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---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------|------:|------|-----:|--------|-----:|---|-----:|
|arc_challenge| 1|none | 25|acc |0.2108|± |0.0119|
| | |none | 25|acc_norm|0.2423|± |0.0125|
|truthfulqa_mc2| 2|none | 0|acc |0.4356|± |0.0151|
|winogrande| 1|none | 5|acc |0.5138|± | 0.014|
|hellaswag| 1|none | 10|acc |0.2938|± |0.0045|
| | |none | 10|acc_norm|0.3242|± |0.0047|
|gsm8k| 3|strict-match | 5|exact_match|0.0129|± |0.0031|
| | |flexible-extract| 5|exact_match|0.0197|± |0.0038|
### MMLU *(0.2649701754385965, 0.004451753262466369)*
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|-----------------------------------|------:|------|-----:|------|-----:|---|-----:|
|world_religions | 0|none | 5|acc |0.2281|± |0.0322|
|virology | 0|none | 5|acc |0.1747|± |0.0296|
|us_foreign_policy | 0|none | 5|acc |0.2600|± |0.0441|
|sociology | 0|none | 5|acc |0.2736|± |0.0315|
|security_studies | 0|none | 5|acc |0.4000|± |0.0314|
|public_relations | 0|none | 5|acc |0.2273|± |0.0401|
|professional_psychology | 0|none | 5|acc |0.2467|± |0.0174|
|professional_medicine | 0|none | 5|acc |0.4485|± |0.0302|
|professional_law | 0|none | 5|acc |0.2490|± |0.0110|
|professional_accounting | 0|none | 5|acc |0.2340|± |0.0253|
|prehistory | 0|none | 5|acc |0.2315|± |0.0235|
|philosophy | 0|none | 5|acc |0.2154|± |0.0234|
|nutrition | 0|none | 5|acc |0.2516|± |0.0248|
|moral_scenarios | 0|none | 5|acc |0.2536|± |0.0146|
|moral_disputes | 0|none | 5|acc |0.1879|± |0.0210|
|miscellaneous | 0|none | 5|acc |0.2197|± |0.0148|
|medical_genetics | 0|none | 5|acc |0.1900|± |0.0394|
|marketing | 0|none | 5|acc |0.1923|± |0.0258|
|management | 0|none | 5|acc |0.3301|± |0.0466|
|machine_learning | 0|none | 5|acc |0.1875|± |0.0370|
|logical_fallacies | 0|none | 5|acc |0.2577|± |0.0344|
|jurisprudence | 0|none | 5|acc |0.2222|± |0.0402|
|international_law | 0|none | 5|acc |0.3802|± |0.0443|
|human_sexuality | 0|none | 5|acc |0.2137|± |0.0360|
|human_aging | 0|none | 5|acc |0.1121|± |0.0212|
|high_school_world_history | 0|none | 5|acc |0.2743|± |0.0290|
|high_school_us_history | 0|none | 5|acc |0.2353|± |0.0298|
|high_school_statistics | 0|none | 5|acc |0.4722|± |0.0340|
|high_school_psychology | 0|none | 5|acc |0.3358|± |0.0202|
|high_school_physics | 0|none | 5|acc |0.3245|± |0.0382|
|high_school_microeconomics | 0|none | 5|acc |0.2605|± |0.0285|
|high_school_mathematics | 0|none | 5|acc |0.2741|± |0.0272|
|high_school_macroeconomics | 0|none | 5|acc |0.3615|± |0.0244|
|high_school_government_and_politics| 0|none | 5|acc |0.3679|± |0.0348|
|high_school_geography | 0|none | 5|acc |0.3535|± |0.0341|
|high_school_european_history | 0|none | 5|acc |0.2485|± |0.0337|
|high_school_computer_science | 0|none | 5|acc |0.1600|± |0.0368|
|high_school_chemistry | 0|none | 5|acc |0.2709|± |0.0313|
|high_school_biology | 0|none | 5|acc |0.3032|± |0.0261|
|global_facts | 0|none | 5|acc |0.2500|± |0.0435|
|formal_logic | 0|none | 5|acc |0.1587|± |0.0327|
|elementary_mathematics | 0|none | 5|acc |0.2857|± |0.0233|
|electrical_engineering | 0|none | 5|acc |0.2483|± |0.0360|
|econometrics | 0|none | 5|acc |0.2895|± |0.0427|
|conceptual_physics | 0|none | 5|acc |0.2894|± |0.0296|
|computer_security | 0|none | 5|acc |0.1900|± |0.0394|
|college_physics | 0|none | 5|acc |0.2451|± |0.0428|
|college_medicine | 0|none | 5|acc |0.2775|± |0.0341|
|college_mathematics | 0|none | 5|acc |0.2800|± |0.0451|
|college_computer_science | 0|none | 5|acc |0.2400|± |0.0429|
|college_chemistry | 0|none | 5|acc |0.3300|± |0.0473|
|college_biology | 0|none | 5|acc |0.2639|± |0.0369|
|clinical_knowledge | 0|none | 5|acc |0.3094|± |0.0285|
|business_ethics | 0|none | 5|acc |0.1900|± |0.0394|
|astronomy | 0|none | 5|acc |0.2303|± |0.0343|
|anatomy | 0|none | 5|acc |0.3259|± |0.0405|
|abstract_algebra | 0|none | 5|acc |0.2700|± |0.0446|
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Paper [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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### Downstream Use [optional]
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### Out-of-Scope Use
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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.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
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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]
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## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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## Glossary [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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