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m2m_100
text2text-generation
Generated from Trainer
Instructions to use Norphel/nllb_norphel_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Norphel/nllb_norphel_v1 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="Norphel/nllb_norphel_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Norphel/nllb_norphel_v1") model = AutoModelForSeq2SeqLM.from_pretrained("Norphel/nllb_norphel_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
nllb_norphel_v1
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: 1.7575
- sacredBleu: 9.299410699309234
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 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: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.0176 | 1.0 | 1124 | 1.8654 |
| 1.6219 | 2.0 | 2248 | 1.7696 |
| 1.3945 | 3.0 | 3372 | 1.7575 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
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Model tree for Norphel/nllb_norphel_v1
Base model
facebook/nllb-200-distilled-600M