51-language coverage
According to the model card, the MASSIVE association covers more than 1M utterances across 51 languages for intent and slot tasks.
Open Source Model Profile · qanastek
51-languages-classifier
51-languages-classifier is a text-classification model from qanastek. Its model card documents XLM-Roberta use and a Transformers pipeline for 51-language classification.
- Publisher
- qanastek
- Task
- text-classification
- Model type
- Unknown
- License
- cc-by-4.0
- Library
- transformers
- Publication status
- Approved for indexing
Model overview
51-languages-classifier is published by qanastek as a text-classification model. Captured metadata records a cc-by-4.0 license and transformers library support. According to the model card, it is presented as an XLM-Roberta model with a Hugging Face Transformers classification-pipeline example.
Recorded capabilities
Transformers pipeline usage
According to the model card, the publisher documents loading the model with AutoTokenizer, AutoModelForSequenceClassification, and TextClassificationPipeline.
Multilingual classification tags
Hub tags record transformers, pytorch, text-classification, and multi-class-classification with a MASSIVE dataset link.
Use cases in the source record
- Multilingual text-classification workflows that follow the card's TextClassificationPipeline loading pattern.
- Language-identification style checks of the kind illustrated by the card's Hebrew example and MASSIVE language coverage.
Limitations and unknowns
- No architecture list was extracted from this record.
- No parameter count was extracted from this record.
- No context-window value was extracted from this record.
- No evaluation results were extracted from this record.
- Provider state is historical snapshot data, not independently refreshed current availability.
Source and provenance
Source: qanastek/51-languages-classifier
Captured: Unknown. Processed: 2026-09-07T19:34:56.435940+00:00.
People Involved LABRAK Yanis (1) Affiliations LIA, NLP team , Avignon University, Avignon, France. Model XLM-Roberta : https://huggingface.co/xlm-roberta-base Paper : Unsupervised Cross-lingual Representation Learning at Scale Demo: How to use in HuggingFace Transformers Pipeline Requires transformers : pip install transformers from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline model_name = 'qanastek/51-languages-classifier' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) classifier = TextClassificationPip…
F001F002F003F004F005F006F007F009F010F011