Instructions to use deepanshumiglani0408/indictrans2_finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepanshumiglani0408/indictrans2_finetune with Transformers:
# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("deepanshumiglani0408/indictrans2_finetune", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
indictrans2-en-hi-finetuned-iitb-kaggle
This model is a fine-tuned version of ai4bharat/indictrans2-en-indic-dist-200M on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2907
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: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 469 | 5.4269 |
| 7.4686 | 2.0 | 938 | 2.9200 |
| 4.0507 | 3.0 | 1407 | 1.5974 |
| 2.1072 | 4.0 | 1876 | 1.2418 |
Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.21.2
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Model tree for deepanshumiglani0408/indictrans2_finetune
Base model
ai4bharat/indictrans2-en-indic-dist-200M