File size: 3,028 Bytes
ed8a40a e3d39f7 ed8a40a e3d39f7 ed8a40a e3d39f7 ed8a40a |
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-pipps_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-pipps_removal-seed_1024-1e-3
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual-babylm-pipps_removal
type: kanishka/counterfactual-babylm-pipps_removal
metrics:
- name: Accuracy
type: accuracy
value: 0.4102895476662934
---
<!-- 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-pipps_removal-seed_1024-1e-3
This model was trained from scratch on the kanishka/counterfactual-babylm-pipps_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4120
- Accuracy: 0.4103
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 64
- seed: 1024
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.5992 | 1.0 | 18593 | 3.7873 | 0.3590 |
| 3.3841 | 2.0 | 37186 | 3.5957 | 0.3798 |
| 3.2517 | 3.0 | 55779 | 3.4368 | 0.3935 |
| 3.1729 | 4.0 | 74372 | 3.4158 | 0.3977 |
| 3.1228 | 5.0 | 92965 | 3.4132 | 0.4017 |
| 3.074 | 6.0 | 111558 | 3.3903 | 0.4035 |
| 3.0396 | 7.0 | 130151 | 3.3731 | 0.4067 |
| 3.0136 | 8.0 | 148744 | 3.3697 | 0.4065 |
| 2.9841 | 9.0 | 167337 | 3.3754 | 0.4067 |
| 2.9561 | 10.0 | 185930 | 3.3766 | 0.4088 |
| 2.9356 | 11.0 | 204523 | 3.3834 | 0.4089 |
| 2.9099 | 12.0 | 223116 | 3.3625 | 0.4105 |
| 2.8924 | 13.0 | 241709 | 3.3680 | 0.4097 |
| 2.8738 | 14.0 | 260302 | 3.3766 | 0.4103 |
| 2.8485 | 15.0 | 278895 | 3.3746 | 0.4108 |
| 2.834 | 16.0 | 297488 | 3.3823 | 0.4107 |
| 2.8108 | 17.0 | 316081 | 3.3894 | 0.4108 |
| 2.7936 | 18.0 | 334674 | 3.4001 | 0.4101 |
| 2.7783 | 19.0 | 353267 | 3.4030 | 0.4107 |
| 2.755 | 20.0 | 371860 | 3.4120 | 0.4103 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|