# Model Card for Model ID

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

This model was finetuned on RACE for multiple choice (text classification). The initial model used was distilroberta-base https://huggingface.co/distilroberta-base

The model was trained using the code from https://github.com/zphang/lrqa. Please refer to and cite the authors.

# Model Details

- **Initial model:** distilroberta-base
- **LR:** 1e-5
- **Epochs:** 3
- **Warmup Ratio:** 0.1 (10%)
- **Batch Size:** 16
- **Max Seq Len:** 512

## Model Description

<!-- Provide a longer summary of what this model is. -->
- **Model type:** [DistilRoBERTa]
- **Language(s) (NLP):** [English]
- **License:** [Apache-2.0]
- **Finetuned from model [optional]:** [distilroberta-base]

## Model Sources [optional]

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

- **Repository:** [https://github.com/zphang/lrqa]
- **Dataset:** [https://huggingface.co/datasets/race]

# Bias, Risks, and Limitations

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

[More Information Needed]

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

# 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]


# 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 -->

- **Hardware Type:** A100 - 40GB
- **Hours used:** 4
- **Cloud Provider:** Private
- **Compute Region:** Portugal
- **Carbon Emitted:** 0.18 kgCO2

Experiments were conducted using a private infrastructure, which has a carbon efficiency of 0.178 kgCO$_2$eq/kWh. A cumulative of 4 hours of computation was performed on hardware of type A100 PCIe 40/80GB (TDP of 250W).
Total emissions are estimated to be 0.18 kgCO$_2$eq of which 0 percent were directly offset.
Estimations were conducted using the \href{https://mlco2.github.io/impact#compute}{MachineLearning Impact calculator} presented in \cite{lacoste2019quantifying}.




{"language": ["en"], "license": "apache-2.0", "tags": ["text classification", "multiple-choice"], "datasets": ["race"]}
transformers pytorch roberta multiple-choice text classification en dataset:race license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #multiple-choice #text classification #en #dataset-race #license-apache-2.0 #endpoints_compatible #region-us 

# Model Card for Model ID



This model was finetuned on RACE for multiple choice (text classification). The initial model used was distilroberta-base URL

The model was trained using the code from URL Please refer to and cite the authors.

# Model Details

- Initial model: distilroberta-base
- LR: 1e-5
- Epochs: 3
- Warmup Ratio: 0.1 (10%)
- Batch Size: 16
- Max Seq Len: 512

## Model Description


- Model type: [DistilRoBERTa]
- Language(s) (NLP): [English]
- License: [Apache-2.0]
- Finetuned from model [optional]: [distilroberta-base]

## Model Sources [optional]



- Repository: [URL
- Dataset: [URL

# Bias, Risks, and Limitations





## How to Get Started with the Model

Use the code below to get started with the model.



# Training Details

## Training Data






# Model Examination [optional]





# Environmental Impact



- Hardware Type: A100 - 40GB
- Hours used: 4
- Cloud Provider: Private
- Compute Region: Portugal
- Carbon Emitted: 0.18 kgCO2

Experiments were conducted using a private infrastructure, which has a carbon efficiency of 0.178 kgCO$_2$eq/kWh. A cumulative of 4 hours of computation was performed on hardware of type A100 PCIe 40/80GB (TDP of 250W).
Total emissions are estimated to be 0.18 kgCO$_2$eq of which 0 percent were directly offset.
Estimations were conducted using the \href{URL Impact calculator} presented in \cite{lacoste2019quantifying}.




