
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# MiniLMv2-L12-H384-distilled-finetuned-spam-detection

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 sms_spam dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0938
- Accuracy: 0.9928

## 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: 32
- eval_batch_size: 32
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4101        | 1.0   | 131  | 0.4930          | 0.9763   |
| 0.8003        | 2.0   | 262  | 0.3999          | 0.9799   |
| 0.377         | 3.0   | 393  | 0.3196          | 0.9828   |
| 0.302         | 4.0   | 524  | 0.3462          | 0.9828   |
| 0.1945        | 5.0   | 655  | 0.1094          | 0.9928   |
| 0.1393        | 6.0   | 786  | 0.0938          | 0.9928   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "datasets": ["sms_spam"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L12-H384-distilled-finetuned-spam-detection", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "sms_spam", "type": "sms_spam", "args": "plain_text"}, "metrics": [{"type": "accuracy", "value": 0.9928263988522238, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer dataset:sms_spam model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-sms_spam #model-index #autotrain_compatible #endpoints_compatible #region-us 

MiniLMv2-L12-H384-distilled-finetuned-spam-detection
====================================================


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


* Loss: 0.0938
* Accuracy: 0.9928


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: 32
* eval\_batch\_size: 32
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.2+cu113
* Datasets 1.18.4
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.2+cu113
* Datasets 1.18.4
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-sms_spam #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.2+cu113
* Datasets 1.18.4
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-sms_spam #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.2+cu113
* Datasets 1.18.4
* Tokenizers 0.12.1