Upload README.md

README.md

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+---
+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)|0.64|
+
+
+
+ ![alt text](https://media-exp1.licdn.com/dms/image/C4D0BAQHpfgjEyhtE1g/company-logo_200_200/0/1625210573748?e=1638403200&v=beta&t=toQNpiOlyim5Ja4f7Ejv8yKoCWifMsLWjkC7XnyXICI "Logo M47")
+