
<!-- 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

{"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": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "enoriega/odinsynth_sequence_dataset synthetic_surface", "type": "enoriega/odinsynth_sequence_dataset", "config": "synthetic_surface", "split": "validation", "args": "synthetic_surface"}, "metrics": [{"type": "accuracy", "value": 0.9332402379440391, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard encoder-decoder text2text-generation generated_from_trainer dataset:enoriega/odinsynth_sequence_dataset license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-enoriega/odinsynth_sequence_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

odinsynth\_encoder\_decoder\_native\_hf\_test
=============================================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.11.0



### 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 ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-enoriega/odinsynth_sequence_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.11.0