metadata

license: apache-2.0
tags:
  - mdeberta-v3-base
  - text-classification
  - nli
  - natural-language-inference
  - multilingual
  - multitask
  - multi-task
  - pipeline
  - extreme-multi-task
  - extreme-mtl
  - tasksource
  - zero-shot
  - rlhf
datasets:
  - xnli
  - metaeval/xnli
  - americas_nli
  - MoritzLaurer/multilingual-NLI-26lang-2mil7
  - stsb_multi_mt
  - paws-x
  - miam
  - strombergnlp/x-stance
  - tyqiangz/multilingual-sentiments
  - metaeval/universal-joy
  - amazon_reviews_multi
  - cardiffnlp/tweet_sentiment_multilingual
  - strombergnlp/offenseval_2020
  - offenseval_dravidian
  - nedjmaou/MLMA_hate_speech
  - xglue
  - ylacombe/xsum_factuality
  - metaeval/x-fact
  - pasinit/xlwic
  - tasksource/oasst1_dense_flat
  - papluca/language-identification
  - wili_2018
  - exams
  - xcsr
  - xcopa
  - juletxara/xstory_cloze
  - Anthropic/hh-rlhf
  - OpenAssistant/oasst1
language:
  - multilingual
  - zh
  - ja
  - ar
  - ko
  - de
  - fr
  - es
  - pt
  - hi
  - id
  - it
  - tr
  - ru
  - bn
  - ur
  - mr
  - ta
  - vi
  - fa
  - pl
  - uk
  - nl
  - sv
  - he
  - sw
  - ps
pipeline_tag: zero-shot-classification

Model Card for mDeBERTa-v3-base-tasksource-nli

Multilingual mdeberta-v3-base with multi-task training on mtasksource

This model can be used as a stable starting-point for further fine-tuning, or directly in zero-shot NLI model or a zero-shot pipeline.

In addition, you can use the provided adapters to directly load a model for hundreds of tasks.

!pip install tasknet, tasksource -q
import tasknet as tn 
pipe=tn.load_pipeline('sileod/mdeberta-v3-base-tasksource-nli',"miam/dihana")
pipe(['si','como esta?'])

Software

https://github.com/sileod/tasksource/ https://github.com/sileod/tasknet/ Training took 7 days on RTX6000 24GB gpu.