metadata

widget:
  - text: Kuchli yomg‘irlar tufayli bir qator <mask> kuchli sel oqishi kuzatildi.
    example_title: Example 1
  - text: >-
      Shu munosabat bilan O‘zbekiston Prezidenti global inqiroz sharoitida
      savdo-iqtisodiy hamkorlikni <mask> va hududlararo aloqalarni
      rivojlantirishning muhim masalalariga to‘xtalib o‘tdi.
    example_title: Example 2
tags:
  - generated_from_trainer
datasets:
  - sinonimayzer/mixed-data
language:
  - uz
library_name: transformers
pipeline_tag: fill-mask

UzRoBERTa-v2

This model achieves the following results on the evaluation set:

Loss: 1.9097

How to use

>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='sinonimayzer/UzRoBERTa-v2')
>>> unmasker("Kuchli yomg‘irlar tufayli bir qator <mask> kuchli sel oqishi kuzatildi.")

[{'score': 0.3318027853965759,
  'token': 4877,
  'token_str': ' hududlarda',
  'sequence': 'Kuchli yomg‘irlar tufayli bir qator hududlarda kuchli sel oqishi kuzatildi.'},
 {'score': 0.13175441324710846,
  'token': 14470,
  'token_str': ' viloyatlarda',
  'sequence': 'Kuchli yomg‘irlar tufayli bir qator viloyatlarda kuchli sel oqishi kuzatildi.'},
 {'score': 0.09735308587551117,
  'token': 13555,
  'token_str': ' tumanlarda',
  'sequence': 'Kuchli yomg‘irlar tufayli bir qator tumanlarda kuchli sel oqishi kuzatildi.'},
 {'score': 0.09112472087144852,
  'token': 12261,
  'token_str': ' shaharlarda',
  'sequence': 'Kuchli yomg‘irlar tufayli bir qator shaharlarda kuchli sel oqishi kuzatildi.'},
 {'score': 0.05940879508852959,
  'token': 2767,
  'token_str': ' joylarda',
  'sequence': 'Kuchli yomg‘irlar tufayli bir qator joylarda kuchli sel oqishi kuzatildi.'}]

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 92
eval_batch_size: 92
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 500000

Training results

Training Loss	Epoch	Step	Validation Loss
2.3673	0.25	100000	2.4588
2.0797	0.51	200000	2.1653
1.9369	0.76	300000	2.0265
1.8545	1.02	400000	1.9456
1.8133	1.27	500000	1.9101

Framework versions

Transformers 4.35.2
Pytorch 2.1.1+cu121
Datasets 2.15.0
Tokenizers 0.15.0