llama-160m-mnli
This model is a fine-tuned version of JackFram/llama-160m on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0860
- Accuracy: 0.4032
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: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.1021 | 1.0 | 3068 | 1.0996 | 0.3860 |
| 1.0902 | 2.0 | 6136 | 1.0914 | 0.3947 |
| 1.0871 | 3.0 | 9204 | 1.0878 | 0.3980 |
| 1.0871 | 4.0 | 12272 | 1.0860 | 0.4032 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.13.3
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Model tree for Cheng98/llama-160m-mnli
Base model
JackFram/llama-160m