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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- enoriega/odinsynth_sequence_dataset
metrics:
- accuracy
model-index:
- name: odinsynth_encoder_decoder_native_hf_test_2
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: enoriega/odinsynth_sequence_dataset synthetic_surface
type: enoriega/odinsynth_sequence_dataset
config: synthetic_surface
split: validation
args: synthetic_surface
metrics:
- name: Accuracy
type: accuracy
value: 0.9343159108876246
---
<!-- 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. -->
# odinsynth_encoder_decoder_native_hf_test_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the enoriega/odinsynth_sequence_dataset synthetic_surface dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0771
- Accuracy: 0.9343
## 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: 5e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 200
- total_train_batch_size: 600
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1612 | 0.67 | 60 | 0.1145 | 0.9376 |
| 0.0666 | 1.34 | 120 | 0.0628 | 0.9356 |
| 0.0599 | 2.01 | 180 | 0.0611 | 0.9355 |
| 0.0563 | 2.68 | 240 | 0.0631 | 0.9352 |
| 0.0512 | 3.35 | 300 | 0.0630 | 0.9347 |
| 0.0472 | 4.02 | 360 | 0.0638 | 0.9338 |
| 0.0438 | 4.69 | 420 | 0.0655 | 0.9339 |
| 0.0405 | 5.36 | 480 | 0.0660 | 0.9345 |
| 0.0378 | 6.03 | 540 | 0.0666 | 0.9342 |
| 0.0344 | 6.69 | 600 | 0.0669 | 0.9343 |
| 0.0323 | 7.36 | 660 | 0.0678 | 0.9344 |
| 0.0307 | 8.03 | 720 | 0.0694 | 0.9343 |
| 0.0294 | 8.7 | 780 | 0.0706 | 0.9345 |
| 0.0286 | 9.37 | 840 | 0.0725 | 0.9342 |
| 0.0275 | 10.04 | 900 | 0.0727 | 0.9343 |
| 0.0282 | 10.71 | 960 | 0.0732 | 0.9342 |
| 0.0264 | 11.38 | 1020 | 0.0735 | 0.9343 |
| 0.026 | 12.05 | 1080 | 0.0750 | 0.9342 |
| 0.0254 | 12.72 | 1140 | 0.0753 | 0.9343 |
| 0.0244 | 13.39 | 1200 | 0.0746 | 0.9344 |
| 0.0242 | 14.06 | 1260 | 0.0752 | 0.9343 |
| 0.024 | 14.73 | 1320 | 0.0758 | 0.9342 |
| 0.0239 | 15.4 | 1380 | 0.0764 | 0.9343 |
| 0.0234 | 16.07 | 1440 | 0.0763 | 0.9343 |
| 0.0231 | 16.74 | 1500 | 0.0764 | 0.9343 |
| 0.0226 | 17.41 | 1560 | 0.0770 | 0.9343 |
| 0.023 | 18.08 | 1620 | 0.0770 | 0.9343 |
| 0.0227 | 18.74 | 1680 | 0.0771 | 0.9343 |
| 0.0221 | 19.41 | 1740 | 0.0771 | 0.9343 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.11.0
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