
# Model Card for BenjaminOcampo/task-implicit_task__model-deberta__aug_method-gm

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

#  Table of Contents

1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#model-examination-optional)
7. [Environmental Impact](#environmental-impact)
8. [Technical Specifications](#technical-specifications-optional)
9. [Citation](#citation-optional)
10. [Glossary](#glossary-optional)
11. [More Information](#more-information-optional)
12. [Model Card Authors](#model-card-authors-optional)
13. [Model Card Contact](#model-card-contact)
14. [How To Get Started With the Model](#how-to-get-started-with-the-model)


# Model Details

## Model Description

<!-- Provide a longer summary of what this model is. -->

**Classification results dev set**
```
              precision    recall  f1-score   support

           0       0.90      0.90      0.90      2680
           1       0.83      0.82      0.83      1501
           2       0.40      0.39      0.40       186

    accuracy                           0.85      4367
   macro avg       0.71      0.70      0.71      4367
weighted avg       0.85      0.85      0.85      4367
```
**Classification results test set**
```
              precision    recall  f1-score   support

           0       0.91      0.91      0.91      2681
           1       0.84      0.85      0.85      1501
           2       0.45      0.44      0.45       186

    accuracy                           0.87      4368
   macro avg       0.73      0.73      0.73      4368
weighted avg       0.87      0.87      0.87      4368
```


- **Developed by:** Nicolás Benjamín Ocampo
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
- **Resources for more information:** [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 -->

[More Information Needed]

## Out-of-Scope Use

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

[More Information Needed]

# Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

## Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

# Training Details

## Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

## Training Procedure [optional]

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

### Preprocessing

[More Information Needed]

### Speeds, Sizes, Times

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

# Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

## Testing Data, Factors & Metrics

### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

### Metrics

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

[More Information Needed]

## Results

[More Information Needed]

# Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

# Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

# Technical Specifications [optional]

## Model Architecture and Objective

[More Information Needed]

## Compute Infrastructure

[More Information Needed]

### Hardware

[More Information Needed]

### Software

[More Information Needed]

# Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

# Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

# More Information [optional]

[More Information Needed]

# Model Card Authors [optional]

[More Information Needed]

# Model Card Contact

[More Information Needed]

# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>

[More Information Needed]

</details>
{"language": "en"}
transformers pytorch deberta-v2 text-classification en arxiv:1910.09700 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #en #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us 


# Model Card for BenjaminOcampo/task-implicit_task__model-deberta__aug_method-gm



#  Table of Contents

1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Model Examination
7. Environmental Impact
8. Technical Specifications
9. Citation
10. Glossary
11. More Information
12. Model Card Authors
13. Model Card Contact
14. How To Get Started With the Model


# Model Details

## Model Description



Classification results dev set

Classification results test set



- Developed by: Nicolás Benjamín Ocampo
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): en
- License: 
- Finetuned from model [optional]: 
- Resources for more information: 

# 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.

# Training Details

## Training Data





## Training Procedure [optional]



### Preprocessing



### Speeds, Sizes, Times





# Evaluation



## Testing Data, Factors & Metrics

### Testing Data





### Factors





### Metrics





## Results



# Model Examination [optional]





# Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: 

# Technical Specifications [optional]

## Model Architecture and Objective



## Compute Infrastructure



### Hardware



### Software



[optional]



BibTeX:



APA:



# Glossary [optional]





# More Information [optional]



# Model Card Authors [optional]



# Model Card Contact



# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>



</details>
# Model Card for BenjaminOcampo/task-implicit_task__model-deberta__aug_method-gm #  Table of Contents

1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Model Examination
7. Environmental Impact
8. Technical Specifications
9. Citation
10. Glossary
11. More Information
12. Model Card Authors
13. Model Card Contact
14. How To Get Started With the Model # Model Details ## Model Description



Classification results dev set

Classification results test set



- Developed by: Nicolás Benjamín Ocampo
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): en
- License: 
- Finetuned from model [optional]: 
- Resources for more information: # 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. # Training Details ## Training Data ## Training Procedure [optional] ### Preprocessing ### Speeds, Sizes, Times # Evaluation ## Testing Data, Factors & Metrics ### Testing Data ### Factors ### Metrics ## Results # Model Examination [optional] # Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: # Technical Specifications [optional] ## Model Architecture and Objective ## Compute Infrastructure ### Hardware ### Software



[optional]



BibTeX:



APA: # Glossary [optional] # More Information [optional] # Model Card Authors [optional] # Model Card Contact # How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>



</details>
TAGS
#transformers #pytorch #deberta-v2 #text-classification #en #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us 
 # Model Card for BenjaminOcampo/task-implicit_task__model-deberta__aug_method-gm #  Table of Contents

1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Model Examination
7. Environmental Impact
8. Technical Specifications
9. Citation
10. Glossary
11. More Information
12. Model Card Authors
13. Model Card Contact
14. How To Get Started With the Model # Model Details ## Model Description



Classification results dev set

Classification results test set



- Developed by: Nicolás Benjamín Ocampo
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): en
- License: 
- Finetuned from model [optional]: 
- Resources for more information: # 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. # Training Details ## Training Data ## Training Procedure [optional] ### Preprocessing ### Speeds, Sizes, Times # Evaluation ## Testing Data, Factors & Metrics ### Testing Data ### Factors ### Metrics ## Results # Model Examination [optional] # Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: # Technical Specifications [optional] ## Model Architecture and Objective ## Compute Infrastructure ### Hardware ### Software



[optional]



BibTeX:



APA: # Glossary [optional] # More Information [optional] # Model Card Authors [optional] # Model Card Contact # How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>



</details>
TAGS
#transformers #pytorch #deberta-v2 #text-classification #en #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us 
# Model Card for BenjaminOcampo/task-implicit_task__model-deberta__aug_method-gm#  Table of Contents

1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Model Examination
7. Environmental Impact
8. Technical Specifications
9. Citation
10. Glossary
11. More Information
12. Model Card Authors
13. Model Card Contact
14. How To Get Started With the Model# Model Details## Model Description



Classification results dev set

Classification results test set



- Developed by: Nicolás Benjamín Ocampo
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): en
- License: 
- Finetuned from model [optional]: 
- Resources for more information:# 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.# Training Details## Training Data## Training Procedure [optional]### Preprocessing### Speeds, Sizes, Times# Evaluation## Testing Data, Factors & Metrics### Testing Data### Factors### Metrics## Results# Model Examination [optional]# Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted:# Technical Specifications [optional]## Model Architecture and Objective## Compute Infrastructure### Hardware### Software



[optional]



BibTeX:



APA:# Glossary [optional]# More Information [optional]# Model Card Authors [optional]# Model Card Contact# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>



</details>