UzRoBERTa-v2

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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

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.1+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0