metadata

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:

# 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.