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xlm-roberta-ovos-intent-classifier

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metadata
license: apache-2.0
language:
  - multilingual
  - af
  - am
  - ar
  - as
  - az
  - be
  - bg
  - bn
  - br
  - bs
  - ca
  - cs
  - cy
  - da
  - de
  - el
  - en
  - eo
  - es
  - et
  - eu
  - fa
  - fi
  - fr
  - fy
  - ga
  - gd
  - gl
  - gu
  - ha
  - he
  - hi
  - hr
  - hu
  - hy
  - id
  - is
  - it
  - ja
  - jv
  - ka
  - kk
  - km
  - kn
  - ko
  - ku
  - ky
  - la
  - lo
  - lt
  - lv
  - mg
  - mk
  - ml
  - mn
  - mr
  - ms
  - my
  - ne
  - nl
  - 'no'
  - om
  - or
  - pa
  - pl
  - ps
  - pt
  - ro
  - ru
  - sa
  - sd
  - si
  - sk
  - sl
  - so
  - sq
  - sr
  - su
  - sv
  - sw
  - ta
  - te
  - th
  - tl
  - tr
  - ug
  - uk
  - ur
  - uz
  - vi
  - xh
  - yi
  - zh
datasets:
  - Jarbas/ovos_intents_train
base_model:
  - FacebookAI/xlm-roberta-base
metrics:
  - accuracy
  - precision
  - recall
  - f1
  - matthews_correlation

XLM-RoBERTa OVOS intent classifier (base-sized model)

XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al. and first released in this repository.

This model was fine-tuned to classify intents based on the dataset Jarbas/ovos_intents_train

Intended uses & limitations

You can use the raw model for intent classification in the Open Voice OS project context.

Usage 

from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)