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# rule_learning_margin_3mm_many_negatives_spanpred_attention

This model is a fine-tuned version of [enoriega/rule_softmatching](https://huggingface.co/enoriega/rule_softmatching) on the enoriega/odinsynth_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2196
- Margin Accuracy: 0.8969

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|
| 0.3149        | 0.16  | 60   | 0.3098          | 0.8608          |
| 0.2754        | 0.32  | 120  | 0.2725          | 0.8733          |
| 0.2619        | 0.48  | 180  | 0.2512          | 0.8872          |
| 0.2378        | 0.64  | 240  | 0.2391          | 0.8925          |
| 0.2451        | 0.8   | 300  | 0.2305          | 0.8943          |
| 0.2357        | 0.96  | 360  | 0.2292          | 0.8949          |
| 0.2335        | 1.12  | 420  | 0.2269          | 0.8952          |
| 0.2403        | 1.28  | 480  | 0.2213          | 0.8957          |
| 0.2302        | 1.44  | 540  | 0.2227          | 0.8963          |
| 0.2353        | 1.6   | 600  | 0.2222          | 0.8961          |
| 0.2271        | 1.76  | 660  | 0.2207          | 0.8964          |
| 0.228         | 1.92  | 720  | 0.2218          | 0.8967          |
| 0.2231        | 2.08  | 780  | 0.2201          | 0.8967          |
| 0.2128        | 2.24  | 840  | 0.2219          | 0.8967          |
| 0.2186        | 2.4   | 900  | 0.2202          | 0.8967          |
| 0.2245        | 2.56  | 960  | 0.2205          | 0.8969          |
| 0.2158        | 2.72  | 1020 | 0.2196          | 0.8969          |
| 0.2106        | 2.88  | 1080 | 0.2192          | 0.8968          |


### Framework versions

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

{"tags": ["generated_from_trainer"], "datasets": ["enoriega/odinsynth_dataset"], "model-index": [{"name": "rule_learning_margin_3mm_many_negatives_spanpred_attention", "results": []}]}
transformers pytorch tensorboard bert generated_from_trainer dataset:enoriega/odinsynth_dataset endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #endpoints_compatible #region-us 

rule\_learning\_margin\_3mm\_many\_negatives\_spanpred\_attention
=================================================================


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


* Loss: 0.2196
* Margin Accuracy: 0.8969


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 #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 #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