---
tags:
- text2text-generation
- definition-modeling
metrics:
- rouge
model-index:
- name: mt0-definition-ru-xl
  results: []
language:
- ru
widget:
- text: "Мы сели в тачку и поехали по ресторанам. Что такое тачка?"
  example_title: "Definition generation"
license: cc-by-sa-4.0
---

# mT0-Definition-Ru XL

This model is a version of [mT0 XL](https://huggingface.co/bigscience/mt0-xl) finetuned on the Russian part of [CodWoE](https://aclanthology.org/2022.semeval-1.1/),
a dataset of definitions and usage examples.

It generates definitions of Russian words in context.
Its input is the usage example and the instruction question "Что такое TARGET_WORD?"

## Model description

See details in the paper `Enriching Word Usage Graphs with Cluster Definitions` (LREC-COLING'2024) by
Mariia Fedorova, Andrey Kutuzov, Nikolay Arefyev and Dominik Schlechtweg.

## Intended uses & limitations

The model is intended for research purposes, as a source of contextualized dictionary-like lexical definitions.
Generated definitions can contain all sorts of biases and stereotypes, stemming from the underlying language model.

## Training and evaluation data

Russian subset of *CodWoE* ([Mickus et al., SemEval 2022](https://aclanthology.org/2022.semeval-1.1/)).

## Training results

mT0-Definition-Ru XL achieves the following results on the CodWoE evaluation set:

- Loss: 1.7996
- Rouge1: 17.5576
- Rouge2: 8.7614
- Rougel: 17.2533
- Rougelsum: 17.3204
- Gen Len: 21.6774

## Training procedure

mT0-Definition-Ru XL was fine-tuned in a sequence-to-sequence mode on examples of contextualized dictionary definitions.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0

### Framework versions

- Transformers 4.37.1
- Pytorch 1.13.1+rocm5.2
- Datasets 2.16.1
- Tokenizers 0.15.1

## Citation