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M47Labs/spanish_news_classification_headlines

sourceHugging Faceupdated 5y agoView on Hugging Face

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Model Card

Spanish News Classification Headlines

SNCH: this model was develop by M47Labs the goal is text classification, the base model use was BETO, 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

import torch from transformers import AutoTokenizer, BertForSequenceClassification,TextClassificationPipeline from numpy import np

modelname = 'M47Labs/spanishnewsclassificationheadlines' MAX_LEN = 32

tokenizer = AutoTokenizer.frompretrained(modelname)

model = AutoModelForSequenceClassification.frompretrained(modelname)

texto = "las emisiones estan bajando, debido a las medidas ambientales tomadas por el gobierno"

encodedreview = tokenizer.encodeplus( texto, maxlength=MAXLEN, addspecialtokens=True, #returntokentypeids=False, padtomaxlength=True, returnattentionmask=True, return_tensors='pt', )

inputids = encodedreview['inputids'] attentionmask = encodedreview['attentionmask'] output = model(inputids, attentionmask)

_, prediction = torch.max(output['logits'], dim=1) print(f'Review text: {texto}')

print(f'Sentiment : {model.config.id2label[prediction.detach().cpu().numpy()[0]]}')

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")