# A zero-shot classifier based on bertin-roberta-base-spanish

This model was trained on the basis of the model bertin-roberta-base-spanish using Cross encoder for NLI task. A CrossEncoder takes a sentence pair as input and outputs a label so it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.

You can use it with Hugging Face's Zero-shot pipeline to make zero-shot classifications. Given a sentence and an arbitrary set of labels/topics, it will output the likelihood of the sentence belonging to each of the topic.

## Usage (HuggingFace Transformers)

The simplest way to use the model is the huggingface transformers pipeline tool. Just initialize the pipeline specifying the task as "zero-shot-classification" and select "hackathon-pln-es/bertin-roberta-base-zeroshot-esnli" as model.

from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="hackathon-pln-es/bertin-roberta-base-zeroshot-esnli")

classifier(
"El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo",
hypothesis_template="Esta oración es sobre {}."
)


The hypothesis_template parameter is important and should be in Spanish. In the widget on the right, this parameter is set to its default value: "This example is {}.", so different results are expected.

## Training

We used sentence-transformers to train the model.

Dataset

We used a collection of datasets of Natural Language Inference as training data:

• ESXNLI, only the part in spanish
• SNLI, automatically translated
• MultiNLI, automatically translated

The whole dataset used is available here.