File size: 3,092 Bytes
9f4e13e 1c2dcfa 9f4e13e 1c2dcfa 9f4e13e 1c2dcfa 9f4e13e |
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_measure_nps_as_singular_new
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual_babylm_measure_nps_as_singular_new-1e-4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual_babylm_measure_nps_as_singular_new
type: kanishka/counterfactual_babylm_measure_nps_as_singular_new
metrics:
- name: Accuracy
type: accuracy
value: 0.40681131693060796
---
<!-- 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_measure_nps_as_singular_new-1e-4
This model was trained from scratch on the kanishka/counterfactual_babylm_measure_nps_as_singular_new dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4240
- Accuracy: 0.4068
## 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: 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0517 | 1.0 | 18602 | 4.2617 | 0.3086 |
| 3.5614 | 2.0 | 37204 | 3.7325 | 0.3617 |
| 3.3871 | 3.0 | 55806 | 3.5926 | 0.3794 |
| 3.2873 | 4.0 | 74408 | 3.4903 | 0.3889 |
| 3.2166 | 5.0 | 93010 | 3.4705 | 0.3930 |
| 3.1683 | 6.0 | 111612 | 3.4386 | 0.3965 |
| 3.122 | 7.0 | 130214 | 3.4230 | 0.3987 |
| 3.0883 | 8.0 | 148816 | 3.4103 | 0.4020 |
| 3.059 | 9.0 | 167418 | 3.4161 | 0.4022 |
| 3.0294 | 10.0 | 186020 | 3.4004 | 0.4039 |
| 3.0081 | 11.0 | 204622 | 3.4048 | 0.4041 |
| 2.9849 | 12.0 | 223224 | 3.4068 | 0.4046 |
| 2.9618 | 13.0 | 241826 | 3.4127 | 0.4048 |
| 2.9398 | 14.0 | 260428 | 3.4079 | 0.4054 |
| 2.9226 | 15.0 | 279030 | 3.3963 | 0.4065 |
| 2.9009 | 16.0 | 297632 | 3.4036 | 0.4068 |
| 2.8845 | 17.0 | 316234 | 3.4090 | 0.4067 |
| 2.8685 | 18.0 | 334836 | 3.4054 | 0.4071 |
| 2.8513 | 19.0 | 353438 | 3.4187 | 0.4069 |
| 2.8368 | 20.0 | 372040 | 3.4240 | 0.4068 |
### Framework versions
- Transformers 4.38.0
- Pytorch 2.3.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2
|