| --- |
| 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). |
|
|
