  
  Columns: idTask,task content 1,idTag,tag.
  
  |idTask|task content 1|idTag|tag|
  |------|------|------|------|
  |3637d9ac-119c-4a8f-899c-339cf5b42ae0|Alcalá de Guadaíra celebra la IV Semana de la Diversidad Sexual con acciones de sensibilización|81b36360-6cbf-4ffa-b558-9ef95c136714|sociedad|
  |d56bab52-0029-45dd-ad90-5c17d4ed4c88|El Archipiélago Chinijo Graciplus se impone en el Trofeo Centro Comercial Rubicón|ed198b6d-a5b9-4557-91ff-c0be51707dec|deportes|
  |dec70bc5-4932-4fa2-aeac-31a52377be02|Un total de 39 personas padecen ELA actualmente en la provincia|81b36360-6cbf-4ffa-b558-9ef95c136714|sociedad|
  |fb396ba9-fbf1-4495-84d9-5314eb731405|Eurocopa 2021 : Italia vence a Gales y pasa a octavos con su candidatura reforzada|ed198b6d-a5b9-4557-91ff-c0be51707dec|deportes|
  |bc5a36ca-4e0a-422e-9167-766b41008c01|Resolución de 10 de junio de 2021, del Ayuntamiento de Tarazona de La Mancha (Albacete), referente a la convocatoria para proveer una plaza.|81b36360-6cbf-4ffa-b558-9ef95c136714|sociedad|
  |a87f8703-ce34-47a5-9c1b-e992c7fe60f6|El primer ministro sueco pierde una moción de censura|209ae89e-55b4-41fd-aac0-5400feab479e|politica|
  |d80bdaad-0ad5-43a0-850e-c473fd612526|El dólar se dispara tras la reunión de la Fed|11925830-148e-4890-a2bc-da9dc059dc17|economia|
  
  
  ## Labels:
  
   * ciencia_tecnologia
   
   * clickbait
   
   * cultura 
   
   * deportes
   
   * economia
   
   * educacion
   
   * medio_ambiente
   
   * opinion
   
   * politica
   
   * sociedad
   
  
  
  ## Example of Use
  
  ### Pipeline
  
  ```{python}
  
  import torch
  from transformers import AutoTokenizer, BertForSequenceClassification,TextClassificationPipeline
  
  
  review_text = 'los vehiculos que esten esperando pasajaeros deberan estar apagados para reducir emisiones'
  path = "M47Labs/spanish_news_classification_headlines"
  tokenizer = AutoTokenizer.from_pretrained(path)
  model = BertForSequenceClassification.from_pretrained(path)
  
  
  nlp = TextClassificationPipeline(task = "text-classification",
                  model = model,
                  tokenizer = tokenizer)
  
  print(nlp(review_text))
  
  ```
  
  ```[{'label': 'medio_ambiente', 'score': 0.5648820996284485}]```
  
  ### Pytorch
  
  ```{python}
  
  import torch
  from transformers import AutoTokenizer, BertForSequenceClassification,TextClassificationPipeline
  from numpy import np
  
  model_name  = 'M47Labs/spanish_news_classification_headlines'
  MAX_LEN = 32
  
  
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  
  model = AutoModelForSequenceClassification.from_pretrained(model_name)
  
  texto = "las emisiones estan bajando, debido a las medidas ambientales tomadas por el gobierno"
  
  
  encoded_review = tokenizer.encode_plus(
    texto,
    max_length=MAX_LEN,
    add_special_tokens=True,
    #return_token_type_ids=False,
    pad_to_max_length=True,
    return_attention_mask=True,
    return_tensors='pt',
  )
  
  input_ids = encoded_review['input_ids']
  attention_mask = encoded_review['attention_mask']
  output = model(input_ids, attention_mask)
  
  _, prediction = torch.max(output['logits'], dim=1)
  print(f'Review text: {texto}')
  
  print(f'Sentiment  : {model.config.id2label[prediction.detach().cpu().numpy()[0]]}')
  
  ```
  
  ```Review text: las emisiones estan bajando, debido a las medidas ambientales tomadas por el gobierno```
  
  
  ```Sentiment  : medio_ambiente```
  
  
  A more in depth example on how to use the model can be found in this colab notebook: https://colab.research.google.com/drive/1XsKea6oMyEckye2FePW_XN7Rf8v41Cw_?usp=sharing
  
  
  ## Finetune Hyperparameters
  
  
   * MAX_LEN = 32
   * TRAIN_BATCH_SIZE = 8
   * VALID_BATCH_SIZE = 4
   * EPOCHS = 5
   * LEARNING_RATE = 1e-05
   
  ## Train Results
  
  |n_example|epoch|loss|acc|
  |------|------|------|------|
  |100|0|2.286327266693115|12.5|
  |100|1|2.018876111507416|40.0|
  |100|2|1.8016730904579163|43.75|
  |100|3|1.6121837735176086|46.25|
  |100|4|1.41565443277359|68.75|
   
  |n_example|epoch|loss|acc|
  |------|------|------|------|
  |500|0|2.0770938420295715|24.5|
  |500|1|1.6953029704093934|50.25|
  |500|2|1.258900796175003|64.25|
  |500|3|0.8342628020048142|78.25|
  |500|4|0.5135736921429634|90.25|
   
  |n_example|epoch|loss|acc|
  |------|------|------|------|
  |1000|0|1.916002897115854|36.1997226074896|
  |1000|1|1.2941598492664295|62.2746185852982|
  |1000|2|0.8201534710415117|76.97642163661581|
  |1000|3|0.524806430051615|86.9625520110957|
  |1000|4|0.30662027455784463|92.64909847434119|
  
  ## Validation Results
   
  |n_examples|100|
  |------|------|
  |Accuracy Score|0.35|
  |Precision (Macro)|0.35|
  |Recall (Macro)|0.16|
  
  |n_examples|500|
  |------|------|
  |Accuracy Score|0.62|
  |Precision (Macro)|0.60|
  |Recall (Macro)|0.47|
  
  |n_examples|1000|
  |------|------|
  |Accuracy Score|0.68|
  |Precision(Macro)|0.68|
  |Recall (Macro)|0.6