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qa_kor_math_2
idah4/qa_kor_math_2
qa_kor_math_2 is a machine learning model from idah4. 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. 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
- transformers
- License
- mit
- Model type
- bart
- Access
- Public
- Created
- Apr 15, 2024
- Updated
- Apr 15, 2024
- SHA
- f068b2f5
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. -->
qa_kor_math_2
This model is a fine-tuned version of hyunwoongko/kobart on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1234
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.56 | 100 | 3.2887 |
| No log | 1.13 | 200 | 0.8359 |
| No log | 1.69 | 300 | 0.4944 |
| No log | 2.26 | 400 | 0.3843 |
| 2.4704 | 2.82 | 500 | 0.3349 |
| 2.4704 | 3.39 | 600 | 0.3005 |
| 2.4704 | 3.95 | 700 | 0.2768 |
| 2.4704 | 4.52 | 800 | 0.2641 |
| 2.4704 | 5.08 | 900 | 0.2479 |
| 0.3213 | 5.65 | 1000 | 0.2335 |
| 0.3213 | 6.21 | 1100 | 0.2208 |
| 0.3213 | 6.78 | 1200 | 0.2117 |
| 0.3213 | 7.34 | 1300 | 0.2041 |
| 0.3213 | 7.91 | 1400 | 0.1964 |
| 0.2503 | 8.47 | 1500 | 0.1876 |
| 0.2503 | 9.04 | 1600 | 0.1790 |
| 0.2503 | 9.6 | 1700 | 0.1745 |
| 0.2503 | 10.17 | 1800 | 0.1673 |
| 0.2503 | 10.73 | 1900 | 0.1623 |
| 0.2141 | 11.3 | 2000 | 0.1579 |
| 0.2141 | 11.86 | 2100 | 0.1527 |
| 0.2141 | 12.43 | 2200 | 0.1494 |
| 0.2141 | 12.99 | 2300 | 0.1438 |
| 0.2141 | 13.56 | 2400 | 0.1427 |
| 0.1873 | 14.12 | 2500 | 0.1386 |
| 0.1873 | 14.69 | 2600 | 0.1347 |
| 0.1873 | 15.25 | 2700 | 0.1334 |
| 0.1873 | 15.82 | 2800 | 0.1321 |
| 0.1873 | 16.38 | 2900 | 0.1295 |
| 0.1718 | 16.95 | 3000 | 0.1276 |
| 0.1718 | 17.51 | 3100 | 0.1263 |
| 0.1718 | 18.08 | 3200 | 0.1255 |
| 0.1718 | 18.64 | 3300 | 0.1244 |
| 0.1718 | 19.21 | 3400 | 0.1240 |
| 0.1628 | 19.77 | 3500 | 0.1234 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Library
- transformers
- Type
- bart
- License
- mit
- Created
- Apr 15, 2024
- Updated
- Apr 15, 2024