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# rule_learning_margin_1mm

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the enoriega/odinsynth_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3806
- Margin Accuracy: 0.8239

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2000
- total_train_batch_size: 8000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Margin Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|
| 0.6482        | 0.16  | 20   | 0.6494          | 0.7263          |
| 0.5151        | 0.32  | 40   | 0.5088          | 0.7792          |
| 0.4822        | 0.48  | 60   | 0.4429          | 0.8045          |
| 0.4472        | 0.64  | 80   | 0.4265          | 0.8107          |
| 0.4352        | 0.8   | 100  | 0.4155          | 0.8132          |
| 0.4335        | 0.96  | 120  | 0.4128          | 0.8116          |
| 0.4113        | 1.12  | 140  | 0.4119          | 0.8142          |
| 0.4186        | 1.28  | 160  | 0.4075          | 0.8120          |
| 0.42          | 1.44  | 180  | 0.4072          | 0.8123          |
| 0.4175        | 1.6   | 200  | 0.4080          | 0.8130          |
| 0.4097        | 1.76  | 220  | 0.4031          | 0.8128          |
| 0.397         | 1.92  | 240  | 0.4004          | 0.8130          |
| 0.4115        | 2.08  | 260  | 0.3979          | 0.8136          |
| 0.4108        | 2.24  | 280  | 0.3940          | 0.8167          |
| 0.4125        | 2.4   | 300  | 0.3879          | 0.8218          |
| 0.4117        | 2.56  | 320  | 0.3848          | 0.8217          |
| 0.3967        | 2.72  | 340  | 0.3818          | 0.8231          |
| 0.3947        | 2.88  | 360  | 0.3813          | 0.8240          |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["enoriega/odinsynth_dataset"], "model-index": [{"name": "rule_learning_margin_1mm", "results": []}]}
transformers pytorch tensorboard bert generated_from_trainer dataset:enoriega/odinsynth_dataset license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #license-apache-2.0 #endpoints_compatible #region-us 

rule\_learning\_margin\_1mm
===========================


This model is a fine-tuned version of bert-base-uncased on the enoriega/odinsynth\_dataset dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3806
* Margin Accuracy: 0.8239


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: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2000
* total\_train\_batch\_size: 8000
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0
* Datasets 2.2.1
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2000
* total\_train\_batch\_size: 8000
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0
* Datasets 2.2.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2000
* total\_train\_batch\_size: 8000
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0
* Datasets 2.2.1
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2000
* total\_train\_batch\_size: 8000
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0
* Datasets 2.2.1
* Tokenizers 0.12.1