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# MiniLMv2-L12-H384-distilled-finetuned-clinc

This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3058
- Accuracy: 0.9529

## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9908        | 1.0   | 239  | 1.6816          | 0.3910   |
| 1.5212        | 2.0   | 478  | 1.2365          | 0.7697   |
| 1.129         | 3.0   | 717  | 0.9209          | 0.8706   |
| 0.8462        | 4.0   | 956  | 0.6978          | 0.9152   |
| 0.6497        | 5.0   | 1195 | 0.5499          | 0.9342   |
| 0.5124        | 6.0   | 1434 | 0.4447          | 0.9445   |
| 0.4196        | 7.0   | 1673 | 0.3797          | 0.9455   |
| 0.3587        | 8.0   | 1912 | 0.3358          | 0.95     |
| 0.3228        | 9.0   | 2151 | 0.3133          | 0.9513   |
| 0.3052        | 10.0  | 2390 | 0.3058          | 0.9529   |


### Framework versions

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

{"tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L12-H384-distilled-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plus"}, "metrics": [{"type": "accuracy", "value": 0.9529032258064516, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer dataset:clinc_oos model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #model-index #autotrain_compatible #endpoints_compatible #region-us 

MiniLMv2-L12-H384-distilled-finetuned-clinc
===========================================


This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc\_oos dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3058
* Accuracy: 0.9529


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: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* 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: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* 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-clinc_oos #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* 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-clinc_oos #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* 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