transformers safetensors opt text-generation generated_from_trainer dataset:kanishka/counterfactual_babylm_aann_excess_adj_removal model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_excess_adj_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-adj_num_freq_balanced-1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_aann_excess_adj_removal
      type: kanishka/counterfactual_babylm_aann_excess_adj_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.40559690392441244
---

<!-- 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-adj_num_freq_balanced-1e-3

This model was trained from scratch on the kanishka/counterfactual_babylm_aann_excess_adj_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4904
- Accuracy: 0.4056

## 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: 16
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.5233        | 1.0   | 37258  | 3.7374          | 0.3658   |
| 3.3131        | 2.0   | 74516  | 3.5591          | 0.3855   |
| 3.2155        | 3.0   | 111774 | 3.4638          | 0.3939   |
| 3.1471        | 4.0   | 149032 | 3.4658          | 0.3964   |
| 3.0992        | 5.0   | 186290 | 3.4396          | 0.3985   |
| 3.0583        | 6.0   | 223548 | 3.3943          | 0.4039   |
| 3.0231        | 7.0   | 260806 | 3.3958          | 0.4046   |
| 2.9862        | 8.0   | 298064 | 3.4018          | 0.4053   |
| 2.9604        | 9.0   | 335322 | 3.3988          | 0.4071   |
| 2.935         | 10.0  | 372580 | 3.3970          | 0.4081   |
| 2.9131        | 11.0  | 409838 | 3.3988          | 0.4076   |
| 2.8857        | 12.0  | 447096 | 3.4045          | 0.4076   |
| 2.8613        | 13.0  | 484354 | 3.4254          | 0.4063   |
| 2.8427        | 14.0  | 521612 | 3.4185          | 0.4071   |
| 2.8172        | 15.0  | 558870 | 3.4428          | 0.4072   |
| 2.8031        | 16.0  | 596128 | 3.4478          | 0.4060   |
| 2.7782        | 17.0  | 633386 | 3.4540          | 0.4064   |
| 2.7535        | 18.0  | 670644 | 3.4752          | 0.4059   |
| 2.7371        | 19.0  | 707902 | 3.4821          | 0.4059   |
| 2.7238        | 20.0  | 745160 | 3.4904          | 0.4056   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
