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core-350
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| arc_challenge | 0 | acc | 0.2048 | ± | 0.0118 |
| acc_norm | 0.2509 | ± | 0.0127 | ||
| arc_easy | 0 | acc | 0.4247 | ± | 0.0101 |
| acc_norm | 0.3965 | ± | 0.0100 | ||
| boolq | 1 | acc | 0.5468 | ± | 0.0087 |
| hellaswag | 0 | acc | 0.2844 | ± | 0.0045 |
| acc_norm | 0.3031 | ± | 0.0046 | ||
| openbookqa | 0 | acc | 0.1560 | ± | 0.0162 |
| acc_norm | 0.2660 | ± | 0.0198 | ||
| piqa | 0 | acc | 0.5854 | ± | 0.0115 |
| acc_norm | 0.5762 | ± | 0.0115 | ||
| winogrande | 0 | acc | 0.4909 | ± | 0.0141 |
This model is a fine-tuned version of ./core-350 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8128
- Accuracy: 0.8237
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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 10.0
Training results
Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
