File size: 3,042 Bytes
8693314 29d9803 8693314 29d9803 8693314 29d9803 8693314 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_prototypical_only
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-aann-prototypical_only-3e-4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual_babylm_prototypical_only
type: kanishka/counterfactual_babylm_prototypical_only
metrics:
- name: Accuracy
type: accuracy
value: 0.40804219428728983
---
<!-- 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. -->
# smolm-autoreg-bpe-counterfactual-babylm-aann-prototypical_only-3e-4
This model was trained from scratch on the kanishka/counterfactual_babylm_prototypical_only dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4082
- Accuracy: 0.4080
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7341 | 1.0 | 18593 | 3.8768 | 0.3468 |
| 3.4322 | 2.0 | 37186 | 3.6158 | 0.3751 |
| 3.2902 | 3.0 | 55779 | 3.4817 | 0.3883 |
| 3.21 | 4.0 | 74372 | 3.4286 | 0.3960 |
| 3.1498 | 5.0 | 92965 | 3.4151 | 0.3978 |
| 3.0981 | 6.0 | 111558 | 3.3790 | 0.4022 |
| 3.0651 | 7.0 | 130151 | 3.3750 | 0.4034 |
| 3.0292 | 8.0 | 148744 | 3.3879 | 0.4041 |
| 3.0031 | 9.0 | 167337 | 3.3773 | 0.4046 |
| 2.9713 | 10.0 | 185930 | 3.3769 | 0.4061 |
| 2.9529 | 11.0 | 204523 | 3.3778 | 0.4069 |
| 2.9286 | 12.0 | 223116 | 3.3612 | 0.4077 |
| 2.9065 | 13.0 | 241709 | 3.3686 | 0.4073 |
| 2.8837 | 14.0 | 260302 | 3.3861 | 0.4078 |
| 2.8621 | 15.0 | 278895 | 3.3851 | 0.4077 |
| 2.8487 | 16.0 | 297488 | 3.3876 | 0.4080 |
| 2.8243 | 17.0 | 316081 | 3.3908 | 0.4081 |
| 2.8078 | 18.0 | 334674 | 3.3952 | 0.4082 |
| 2.7887 | 19.0 | 353267 | 3.4020 | 0.4082 |
| 2.7716 | 20.0 | 371860 | 3.4082 | 0.4080 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|