File size: 3,068 Bytes
556fcf5 bc369b4 556fcf5 bc369b4 556fcf5 bc369b4 556fcf5 7e8a02e 556fcf5 7e8a02e 556fcf5 7e8a02e 556fcf5 |
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-random_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-pipps-random_removal-seed_211-1e-3
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual-babylm-pipps-random_removal
type: kanishka/counterfactual-babylm-pipps-random_removal
metrics:
- name: Accuracy
type: accuracy
value: 0.4112973955375624
---
<!-- 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-random_removal-seed_211-1e-3
This model was trained from scratch on the kanishka/counterfactual-babylm-pipps-random_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3976
- Accuracy: 0.4113
## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6046 | 1.0 | 18592 | 3.7833 | 0.3594 |
| 3.3837 | 2.0 | 37184 | 3.5854 | 0.3805 |
| 3.26 | 3.0 | 55776 | 3.4488 | 0.3931 |
| 3.1824 | 4.0 | 74368 | 3.4153 | 0.3986 |
| 3.1238 | 5.0 | 92960 | 3.3853 | 0.4028 |
| 3.0837 | 6.0 | 111552 | 3.3512 | 0.4060 |
| 3.0442 | 7.0 | 130144 | 3.3564 | 0.4065 |
| 3.0168 | 8.0 | 148736 | 3.3438 | 0.4083 |
| 2.9792 | 9.0 | 167328 | 3.3495 | 0.4090 |
| 2.9607 | 10.0 | 185920 | 3.3579 | 0.4091 |
| 2.9363 | 11.0 | 204512 | 3.3420 | 0.4116 |
| 2.9148 | 12.0 | 223104 | 3.3631 | 0.4106 |
| 2.893 | 13.0 | 241696 | 3.3609 | 0.4106 |
| 2.8729 | 14.0 | 260288 | 3.3806 | 0.4101 |
| 2.8543 | 15.0 | 278880 | 3.3685 | 0.4112 |
| 2.8352 | 16.0 | 297472 | 3.3734 | 0.4119 |
| 2.8131 | 17.0 | 316064 | 3.3759 | 0.4115 |
| 2.7949 | 18.0 | 334656 | 3.3842 | 0.4111 |
| 2.7756 | 19.0 | 353248 | 3.3893 | 0.4115 |
| 2.7607 | 20.0 | 371840 | 3.3976 | 0.4113 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|