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--- |
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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 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. |
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## Dataset Sample |
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Dataset size : 1000 |
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Columns: idTask,task content 1,idTag,tag. |
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|idTask|task content 1|idTag|tag| |
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|------|------|------|------| |
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|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| |
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|d56bab52-0029-45dd-ad90-5c17d4ed4c88|El Archipiélago Chinijo Graciplus se impone en el Trofeo Centro Comercial Rubicón|ed198b6d-a5b9-4557-91ff-c0be51707dec|deportes| |
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|dec70bc5-4932-4fa2-aeac-31a52377be02|Un total de 39 personas padecen ELA actualmente en la provincia|81b36360-6cbf-4ffa-b558-9ef95c136714|sociedad| |
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|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| |
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|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| |
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|a87f8703-ce34-47a5-9c1b-e992c7fe60f6|El primer ministro sueco pierde una moción de censura|209ae89e-55b4-41fd-aac0-5400feab479e|politica| |
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|d80bdaad-0ad5-43a0-850e-c473fd612526|El dólar se dispara tras la reunión de la Fed|11925830-148e-4890-a2bc-da9dc059dc17|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" |
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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': 0.5648820996284485}]``` |
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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' |
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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 : medio_ambiente``` |
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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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## Finetune Hyperparameters |
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* MAX_LEN = 32 |
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* TRAIN_BATCH_SIZE = 8 |
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* VALID_BATCH_SIZE = 4 |
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* EPOCHS = 5 |
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* LEARNING_RATE = 1e-05 |
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## Train Results |
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|n_example|epoch|loss|acc| |
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|------|------|------|------| |
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|100|0|2.286327266693115|12.5| |
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|100|1|2.018876111507416|40.0| |
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|100|2|1.8016730904579163|43.75| |
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|100|3|1.6121837735176086|46.25| |
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|100|4|1.41565443277359|68.75| |
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|n_example|epoch|loss|acc| |
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|500|0|2.0770938420295715|24.5| |
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|500|1|1.6953029704093934|50.25| |
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|500|2|1.258900796175003|64.25| |
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|500|3|0.8342628020048142|78.25| |
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|500|4|0.5135736921429634|90.25| |
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|n_example|epoch|loss|acc| |
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|------|------|------|------| |
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|1000|0|1.916002897115854|36.1997226074896| |
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|1000|1|1.2941598492664295|62.2746185852982| |
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|1000|2|0.8201534710415117|76.97642163661581| |
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|1000|3|0.524806430051615|86.9625520110957| |
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|1000|4|0.30662027455784463|92.64909847434119| |
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## Validation Results |
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|n_examples|100| |
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|Accuracy Score|0.35| |
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|Precision (Macro)|0.35| |
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|Recall (Macro)|0.16| |
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|n_examples|500| |
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|Accuracy Score|0.62| |
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|Precision (Macro)|0.60| |
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|Recall (Macro)|0.47| |
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|n_examples|1000| |
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|Accuracy Score|0.68| |
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|Precision(Macro)|0.68| |
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|Recall (Macro)|0.64| |
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