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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
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: 0.9332402379440391
---
<!-- 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
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: 0.0533
- Accuracy: 0.9332
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 6.5753 | 0.67 | 60 | 6.1666 | 0.0150 |
| 2.5262 | 1.34 | 120 | 2.1713 | 0.9345 |
| 0.2343 | 2.01 | 180 | 0.1787 | 0.9346 |
| 0.0713 | 2.68 | 240 | 0.0686 | 0.9330 |
| 0.0631 | 3.35 | 300 | 0.0621 | 0.9334 |
| 0.0603 | 4.02 | 360 | 0.0594 | 0.9332 |
| 0.0589 | 4.69 | 420 | 0.0583 | 0.9334 |
| 0.0579 | 5.36 | 480 | 0.0572 | 0.9336 |
| 0.0575 | 6.03 | 540 | 0.0566 | 0.9333 |
| 0.0561 | 6.69 | 600 | 0.0562 | 0.9333 |
| 0.0559 | 7.36 | 660 | 0.0559 | 0.9332 |
| 0.0551 | 8.03 | 720 | 0.0556 | 0.9332 |
| 0.0548 | 8.7 | 780 | 0.0552 | 0.9333 |
| 0.0546 | 9.37 | 840 | 0.0550 | 0.9333 |
| 0.0539 | 10.04 | 900 | 0.0547 | 0.9331 |
| 0.0546 | 10.71 | 960 | 0.0544 | 0.9332 |
| 0.0538 | 11.38 | 1020 | 0.0543 | 0.9335 |
| 0.0534 | 12.05 | 1080 | 0.0540 | 0.9333 |
| 0.0532 | 12.72 | 1140 | 0.0539 | 0.9334 |
| 0.0525 | 13.39 | 1200 | 0.0538 | 0.9334 |
| 0.0526 | 14.06 | 1260 | 0.0538 | 0.9331 |
| 0.0527 | 14.73 | 1320 | 0.0536 | 0.9331 |
| 0.0529 | 15.4 | 1380 | 0.0536 | 0.9331 |
| 0.0526 | 16.07 | 1440 | 0.0535 | 0.9331 |
| 0.0524 | 16.74 | 1500 | 0.0534 | 0.9333 |
| 0.0516 | 17.41 | 1560 | 0.0534 | 0.9331 |
| 0.0527 | 18.08 | 1620 | 0.0534 | 0.9332 |
| 0.0521 | 18.74 | 1680 | 0.0533 | 0.9332 |
| 0.0519 | 19.41 | 1740 | 0.0533 | 0.9332 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.11.0
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