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p-tuning-roberta-large-with-mrpc
aisuko/p-tuning-roberta-large-with-mrpc
p-tuning-roberta-large-with-mrpc is a machine learning model from aisuko. 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. 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
- peft
- License
- mit
- Access
- Public
- Created
- Jan 11, 2024
- Updated
- Jan 11, 2024
- SHA
- c2c20557
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. -->
p-tuning-roberta-large-with-mrpc
This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5925
- Accuracy: 0.6957
- F1: 0.7992
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: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 58 | 0.6199 | 0.6754 | 0.8003 |
| No log | 2.0 | 116 | 0.6082 | 0.6939 | 0.8066 |
| No log | 3.0 | 174 | 0.6217 | 0.6725 | 0.8013 |
| No log | 4.0 | 232 | 0.6317 | 0.6667 | 0.7993 |
| No log | 5.0 | 290 | 0.6402 | 0.6499 | 0.7703 |
| No log | 6.0 | 348 | 0.6219 | 0.6678 | 0.7994 |
| No log | 7.0 | 406 | 0.6287 | 0.6684 | 0.7556 |
| No log | 8.0 | 464 | 0.5914 | 0.6991 | 0.8051 |
| 0.6347 | 9.0 | 522 | 0.5826 | 0.6875 | 0.8022 |
| 0.6347 | 10.0 | 580 | 0.6129 | 0.6875 | 0.7764 |
| 0.6347 | 11.0 | 638 | 0.5942 | 0.6922 | 0.8036 |
| 0.6347 | 12.0 | 696 | 0.5975 | 0.6887 | 0.7822 |
| 0.6347 | 13.0 | 754 | 0.5943 | 0.6899 | 0.7963 |
| 0.6347 | 14.0 | 812 | 0.5863 | 0.6968 | 0.8039 |
| 0.6347 | 15.0 | 870 | 0.5880 | 0.6945 | 0.7998 |
| 0.6347 | 16.0 | 928 | 0.5967 | 0.6858 | 0.7982 |
| 0.6347 | 17.0 | 986 | 0.5951 | 0.6968 | 0.8020 |
| 0.5717 | 18.0 | 1044 | 0.5931 | 0.7043 | 0.7981 |
| 0.5717 | 19.0 | 1102 | 0.5936 | 0.6974 | 0.8006 |
| 0.5717 | 20.0 | 1160 | 0.5925 | 0.6957 | 0.7992 |
Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
- Library
- peft
- License
- mit
- Created
- Jan 11, 2024
- Updated
- Jan 11, 2024