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# rule_learning_1mm_many_negatives_spanpred_margin_avg

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: 0.2421
- Margin Accuracy: 0.8897

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|
| 0.3867        | 0.16  | 20   | 0.4023          | 0.8187          |
| 0.3506        | 0.32  | 40   | 0.3381          | 0.8523          |
| 0.3195        | 0.48  | 60   | 0.3096          | 0.8613          |
| 0.3052        | 0.64  | 80   | 0.2957          | 0.8640          |
| 0.2859        | 0.8   | 100  | 0.2922          | 0.8679          |
| 0.297         | 0.96  | 120  | 0.2871          | 0.8688          |
| 0.2717        | 1.12  | 140  | 0.2761          | 0.8732          |
| 0.2671        | 1.28  | 160  | 0.2751          | 0.8743          |
| 0.2677        | 1.44  | 180  | 0.2678          | 0.8757          |
| 0.2693        | 1.6   | 200  | 0.2627          | 0.8771          |
| 0.2675        | 1.76  | 220  | 0.2573          | 0.8813          |
| 0.2732        | 1.92  | 240  | 0.2546          | 0.8858          |
| 0.246         | 2.08  | 260  | 0.2478          | 0.8869          |
| 0.2355        | 2.24  | 280  | 0.2463          | 0.8871          |
| 0.2528        | 2.4   | 300  | 0.2449          | 0.8886          |
| 0.2512        | 2.56  | 320  | 0.2443          | 0.8892          |
| 0.2527        | 2.72  | 340  | 0.2441          | 0.8893          |
| 0.2346        | 2.88  | 360  | 0.2424          | 0.8895          |


### 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_1mm_many_negatives_spanpred_margin_avg", "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\_1mm\_many\_negatives\_spanpred\_margin\_avg
===========================================================


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: 0.2421
* Margin Accuracy: 0.8897


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