metadata
pipeline_tag: zero-shot-classification
tags:
- zero-shot-classification
- nli
language:
- es
datasets:
- hackathon-pln-es/nli-es
widget:
- text: >-
El autor se perfila, a los 50 años de su muerte, como uno de los grandes
de su siglo
candidate_labels: cultura, sociedad, economia, salud, deportes
A zero-shot classifier based on bertin-roberta-base-finetuning-esnli
Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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",
candidate_labels=["cultura", "sociedad", "economia", "salud", "deportes"],
hypothesis_template="Este ejemplo es {}."
)
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
Dataset
We used a collection of datasets of Natural Language Inference as training data:
The whole dataset used is available here.
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)