Downloads · 30 days
17
16% of all-time downloads
nllb-200-1.3B-ICFOSS-Malayalam_English_Translation1.3b
ArunIcfoss/nllb-200-1.3B-ICFOSS-Malayalam_English_Translation1.3b
nllb-200-1.3B-ICFOSS-Malayalam_English_Translation1.3b is a machine learning model from ArunIcfoss. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as cc-by-nc-4.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
106
Public
Repo size
408 MB
Likes
2
Public
Repo makeup
Click a slice to open those files.
.json17.4 MB · 64%
At a glance
- Library
- peft
- License
- cc-by-nc-4.0
- Access
- Public
- Created
- May 20, 2024
- Updated
- May 22, 2024
- SHA
- 7cbb3cc3
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
nllb-200-1.3B-ICFOSS-Malayalam_English_Translation1.3b
This model is a fine-tuned version of facebook/nllb-200-1.3B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0536
- Bleu: 36.7256
- Rouge: {'rouge1': 0.6977825292445439, 'rouge2': 0.47317224666360513, 'rougeL': 0.6369586014923634, 'rougeLsum': 0.6367120144580565}
- Chrf: {'score': 63.88643397225133, 'char_order': 6, 'word_order': 0, 'beta': 2}
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge | Chrf |
|---|---|---|---|---|---|---|
| 1.1683 | 1.0 | 5750 | 1.0774 | 35.9761 | {'rouge1': 0.6937855960659589, 'rouge2': 0.466938063654629, 'rougeL': 0.6325990208208303, 'rougeLsum': 0.6323899971616622} | {'score': 63.363704282940446, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 1.1177 | 2.0 | 11500 | 1.0617 | 36.3486 | {'rouge1': 0.6957984629345982, 'rouge2': 0.47067647725021045, 'rougeL': 0.6351678391451753, 'rougeLsum': 0.6350175761315434} | {'score': 63.657728669261445, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 1.102 | 3.0 | 17250 | 1.0559 | 36.7216 | {'rouge1': 0.6970801919668868, 'rouge2': 0.47279660574601357, 'rougeL': 0.6364385448189633, 'rougeLsum': 0.6362592345657716} | {'score': 63.89202343434442, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 1.0967 | 4.0 | 23000 | 1.0545 | 36.7450 | {'rouge1': 0.6977900451765099, 'rouge2': 0.4734910607221403, 'rougeL': 0.6373405033951935, 'rougeLsum': 0.6371420919202282} | {'score': 63.918132836888965, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 1.0935 | 5.0 | 28750 | 1.0538 | 36.7038 | {'rouge1': 0.6978511315129863, 'rouge2': 0.4733012047244315, 'rougeL': 0.6371351829239855, 'rougeLsum': 0.6369801889854168} | {'score': 63.87115369473548, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 1.0928 | 6.0 | 34500 | 1.0536 | 36.7485 | {'rouge1': 0.6977169592049554, 'rouge2': 0.4734304167965041, 'rougeL': 0.636966108177003, 'rougeLsum': 0.6367749449397957} | {'score': 63.894445637643784, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 1.0918 | 7.0 | 40250 | 1.0536 | 36.7256 | {'rouge1': 0.6977825292445439, 'rouge2': 0.47317224666360513, 'rougeL': 0.6369586014923634, 'rougeLsum': 0.6367120144580565} | {'score': 63.88643397225133, 'char_order': 6, 'word_order': 0, 'beta': 2} |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
- Library
- peft
- License
- cc-by-nc-4.0
- Created
- May 20, 2024
- Updated
- May 22, 2024