readme: ---
  language:
  - es
  tags:
  - es
  - ticket classification
  license: "apache-2.0"
  datasets:
  - self made to classify whether text is related to technology or not.
  metrics:
  - fscore
  - accuracy
  - precision
  - recall
  ---
  # BETO(cased)
  This model was built using pytorch.
  ## Model description
  Input for the model: Any spanish text
  Output for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))
  #### How to use
  Here is how to use this model to get the features of a given text in *PyTorch*:
  ```python
  # You can include sample code which will be formatted
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  tokenizer = AutoTokenizer.from_pretrained("hiiamsid/BETO_es_binary_classification")
  model = AutoModelForSequenceClassification.from_pretrained("hiiamsid/BETO_es_binary_classification")
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_input)
  ```
  ## Training procedure
  I trained on the dataset on the [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased).
  