Instructions to use thunderboltc/nllb_sanlish_bangla_ckpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thunderboltc/nllb_sanlish_bangla_ckpt with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("thunderboltc/nllb_sanlish_bangla_ckpt") model = AutoModelForSeq2SeqLM.from_pretrained("thunderboltc/nllb_sanlish_bangla_ckpt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
nllb_sanlish_bangla_ckpt
This model is a fine-tuned version of facebook/nllb-200-distilled-600M on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 2.4720
- eval_bleu: 2.5433
- eval_chrf: 22.0207
- eval_runtime: 4.9086
- eval_samples_per_second: 10.186
- eval_steps_per_second: 1.426
- epoch: 27.0
- step: 702
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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: 50
- num_epochs: 30
- mixed_precision_training: Native AMP
Framework versions
- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Base model
facebook/nllb-200-distilled-600M