
# electra-small-finetuned-sst2-rotten_tomatoes-distilled

<!-- Provide a quick summary of what the model is/does. -->

This model is a finetuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on sst2 and rotten tomatoes dataset. It achieves the following result during training:

- Training Loss: 0.070100
- Validation Loss: 0.268334
- Accuracy: 0.935986

This is a distilled version of electra base. Big thanks to philschmid for his [tutorial](https://www.philschmid.de/knowledge-distillation-bert-transformers).

## Model Details

### Model Description

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- **Developed by:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

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### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

Important Note: The text data were pre-processed before being fed to the model. The pre-processing steps will be shared later.


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

The following hyperparameters were used during training:
- learning_rate: 0.0003135413256598537
- train_batch_size: 256
- eval_batch_size:  256
- warmup_ratio: 0.1
- weight_decay: 0.0005018269723538957
- seed: 39
- num_epochs: 8

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

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[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary


## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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{"language": ["en"], "datasets": ["sst2", "rotten_tomatoes"], "metrics": ["accuracy"]}
transformers pytorch safetensors electra text-classification en dataset:sst2 dataset:rotten_tomatoes autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #electra #text-classification #en #dataset-sst2 #dataset-rotten_tomatoes #autotrain_compatible #endpoints_compatible #region-us 


# electra-small-finetuned-sst2-rotten_tomatoes-distilled



This model is a finetuned version of google/electra-small-discriminator on sst2 and rotten tomatoes dataset. It achieves the following result during training:

- Training Loss: 0.070100
- Validation Loss: 0.268334
- Accuracy: 0.935986

This is a distilled version of electra base. Big thanks to philschmid for his tutorial.

## Model Details

### Model Description





- Developed by: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: 

### Model Sources [optional]



- Repository: 

## Uses



### Direct Use





### Downstream Use [optional]





### Out-of-Scope Use





## Bias, Risks, and Limitations





### Recommendations



Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.



## Training Details

### Training Procedure 



#### Preprocessing [optional]

Important Note: The text data were pre-processed before being fed to the model. The pre-processing steps will be shared later.


#### Training Hyperparameters

- Training regime:  

The following hyperparameters were used during training:
- learning_rate: 0.0003135413256598537
- train_batch_size: 256
- eval_batch_size:  256
- warmup_ratio: 0.1
- weight_decay: 0.0005018269723538957
- seed: 39
- num_epochs: 8

#### Speeds, Sizes, Times [optional]





## Evaluation



### Testing Data, Factors & Metrics

#### Testing Data





#### Factors





#### Metrics





### Results



#### Summary


[optional]



BibTeX:



APA:



## Glossary [optional]





## More Information [optional]



## Model Card Authors [optional]



## Model Card Contact


# electra-small-finetuned-sst2-rotten_tomatoes-distilled



This model is a finetuned version of google/electra-small-discriminator on sst2 and rotten tomatoes dataset. It achieves the following result during training:

- Training Loss: 0.070100
- Validation Loss: 0.268334
- Accuracy: 0.935986

This is a distilled version of electra base. Big thanks to philschmid for his tutorial. ## Model Details ### Model Description





- Developed by: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: ### Model Sources [optional]



- Repository: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations



Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model

Use the code below to get started with the model. ## Training Details ### Training Procedure #### Preprocessing [optional]

Important Note: The text data were pre-processed before being fed to the model. The pre-processing steps will be shared later. #### Training Hyperparameters

- Training regime:  

The following hyperparameters were used during training:
- learning_rate: 0.0003135413256598537
- train_batch_size: 256
- eval_batch_size:  256
- warmup_ratio: 0.1
- weight_decay: 0.0005018269723538957
- seed: 39
- num_epochs: 8 #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary


[optional]



BibTeX:



APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
TAGS
#transformers #pytorch #safetensors #electra #text-classification #en #dataset-sst2 #dataset-rotten_tomatoes #autotrain_compatible #endpoints_compatible #region-us 
 # electra-small-finetuned-sst2-rotten_tomatoes-distilled



This model is a finetuned version of google/electra-small-discriminator on sst2 and rotten tomatoes dataset. It achieves the following result during training:

- Training Loss: 0.070100
- Validation Loss: 0.268334
- Accuracy: 0.935986

This is a distilled version of electra base. Big thanks to philschmid for his tutorial. ## Model Details ### Model Description





- Developed by: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: ### Model Sources [optional]



- Repository: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations



Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model

Use the code below to get started with the model. ## Training Details ### Training Procedure #### Preprocessing [optional]

Important Note: The text data were pre-processed before being fed to the model. The pre-processing steps will be shared later. #### Training Hyperparameters

- Training regime:  

The following hyperparameters were used during training:
- learning_rate: 0.0003135413256598537
- train_batch_size: 256
- eval_batch_size:  256
- warmup_ratio: 0.1
- weight_decay: 0.0005018269723538957
- seed: 39
- num_epochs: 8 #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary


[optional]



BibTeX:



APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
TAGS
#transformers #pytorch #safetensors #electra #text-classification #en #dataset-sst2 #dataset-rotten_tomatoes #autotrain_compatible #endpoints_compatible #region-us 
# electra-small-finetuned-sst2-rotten_tomatoes-distilled



This model is a finetuned version of google/electra-small-discriminator on sst2 and rotten tomatoes dataset. It achieves the following result during training:

- Training Loss: 0.070100
- Validation Loss: 0.268334
- Accuracy: 0.935986

This is a distilled version of electra base. Big thanks to philschmid for his tutorial.## Model Details### Model Description





- Developed by: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]:### Model Sources [optional]



- Repository:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations



Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model

Use the code below to get started with the model.## Training Details### Training Procedure#### Preprocessing [optional]

Important Note: The text data were pre-processed before being fed to the model. The pre-processing steps will be shared later.#### Training Hyperparameters

- Training regime:  

The following hyperparameters were used during training:
- learning_rate: 0.0003135413256598537
- train_batch_size: 256
- eval_batch_size:  256
- warmup_ratio: 0.1
- weight_decay: 0.0005018269723538957
- seed: 39
- num_epochs: 8#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary


[optional]



BibTeX:



APA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact