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nepali-gec-error-type-classifier
DipeshChaudhary/nepali-gec-error-type-classifier
nepali-gec-error-type-classifier is a token classification model from DipeshChaudhary. Use it when you need labels on individual words, such as names. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Nov 10, 2025
- Updated
- Nov 10, 2025
- SHA
- bd63906a
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Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
nepali-gec-error-type-classifier
This model is a fine-tuned version of IRIIS-RESEARCH/RoBERTa_Nepali_125M on the arrow dataset. It achieves the following results on the evaluation set:
- Loss: 0.1340
- Accuracy: 0.9502
- Macro F1: 0.6346
- Num Error Tokens: 1934543
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 1024
- eval_batch_size: 1024
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Num Error Tokens |
|---|---|---|---|---|---|---|
| 0.5944 | 0.1574 | 1000 | 0.4266 | 0.8294 | 0.5394 | 1934543 |
| 0.3682 | 0.3149 | 2000 | 0.2821 | 0.8919 | 0.5882 | 1934543 |
| 0.2972 | 0.4723 | 3000 | 0.2351 | 0.9111 | 0.6027 | 1934543 |
| 0.2618 | 0.6297 | 4000 | 0.2077 | 0.9221 | 0.6109 | 1934543 |
| 0.2379 | 0.7872 | 5000 | 0.1900 | 0.9292 | 0.6163 | 1934543 |
| 0.2217 | 0.9446 | 6000 | 0.1780 | 0.9339 | 0.6198 | 1934543 |
| 0.2066 | 1.1020 | 7000 | 0.1675 | 0.9378 | 0.6228 | 1934543 |
| 0.1977 | 1.2594 | 8000 | 0.1604 | 0.9405 | 0.6248 | 1934543 |
| 0.192 | 1.4169 | 9000 | 0.1546 | 0.9425 | 0.6263 | 1934543 |
| 0.1836 | 1.5743 | 10000 | 0.1502 | 0.9443 | 0.6276 | 1934543 |
| 0.18 | 1.7317 | 11000 | 0.1466 | 0.9456 | 0.6286 | 1934543 |
| 0.1763 | 1.8892 | 12000 | 0.1431 | 0.9469 | 0.6296 | 1934543 |
| 0.172 | 2.0466 | 13000 | 0.1408 | 0.9476 | 0.6301 | 1934543 |
| 0.1672 | 2.2040 | 14000 | 0.1383 | 0.9486 | 0.6317 | 1934543 |
| 0.1673 | 2.3615 | 15000 | 0.1368 | 0.9492 | 0.6321 | 1934543 |
| 0.1646 | 2.5189 | 16000 | 0.1355 | 0.9496 | 0.6333 | 1934543 |
| 0.1629 | 2.6763 | 17000 | 0.1349 | 0.9499 | 0.6327 | 1934543 |
| 0.1631 | 2.8338 | 18000 | 0.1342 | 0.9501 | 0.6337 | 1934543 |
| 0.1621 | 2.9912 | 19000 | 0.1340 | 0.9502 | 0.6346 | 1934543 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
- Task
- Token Classification
- Library
- transformers
- Type
- roberta
- Created
- Nov 10, 2025
- Updated
- Nov 10, 2025