
### XLM-RoBERTa-large-XNLI-ANLI

XLM-RoBERTa-large model finetunned over several NLI datasets, ready to use for zero-shot classification.

Here are the accuracies for several test datasets:

|                             | XNLI-es | XNLI-fr | ANLI-R1 | ANLI-R2 | ANLI-R3 |
|-----------------------------|---------|---------|---------|---------|---------|
| xlm-roberta-large-xnli-anli | 93.7% | 93.2% | 68.5%  | 53.6%  | 49.0%  |

The model can be loaded with the zero-shot-classification pipeline like so:
```
from transformers import pipeline
classifier = pipeline("zero-shot-classification", 
                       model="vicgalle/xlm-roberta-large-xnli-anli")
```
You can then use this pipeline to classify sequences into any of the class names you specify:
```
sequence_to_classify = "Algún día iré a ver el mundo"
candidate_labels = ['viaje', 'cocina', 'danza']
classifier(sequence_to_classify, candidate_labels)
#{'sequence': 'Algún día iré a ver el mundo',
#'labels': ['viaje', 'danza', 'cocina'],
#'scores': [0.9991760849952698, 0.0004178212257102132, 0.0004059972707182169]}
```
{"language": "multilingual", "license": "mit", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["mnli", "xnli", "anli"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "De pugna erat fantastic. Nam Crixo decem quam dilexit et praeciderunt caput aemulus.", "candidate_labels": "violent, peaceful"}, {"text": "La pel\u00edcula empezaba bien pero termin\u00f3 siendo un desastre.", "candidate_labels": "positivo, negativo, neutral"}, {"text": "La pel\u00edcula empez\u00f3 siendo un desastre pero en general fue bien.", "candidate_labels": "positivo, negativo, neutral"}, {"text": "\u00bfA qui\u00e9n vas a votar en 2020?", "candidate_labels": "Europa, elecciones, pol\u00edtica, ciencia, deportes"}]}
transformers pytorch safetensors xlm-roberta text-classification zero-shot-classification nli multilingual dataset:mnli dataset:xnli dataset:anli doi:10.57967/hf/0977 license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #zero-shot-classification #nli #multilingual #dataset-mnli #dataset-xnli #dataset-anli #doi-10.57967/hf/0977 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

### XLM-RoBERTa-large-XNLI-ANLI


XLM-RoBERTa-large model finetunned over several NLI datasets, ready to use for zero-shot classification.


Here are the accuracies for several test datasets:



The model can be loaded with the zero-shot-classification pipeline like so:


You can then use this pipeline to classify sequences into any of the class names you specify:



### XLM-RoBERTa-large-XNLI-ANLI


XLM-RoBERTa-large model finetunned over several NLI datasets, ready to use for zero-shot classification.


Here are the accuracies for several test datasets:



The model can be loaded with the zero-shot-classification pipeline like so:


You can then use this pipeline to classify sequences into any of the class names you specify:
TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #zero-shot-classification #nli #multilingual #dataset-mnli #dataset-xnli #dataset-anli #doi-10.57967/hf/0977 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### XLM-RoBERTa-large-XNLI-ANLI


XLM-RoBERTa-large model finetunned over several NLI datasets, ready to use for zero-shot classification.


Here are the accuracies for several test datasets:



The model can be loaded with the zero-shot-classification pipeline like so:


You can then use this pipeline to classify sequences into any of the class names you specify:
passage: TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #zero-shot-classification #nli #multilingual #dataset-mnli #dataset-xnli #dataset-anli #doi-10.57967/hf/0977 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
### XLM-RoBERTa-large-XNLI-ANLI


XLM-RoBERTa-large model finetunned over several NLI datasets, ready to use for zero-shot classification.


Here are the accuracies for several test datasets:



The model can be loaded with the zero-shot-classification pipeline like so:


You can then use this pipeline to classify sequences into any of the class names you specify: