transformers safetensors opt text-generation generated_from_trainer dataset:kanishka/counterfactual_babylm_aann_all_det_removal model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_all_det_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-all_det_removal-1e-4
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_aann_all_det_removal
      type: kanishka/counterfactual_babylm_aann_all_det_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4057243549615228
---

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# smolm-autoreg-bpe-counterfactual-babylm-all_det_removal-1e-4

This model was trained from scratch on the kanishka/counterfactual_babylm_aann_all_det_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4411
- Accuracy: 0.4057

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.05          | 1.0   | 18595  | 4.2668          | 0.3100   |
| 3.5632        | 2.0   | 37190  | 3.7405          | 0.3611   |
| 3.3888        | 3.0   | 55785  | 3.5859          | 0.3783   |
| 3.2909        | 4.0   | 74380  | 3.5018          | 0.3879   |
| 3.2237        | 5.0   | 92975  | 3.4853          | 0.3925   |
| 3.1738        | 6.0   | 111570 | 3.4621          | 0.3954   |
| 3.1329        | 7.0   | 130165 | 3.4464          | 0.3983   |
| 3.0969        | 8.0   | 148760 | 3.4159          | 0.3998   |
| 3.0649        | 9.0   | 167355 | 3.4220          | 0.4017   |
| 3.0391        | 10.0  | 185950 | 3.4191          | 0.4024   |
| 3.0153        | 11.0  | 204545 | 3.4059          | 0.4036   |
| 2.9895        | 12.0  | 223140 | 3.4205          | 0.4036   |
| 2.9701        | 13.0  | 241735 | 3.4289          | 0.4042   |
| 2.9496        | 14.0  | 260330 | 3.4117          | 0.4046   |
| 2.9306        | 15.0  | 278925 | 3.4235          | 0.4050   |
| 2.9122        | 16.0  | 297520 | 3.4334          | 0.4049   |
| 2.8976        | 17.0  | 316115 | 3.4323          | 0.4053   |
| 2.8791        | 18.0  | 334710 | 3.4307          | 0.4059   |
| 2.8622        | 19.0  | 353305 | 3.4373          | 0.4057   |
| 2.8469        | 20.0  | 371900 | 3.4411          | 0.4057   |


### Framework versions

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