Instructions to use sajjad55/wsdbanglat5_2e4_mBART50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sajjad55/wsdbanglat5_2e4_mBART50 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("sajjad55/wsdbanglat5_2e4_mBART50") model = AutoModelForMultimodalLM.from_pretrained("sajjad55/wsdbanglat5_2e4_mBART50") - Notebooks
- Google Colab
- Kaggle
wsdbanglat5_2e4_mBART50
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: 0.0121
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0984 | 1.0 | 1481 | 0.0783 |
| 0.0599 | 2.0 | 2962 | 0.0579 |
| 0.0512 | 3.0 | 4443 | 0.0502 |
| 0.041 | 4.0 | 5924 | 0.0422 |
| 0.0372 | 5.0 | 7405 | 0.0408 |
| 0.0292 | 6.0 | 8886 | 0.0277 |
| 0.021 | 7.0 | 10367 | 0.0216 |
| 0.0155 | 8.0 | 11848 | 0.0174 |
| 0.0109 | 9.0 | 13329 | 0.0144 |
| 0.0089 | 10.0 | 14810 | 0.0121 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Base model
facebook/mbart-large-50