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RuTaskFlow-mBART-T26-122K
damienliccia/RuTaskFlow-mBART-T26-122K
RuTaskFlow-mBART-T26-122K is a machine learning model from damienliccia. 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 transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- mit
- Model type
- mbart
- Access
- Public
- Created
- Dec 4, 2024
- Updated
- Dec 4, 2024
- SHA
- 40bdc760
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. -->
RuTaskFlow-mBART-T26-122K
This model is a fine-tuned version of facebook/mbart-large-50 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3425
- Rouge1: 89.83
- Rouge2: 72.15
- Rougel: 89.02
- Rougelsum: 89.02
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 0.3952 | 1.0 | 6105 | 0.3699 | 88.88 | 71.31 | 88.08 | 88.09 |
| 0.2821 | 2.0 | 12210 | 0.3334 | 89.65 | 71.84 | 88.84 | 88.82 |
| 0.1433 | 3.0 | 18315 | 0.3425 | 89.83 | 72.15 | 89.02 | 89.02 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
- Library
- transformers
- Type
- mbart
- License
- mit
- Created
- Dec 4, 2024
- Updated
- Dec 4, 2024