---
library_name: transformers
tags: []
---

# Model Card for Model ID

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## Model Details

### Model Description

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|    Tasks    |Version|Filter|n-shot| Metric |Value |   |Stderr|
|-------------|------:|------|-----:|--------|-----:|---|-----:|
|arc_challenge|      1|none  |    25|acc     |0.1792|±  |0.0112|
|             |       |none  |    25|acc_norm|0.2065|±  |0.0118|
|truthfulqa_mc2|      2|none  |     0|acc   |0.4553|±  |0.0154|
|winogrande|      1|none  |     5|acc   |0.4972|±  |0.0141|
|hellaswag|      1|none  |    10|acc     |0.2703|±  |0.0044|
|         |       |none  |    10|acc_norm|0.2796|±  |0.0045|
|gsm8k|      3|strict-match    |     5|exact_match|0.0000|±  |0.0000|
|     |       |flexible-extract|     5|exact_match|0.0144|±  |0.0033|

(0.24656842105263158, 0.004373961821155628)
|               Tasks               |Version|Filter|n-shot|Metric|Value |   |Stderr|
|-----------------------------------|------:|------|-----:|------|-----:|---|-----:|
|world_religions                    |      0|none  |     5|acc   |0.2573|±  |0.0335|
|virology                           |      0|none  |     5|acc   |0.2831|±  |0.0351|
|us_foreign_policy                  |      0|none  |     5|acc   |0.2500|±  |0.0435|
|sociology                          |      0|none  |     5|acc   |0.2438|±  |0.0304|
|security_studies                   |      0|none  |     5|acc   |0.2327|±  |0.0270|
|public_relations                   |      0|none  |     5|acc   |0.2273|±  |0.0401|
|professional_psychology            |      0|none  |     5|acc   |0.2500|±  |0.0175|
|professional_medicine              |      0|none  |     5|acc   |0.4485|±  |0.0302|
|professional_law                   |      0|none  |     5|acc   |0.2458|±  |0.0110|
|professional_accounting            |      0|none  |     5|acc   |0.2624|±  |0.0262|
|prehistory                         |      0|none  |     5|acc   |0.2130|±  |0.0228|
|philosophy                         |      0|none  |     5|acc   |0.1929|±  |0.0224|
|nutrition                          |      0|none  |     5|acc   |0.2222|±  |0.0238|
|moral_scenarios                    |      0|none  |     5|acc   |0.2380|±  |0.0142|
|moral_disputes                     |      0|none  |     5|acc   |0.2486|±  |0.0233|
|miscellaneous                      |      0|none  |     5|acc   |0.2644|±  |0.0158|
|medical_genetics                   |      0|none  |     5|acc   |0.3000|±  |0.0461|
|marketing                          |      0|none  |     5|acc   |0.1752|±  |0.0249|
|management                         |      0|none  |     5|acc   |0.1748|±  |0.0376|
|machine_learning                   |      0|none  |     5|acc   |0.2500|±  |0.0411|
|logical_fallacies                  |      0|none  |     5|acc   |0.2945|±  |0.0358|
|jurisprudence                      |      0|none  |     5|acc   |0.2593|±  |0.0424|
|international_law                  |      0|none  |     5|acc   |0.2479|±  |0.0394|
|human_sexuality                    |      0|none  |     5|acc   |0.2595|±  |0.0384|
|human_aging                        |      0|none  |     5|acc   |0.2466|±  |0.0289|
|high_school_world_history          |      0|none  |     5|acc   |0.2911|±  |0.0296|
|high_school_us_history             |      0|none  |     5|acc   |0.2794|±  |0.0315|
|high_school_statistics             |      0|none  |     5|acc   |0.4722|±  |0.0340|
|high_school_psychology             |      0|none  |     5|acc   |0.1927|±  |0.0169|
|high_school_physics                |      0|none  |     5|acc   |0.1987|±  |0.0326|
|high_school_microeconomics         |      0|none  |     5|acc   |0.2227|±  |0.0270|
|high_school_mathematics            |      0|none  |     5|acc   |0.2667|±  |0.0270|
|high_school_macroeconomics         |      0|none  |     5|acc   |0.2103|±  |0.0207|
|high_school_government_and_politics|      0|none  |     5|acc   |0.2435|±  |0.0310|
|high_school_geography              |      0|none  |     5|acc   |0.1717|±  |0.0269|
|high_school_european_history       |      0|none  |     5|acc   |0.2485|±  |0.0337|
|high_school_computer_science       |      0|none  |     5|acc   |0.2700|±  |0.0446|
|high_school_chemistry              |      0|none  |     5|acc   |0.2906|±  |0.0319|
|high_school_biology                |      0|none  |     5|acc   |0.2774|±  |0.0255|
|global_facts                       |      0|none  |     5|acc   |0.1600|±  |0.0368|
|formal_logic                       |      0|none  |     5|acc   |0.1508|±  |0.0320|
|elementary_mathematics             |      0|none  |     5|acc   |0.2540|±  |0.0224|
|electrical_engineering             |      0|none  |     5|acc   |0.2414|±  |0.0357|
|econometrics                       |      0|none  |     5|acc   |0.2544|±  |0.0410|
|conceptual_physics                 |      0|none  |     5|acc   |0.2638|±  |0.0288|
|computer_security                  |      0|none  |     5|acc   |0.2600|±  |0.0441|
|college_physics                    |      0|none  |     5|acc   |0.2157|±  |0.0409|
|college_medicine                   |      0|none  |     5|acc   |0.2081|±  |0.0310|
|college_mathematics                |      0|none  |     5|acc   |0.2300|±  |0.0423|
|college_computer_science           |      0|none  |     5|acc   |0.3100|±  |0.0465|
|college_chemistry                  |      0|none  |     5|acc   |0.2000|±  |0.0402|
|college_biology                    |      0|none  |     5|acc   |0.2431|±  |0.0359|
|clinical_knowledge                 |      0|none  |     5|acc   |0.2415|±  |0.0263|
|business_ethics                    |      0|none  |     5|acc   |0.1600|±  |0.0368|
|astronomy                          |      0|none  |     5|acc   |0.1776|±  |0.0311|
|anatomy                            |      0|none  |     5|acc   |0.3407|±  |0.0409|
|abstract_algebra                   |      0|none  |     5|acc   |0.2200|±  |0.0416|

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## Uses

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### Direct Use

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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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

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## Training Details

### Training Data

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### Training Procedure 

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#### Training Hyperparameters

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## Evaluation

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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).

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