Instructions to use contemmcm/467ad1a8dfb24caee6b2c28ddb48047e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use contemmcm/467ad1a8dfb24caee6b2c28ddb48047e with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("contemmcm/467ad1a8dfb24caee6b2c28ddb48047e") model = AutoModelForSeq2SeqLM.from_pretrained("contemmcm/467ad1a8dfb24caee6b2c28ddb48047e") - Notebooks
- Google Colab
- Kaggle
467ad1a8dfb24caee6b2c28ddb48047e
This model is a fine-tuned version of Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-mul on the Helsinki-NLP/opus_books [fr-nl] dataset. It achieves the following results on the evaluation set:
- Loss: 1.3539
- Data Size: 1.0
- Epoch Runtime: 62.3656
- Bleu: 8.1948
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
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Data Size | Epoch Runtime | Bleu |
|---|---|---|---|---|---|---|
| No log | 0 | 0 | 7.1463 | 0 | 5.5583 | 0.0995 |
| No log | 1 | 1000 | 3.5245 | 0.0078 | 5.9371 | 1.1871 |
| No log | 2 | 2000 | 2.7843 | 0.0156 | 6.3704 | 2.5197 |
| No log | 3 | 3000 | 2.3465 | 0.0312 | 7.8593 | 3.8312 |
| 0.0954 | 4 | 4000 | 2.0408 | 0.0625 | 9.5842 | 4.6602 |
| 1.9945 | 5 | 5000 | 1.8079 | 0.125 | 13.0710 | 5.4975 |
| 0.1064 | 6 | 6000 | 1.6156 | 0.25 | 20.5854 | 6.3412 |
| 0.1331 | 7 | 7000 | 1.4631 | 0.5 | 34.6998 | 6.9294 |
| 1.3124 | 8.0 | 8000 | 1.3311 | 1.0 | 63.1764 | 7.6202 |
| 1.1572 | 9.0 | 9000 | 1.2877 | 1.0 | 62.0938 | 7.9482 |
| 1.0234 | 10.0 | 10000 | 1.2728 | 1.0 | 63.1995 | 8.0784 |
| 0.9217 | 11.0 | 11000 | 1.2734 | 1.0 | 64.5127 | 8.0976 |
| 0.8327 | 12.0 | 12000 | 1.2947 | 1.0 | 63.6552 | 8.1607 |
| 0.7572 | 13.0 | 13000 | 1.3226 | 1.0 | 63.9981 | 8.1808 |
| 0.667 | 14.0 | 14000 | 1.3539 | 1.0 | 62.3656 | 8.1948 |
Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu128
- Datasets 4.2.0
- Tokenizers 0.22.1
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support