Instructions to use Gaoussin/trainer_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gaoussin/trainer_output with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Gaoussin/trainer_output") model = AutoModelForSeq2SeqLM.from_pretrained("Gaoussin/trainer_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Bamalingua-fr-bm-v5
Browse files
README.md
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base_model: facebook/nllb-200-distilled-600M
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tags:
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- generated_from_trainer
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model-index:
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- name: trainer_output
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results: []
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# trainer_output
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This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on an unknown dataset.
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## Model description
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 6
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### Framework versions
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- Transformers 4.57.1
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base_model: facebook/nllb-200-distilled-600M
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tags:
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- generated_from_trainer
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metrics:
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- bleu
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model-index:
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- name: trainer_output
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results: []
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# trainer_output
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This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.9640
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- Bleu: 18.5861
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## Model description
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 6
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Bleu |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:|
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| 2.392 | 1.0 | 2525 | 2.2508 | 13.6779 |
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| 2.1302 | 2.0 | 5050 | 2.0765 | 17.1351 |
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| 1.9159 | 3.0 | 7575 | 1.9920 | 17.7167 |
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| 1.7718 | 4.0 | 10100 | 1.9664 | 18.6164 |
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| 1.6949 | 5.0 | 12625 | 1.9633 | 18.6201 |
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| 1.6918 | 6.0 | 15150 | 1.9640 | 18.5861 |
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### Framework versions
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- Transformers 4.57.1
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