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pegasus-large-wcep10-extracted-sentences
This model is a fine-tuned version of google/pegasus-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 9.7996
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAFACTOR and the args are: No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 117.2686 | 0.4391 | 100 | 13.3092 |
| 96.2180 | 0.8782 | 200 | 11.2029 |
| 88.2080 | 1.3161 | 300 | 10.6627 |
| 83.8274 | 1.7552 | 400 | 10.3639 |
| 80.7851 | 2.1932 | 500 | 10.2632 |
| 79.6089 | 2.6323 | 600 | 10.0617 |
| 78.2148 | 3.0703 | 700 | 10.0079 |
| 77.3345 | 3.5093 | 800 | 9.9085 |
| 77.3444 | 3.9484 | 900 | 9.8489 |
| 75.8633 | 4.3864 | 1000 | 9.8225 |
| 75.7806 | 4.8255 | 1100 | 9.7996 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2
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Base model
google/pegasus-large
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Awesome075/pegasus-large-wcep10-extracted-sentences") model = AutoModelForSeq2SeqLM.from_pretrained("Awesome075/pegasus-large-wcep10-extracted-sentences", device_map="auto")