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npi-sim-ques-lstm-2
CLMBR/npi-sim-ques-lstm-2
npi-sim-ques-lstm-2 is a machine learning model from CLMBR. 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 transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- rnn
- Access
- Public
- Created
- Jan 19, 2024
- Updated
- Jan 26, 2024
- SHA
- 96a3748b
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. -->
npi-sim-ques-lstm-2
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.9709
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: 32
- eval_batch_size: 32
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3052726
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.806 | 0.03 | 76320 | 4.7637 |
| 4.5179 | 1.03 | 152640 | 4.4810 |
| 4.3786 | 0.03 | 228960 | 4.3461 |
| 4.2891 | 1.03 | 305280 | 4.2627 |
| 4.2257 | 0.03 | 381600 | 4.2058 |
| 4.1775 | 1.03 | 457920 | 4.1652 |
| 4.1404 | 0.03 | 534240 | 4.1341 |
| 4.1056 | 1.03 | 610560 | 4.1093 |
| 4.0786 | 0.03 | 686880 | 4.0893 |
| 4.0545 | 1.03 | 763200 | 4.0733 |
| 4.0336 | 0.03 | 839520 | 4.0604 |
| 4.0179 | 1.03 | 915840 | 4.0494 |
| 3.9965 | 0.03 | 992160 | 4.0393 |
| 3.979 | 1.03 | 1068480 | 4.0310 |
| 3.9668 | 0.03 | 1144800 | 4.0236 |
| 3.9616 | 1.03 | 1221120 | 4.0182 |
| 3.9493 | 0.03 | 1297440 | 4.0129 |
| 3.9384 | 1.03 | 1373760 | 4.0078 |
| 3.9327 | 0.03 | 1450080 | 4.0035 |
| 3.924 | 1.03 | 1526400 | 4.0002 |
| 3.9181 | 0.03 | 1602720 | 3.9966 |
| 3.9132 | 1.03 | 1679040 | 3.9935 |
| 3.9076 | 0.03 | 1755360 | 3.9911 |
| 3.9016 | 1.03 | 1831680 | 3.9887 |
| 3.8946 | 0.03 | 1908000 | 3.9864 |
| 3.8885 | 1.03 | 1984320 | 3.9848 |
| 3.8822 | 0.03 | 2060640 | 3.9832 |
| 3.8791 | 0.03 | 2136960 | 3.9816 |
| 3.8702 | 1.03 | 2213280 | 3.9803 |
| 3.8615 | 0.03 | 2289600 | 3.9788 |
| 3.8593 | 0.03 | 2365920 | 3.9775 |
| 3.8616 | 1.03 | 2442240 | 3.9767 |
| 3.8572 | 0.03 | 2518560 | 3.9757 |
| 3.8525 | 1.03 | 2594880 | 3.9745 |
| 3.8549 | 0.03 | 2671200 | 3.9739 |
| 3.8517 | 1.03 | 2747520 | 3.9732 |
| 3.85 | 0.03 | 2823840 | 3.9724 |
| 3.8506 | 1.03 | 2900160 | 3.9718 |
| 3.8519 | 0.03 | 2976480 | 3.9712 |
| 3.8467 | 1.02 | 3052726 | 3.9709 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
- Library
- transformers
- Type
- rnn
- Created
- Jan 19, 2024
- Updated
- Jan 26, 2024