hiiamsid/BETO_es_binary_classification

1---2language:3- es4tags:5- es6- ticket classification7license: "apache-2.0"8datasets:9- self made to classify whether text is related to technology or not.10metrics:11- fscore12- accuracy13- precision14- recall15---16# BETO(cased)17This model was built using pytorch.18## Model description19Input for the model: Any spanish text20Output for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))21#### How to use22Here is how to use this model to get the features of a given text in *PyTorch*:23```python24# You can include sample code which will be formatted25from transformers import AutoTokenizer, AutoModelForSequenceClassification26tokenizer = AutoTokenizer.from_pretrained("hiiamsid/BETO_es_binary_classification")27model = AutoModelForSequenceClassification.from_pretrained("hiiamsid/BETO_es_binary_classification")28text = "Replace me by any text you'd like."29encoded_input = tokenizer(text, return_tensors='pt')30output = model(**encoded_input)31```32## Training procedure33I trained on the dataset on the [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased).34