Instructions to use yousrafourati/nllb_b2b_checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yousrafourati/nllb_b2b_checkpoints with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("yousrafourati/nllb_b2b_checkpoints") model = AutoModelForSeq2SeqLM.from_pretrained("yousrafourati/nllb_b2b_checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
nllb_b2b_checkpoints
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:
- Loss: 2.2663
- Bleu: 10.0040
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 20
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|---|---|---|---|---|
| 5.6683 | 1.0 | 6 | 5.5640 | 4.2746 |
| 5.4244 | 2.0 | 12 | 4.3203 | 5.2153 |
| 4.0256 | 3.0 | 18 | 3.0693 | 8.8737 |
| 2.6780 | 4.0 | 24 | 2.4013 | 10.0040 |
| 1.7783 | 5.0 | 30 | 2.2663 | 10.0040 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for yousrafourati/nllb_b2b_checkpoints
Base model
facebook/nllb-200-distilled-600M