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Legal_mbart_large_50_finetuned_ParaData
arpansapkota/Legal_mbart_large_50_finetuned_ParaData
Legal_mbart_large_50_finetuned_ParaData is a machine learning model from arpansapkota. 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
- Mar 27, 2025
- Updated
- Mar 28, 2025
- SHA
- be41eac5
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. -->
Legal_mbart_large_50_finetuned_ParaData
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: 0.4254
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_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: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.0108 | 0.8840 | 200 | 2.1923 |
| 0.5188 | 1.7646 | 400 | 0.4505 |
| 0.3935 | 2.6453 | 600 | 0.4175 |
| 0.3158 | 3.5260 | 800 | 0.4197 |
| 0.2639 | 4.4066 | 1000 | 0.4254 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.5.1+cu121
- Datasets 3.5.0
- Tokenizers 0.21.0
- Library
- transformers
- Type
- mbart
- License
- mit
- Created
- Mar 27, 2025
- Updated
- Mar 28, 2025