Instructions to use rosadecsai/led-large-16384-BASE3ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rosadecsai/led-large-16384-BASE3ep with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, MultiTask_LED tokenizer = AutoTokenizer.from_pretrained("rosadecsai/led-large-16384-BASE3ep") model = MultiTask_LED.from_pretrained("rosadecsai/led-large-16384-BASE3ep", device_map="auto") - Notebooks
- Google Colab
- Kaggle
led-large-16384-BASE3ep
This model is a fine-tuned version of allenai/led-large-16384 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.0538
- Rouge1: 36.1878
- Rouge2: 14.1274
- Rougel: 19.6133
- Rougelsum: 35.9116
- Gen Len: 1.0
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.8959 | 1.0 | 1132 | 2.1458 | 40.5722 | 14.0808 | 20.8062 | 39.5319 | 1.0 |
| 2.7074 | 2.0 | 2264 | 2.0610 | 41.3972 | 16.3424 | 19.1462 | 39.8448 | 1.0 |
| 2.5389 | 2.9978 | 3393 | 2.0538 | 36.1878 | 14.1274 | 19.6133 | 35.9116 | 1.0 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.21.4
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