

# 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.

```python
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="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](https://www.SBERT.net) to train the model.

**Dataset**

We used a collection of datasets of Natural Language Inference as training data:
 - [ESXNLI](https://raw.githubusercontent.com/artetxem/esxnli/master/esxnli.tsv), only the part in spanish
 - [SNLI](https://nlp.stanford.edu/projects/snli/), automatically translated
 - [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/), automatically translated

The whole dataset used is available [here](https://huggingface.co/datasets/hackathon-pln-es/nli-es).

## Authors

- [Anibal Pérez](https://huggingface.co/Anarpego)
- [Emilio Tomás Ariza](https://huggingface.co/medardodt)
- [Lautaro Gesuelli Pinto](https://huggingface.co/Lautaro)
- [Mauricio Mazuecos](https://huggingface.co/mmazuecos)

{"language": ["es"], "tags": ["zero-shot-classification", "nli"], "datasets": ["hackathon-pln-es/nli-es"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Para detener la pandemia, es importante que todos se presenten a vacunarse.", "candidate_labels": "salud, deporte, entretenimiento"}]}
transformers pytorch safetensors roberta text-classification zero-shot-classification nli es dataset:hackathon-pln-es/nli-es autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #nli #es #dataset-hackathon-pln-es/nli-es #autotrain_compatible #endpoints_compatible #region-us 



# 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.



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.

## Authors

- Anibal Pérez
- Emilio Tomás Ariza
- Lautaro Gesuelli Pinto
- Mauricio Mazuecos

# 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.



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. ## Authors

- Anibal Pérez
- Emilio Tomás Ariza
- Lautaro Gesuelli Pinto
- Mauricio Mazuecos
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #nli #es #dataset-hackathon-pln-es/nli-es #autotrain_compatible #endpoints_compatible #region-us 
 # 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.



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. ## Authors

- Anibal Pérez
- Emilio Tomás Ariza
- Lautaro Gesuelli Pinto
- Mauricio Mazuecos
passage: TAGS
#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #nli #es #dataset-hackathon-pln-es/nli-es #autotrain_compatible #endpoints_compatible #region-us 
# 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.



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.## Authors

- Anibal Pérez
- Emilio Tomás Ariza
- Lautaro Gesuelli Pinto
- Mauricio Mazuecos