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widget: |
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- text: "El dólar se dispara tras la reunión de la Fed" |
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--- |
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# Spanish News Classification Headlines |
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SNCH: this model was developed 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), however this model has not been fine-tuned on any dataset. The objective is to show the performance of this model when is used with the objective of inference without training at all. |
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## Dataset validation Sample |
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Dataset size : 1000 |
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Columns: idTask,task content 1,idTag,tag. |
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|task content|tag| |
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|Alcalá de Guadaíra celebra la IV Semana de la Diversidad Sexual con acciones de sensibilización|sociedad| |
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|El Archipiélago Chinijo Graciplus se impone en el Trofeo Centro Comercial Rubicón|deportes| |
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|Un total de 39 personas padecen ELA actualmente en la provincia|sociedad| |
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|Eurocopa 2021 : Italia vence a Gales y pasa a octavos con su candidatura reforzada|deportes| |
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|Resolución de 10 de junio de 2021, del Ayuntamiento de Tarazona de La Mancha (Albacete), referente a la convocatoria para proveer una plaza.|sociedad| |
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|El primer ministro sueco pierde una moción de censura|politica| |
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|El dólar se dispara tras la reunión de la Fed|economia| |
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## Labels: |
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* ciencia_tecnologia |
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* clickbait |
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* cultura |
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* deportes |
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* economia |
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* educacion |
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* medio_ambiente |
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* opinion |
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* politica |
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* sociedad |
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## Example of Use |
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### Pipeline |
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```{python} |
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import torch |
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from transformers import AutoTokenizer, BertForSequenceClassification,TextClassificationPipeline |
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review_text = 'los vehiculos que esten esperando pasajaeros deberan estar apagados para reducir emisiones' |
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path = "M47Labs/spanish_news_classification_headlines_untrained" |
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tokenizer = AutoTokenizer.from_pretrained(path) |
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model = BertForSequenceClassification.from_pretrained(path) |
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nlp = TextClassificationPipeline(task = "text-classification", |
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model = model, |
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tokenizer = tokenizer) |
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print(nlp(review_text)) |
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``` |
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```[{'label': 'medio_ambiente', 'score': 0.2834321384291023}]``` |
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### Pytorch |
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```{python} |
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import torch |
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from transformers import AutoTokenizer, BertForSequenceClassification,TextClassificationPipeline |
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from numpy import np |
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model_name = 'M47Labs/spanish_news_classification_headlines_untrained' |
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MAX_LEN = 32 |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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texto = "las emisiones estan bajando, debido a las medidas ambientales tomadas por el gobierno" |
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encoded_review = tokenizer.encode_plus( |
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texto, |
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max_length=MAX_LEN, |
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add_special_tokens=True, |
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#return_token_type_ids=False, |
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pad_to_max_length=True, |
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return_attention_mask=True, |
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return_tensors='pt', |
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) |
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input_ids = encoded_review['input_ids'] |
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attention_mask = encoded_review['attention_mask'] |
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output = model(input_ids, attention_mask) |
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_, prediction = torch.max(output['logits'], dim=1) |
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print(f'Review text: {texto}') |
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print(f'Sentiment : {model.config.id2label[prediction.detach().cpu().numpy()[0]]}') |
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``` |
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```Review text: las emisiones estan bajando, debido a las medidas ambientales tomadas por el gobierno``` |
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```Sentiment : opinion``` |
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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 |
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## Validation Results |
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|Full Dataset|| |
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|Accuracy Score|0.362| |
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|Precision (Macro)|0.21| |
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|Recall (Macro)|0.22| |
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