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# MiniLMv2-L6-H768-sst2

This model is a fine-tuned version of [nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2013
- Accuracy: 0.9427

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: sagemaker_data_parallel
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4734        | 1.0   | 264  | 0.2046          | 0.9243   |
| 0.2399        | 2.0   | 528  | 0.1912          | 0.9346   |
| 0.1791        | 3.0   | 792  | 0.1943          | 0.9335   |
| 0.1442        | 4.0   | 1056 | 0.2103          | 0.9369   |
| 0.1217        | 5.0   | 1320 | 0.2013          | 0.9427   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6

{"tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L6-H768-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": [{"type": "accuracy", "value": 0.9426605504587156, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer dataset:glue model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us 

MiniLMv2-L6-H768-sst2
=====================


This model is a fine-tuned version of nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large on the glue dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2013
* Accuracy: 0.9427


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: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* distributed\_type: sagemaker\_data\_parallel
* num\_devices: 8
* total\_train\_batch\_size: 256
* total\_eval\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.2+cu113
* Datasets 1.18.4
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* distributed\_type: sagemaker\_data\_parallel
* num\_devices: 8
* total\_train\_batch\_size: 256
* total\_eval\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.2+cu113
* Datasets 1.18.4
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* distributed\_type: sagemaker\_data\_parallel
* num\_devices: 8
* total\_train\_batch\_size: 256
* total\_eval\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.2+cu113
* Datasets 1.18.4
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* distributed\_type: sagemaker\_data\_parallel
* num\_devices: 8
* total\_train\_batch\_size: 256
* total\_eval\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.2+cu113
* Datasets 1.18.4
* Tokenizers 0.11.6