# Model Card for Model ID



This model was finetuned on RACE for multiple choice (text classification). The initial model used was distilroberta-base URL

The model was trained using the code from URL Please refer to and cite the authors. # Model Details

- Initial model: distilroberta-base
- LR: 1e-5
- Epochs: 3
- Warmup Ratio: 0.1 (10%)
- Batch Size: 16
- Max Seq Len: 512 ## Model Description


- Model type: [DistilRoBERTa]
- Language(s) (NLP): [English]
- License: [Apache-2.0]
- Finetuned from model [optional]: [distilroberta-base] ## Model Sources [optional]



- Repository: [URL
- Dataset: [URL # Bias, Risks, and Limitations ## How to Get Started with the Model

Use the code below to get started with the model. # Training Details ## Training Data # Model Examination [optional] # Environmental Impact



- Hardware Type: A100 - 40GB
- Hours used: 4
- Cloud Provider: Private
- Compute Region: Portugal
- Carbon Emitted: 0.18 kgCO2

Experiments were conducted using a private infrastructure, which has a carbon efficiency of 0.178 kgCO$_2$eq/kWh. A cumulative of 4 hours of computation was performed on hardware of type A100 PCIe 40/80GB (TDP of 250W).
Total emissions are estimated to be 0.18 kgCO$_2$eq of which 0 percent were directly offset.
Estimations were conducted using the \href{URL Impact calculator} presented in \cite{lacoste2019quantifying}.
TAGS
#transformers #pytorch #roberta #multiple-choice #text classification #en #dataset-race #license-apache-2.0 #endpoints_compatible #region-us 
 # Model Card for Model ID



This model was finetuned on RACE for multiple choice (text classification). The initial model used was distilroberta-base URL

The model was trained using the code from URL Please refer to and cite the authors. # Model Details

- Initial model: distilroberta-base
- LR: 1e-5
- Epochs: 3
- Warmup Ratio: 0.1 (10%)
- Batch Size: 16
- Max Seq Len: 512 ## Model Description


- Model type: [DistilRoBERTa]
- Language(s) (NLP): [English]
- License: [Apache-2.0]
- Finetuned from model [optional]: [distilroberta-base] ## Model Sources [optional]



- Repository: [URL
- Dataset: [URL # Bias, Risks, and Limitations ## How to Get Started with the Model

Use the code below to get started with the model. # Training Details ## Training Data # Model Examination [optional] # Environmental Impact



- Hardware Type: A100 - 40GB
- Hours used: 4
- Cloud Provider: Private
- Compute Region: Portugal
- Carbon Emitted: 0.18 kgCO2

Experiments were conducted using a private infrastructure, which has a carbon efficiency of 0.178 kgCO$_2$eq/kWh. A cumulative of 4 hours of computation was performed on hardware of type A100 PCIe 40/80GB (TDP of 250W).
Total emissions are estimated to be 0.18 kgCO$_2$eq of which 0 percent were directly offset.
Estimations were conducted using the \href{URL Impact calculator} presented in \cite{lacoste2019quantifying}.
TAGS
#transformers #pytorch #roberta #multiple-choice #text classification #en #dataset-race #license-apache-2.0 #endpoints_compatible #region-us 
# Model Card for Model ID



This model was finetuned on RACE for multiple choice (text classification). The initial model used was distilroberta-base URL

The model was trained using the code from URL Please refer to and cite the authors.# Model Details

- Initial model: distilroberta-base
- LR: 1e-5
- Epochs: 3
- Warmup Ratio: 0.1 (10%)
- Batch Size: 16
- Max Seq Len: 512## Model Description


- Model type: [DistilRoBERTa]
- Language(s) (NLP): [English]
- License: [Apache-2.0]
- Finetuned from model [optional]: [distilroberta-base]## Model Sources [optional]



- Repository: [URL
- Dataset: [URL# Bias, Risks, and Limitations## How to Get Started with the Model

Use the code below to get started with the model.# Training Details## Training Data# Model Examination [optional]# Environmental Impact



- Hardware Type: A100 - 40GB
- Hours used: 4
- Cloud Provider: Private
- Compute Region: Portugal
- Carbon Emitted: 0.18 kgCO2

Experiments were conducted using a private infrastructure, which has a carbon efficiency of 0.178 kgCO$_2$eq/kWh. A cumulative of 4 hours of computation was performed on hardware of type A100 PCIe 40/80GB (TDP of 250W).
Total emissions are estimated to be 0.18 kgCO$_2$eq of which 0 percent were directly offset.
Estimations were conducted using the \href{URL Impact calculator} presented in \cite{lacoste2019quantifying}.