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PRIMERA-lora-finetuned
toanduz/PRIMERA-lora-finetuned
PRIMERA-lora-finetuned is a machine learning model from toanduz. 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 peft.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- Access
- Public
- Created
- Jul 27, 2024
- Updated
- Jul 27, 2024
- SHA
- d65b6f16
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. -->
PRIMERA-lora-finetuned
This model is a fine-tuned version of allenai/PRIMERA on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4326
- Rouge1: 13.8331
- Rouge2: 6.379
- Rougel: 11.3582
- Rougelsum: 13.0901
- Gen Len: 19.0217
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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.9399 | 1.0 | 4725 | 1.6257 | 13.1779 | 5.0715 | 10.6386 | 11.9435 | 19.3478 |
| 1.8124 | 2.0 | 9450 | 1.5723 | 12.8712 | 5.1519 | 10.5072 | 11.9192 | 19.3478 |
| 1.7698 | 3.0 | 14175 | 1.5261 | 13.6339 | 5.5927 | 10.9273 | 12.3707 | 19.3478 |
| 1.7171 | 4.0 | 18900 | 1.4990 | 13.0984 | 6.164 | 10.9698 | 12.1863 | 19.6739 |
| 1.7096 | 5.0 | 23625 | 1.4832 | 12.7164 | 5.4214 | 10.3325 | 11.8134 | 19.3478 |
| 1.6889 | 6.0 | 28350 | 1.4743 | 13.0677 | 4.9162 | 10.3945 | 11.9299 | 19.3478 |
| 1.6667 | 7.0 | 33075 | 1.4652 | 13.8986 | 6.1753 | 10.9896 | 12.8072 | 19.3478 |
| 1.6836 | 8.0 | 37800 | 1.4573 | 13.0179 | 5.5771 | 10.4498 | 12.2198 | 19.0217 |
| 1.6611 | 9.0 | 42525 | 1.4523 | 12.8773 | 5.3502 | 10.294 | 11.9712 | 19.0217 |
| 1.6842 | 10.0 | 47250 | 1.4503 | 13.1982 | 5.0089 | 10.4547 | 12.2554 | 19.0217 |
| 1.6373 | 11.0 | 51975 | 1.4432 | 13.0444 | 5.533 | 10.3895 | 12.3086 | 19.0217 |
| 1.6882 | 12.0 | 56700 | 1.4408 | 13.7092 | 5.9873 | 11.1084 | 12.8159 | 19.3478 |
| 1.6707 | 13.0 | 61425 | 1.4395 | 13.6103 | 6.2231 | 10.7643 | 12.7343 | 19.0217 |
| 1.677 | 14.0 | 66150 | 1.4366 | 13.5232 | 6.3114 | 10.9432 | 12.7234 | 19.0217 |
| 1.6825 | 15.0 | 70875 | 1.4347 | 13.7907 | 6.1456 | 11.0682 | 12.803 | 19.0217 |
| 1.6516 | 16.0 | 75600 | 1.4326 | 13.8331 | 6.379 | 11.3582 | 13.0901 | 19.0217 |
Framework versions
- PEFT 0.12.0
- Transformers 4.43.3
- Pytorch 2.2.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Library
- peft
- Created
- Jul 27, 2024
- Updated
- Jul 27, 2024