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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- 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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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical 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. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset 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. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
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Agieval |
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| Task | Version | Metric | Value | | StdErr | |
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|-------------------------------------------|---------|--------|-------|---|---------| |
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| agieval\_aqua\_rat | 0 | acc | 24.02 | _ | 2.69 | |
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| agieval\_aqua\_rat | 0 | acc\_norm | 24.02 | _ | 2.69 | |
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| agieval\_logiqa\_en | 0 | acc | 23.20 | _ | 1.66 | |
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| agieval\_logiqa\_en | 0 | acc\_norm | 24.42 | _ | 1.69 | |
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| agieval\_lsat\_ar | 0 | acc | 18.26 | _ | 2.55 | |
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| agieval\_lsat\_ar | 0 | acc\_norm | 18.70 | _ | 2.58 | |
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| agieval\_lsat\_lr | 0 | acc | 22.35 | _ | 1.85 | |
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| agieval\_lsat\_lr | 0 | acc\_norm | 23.53 | _ | 1.88 | |
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| agieval\_lsat\_rc | 0 | acc | 20.82 | _ | 2.48 | |
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| agieval\_lsat\_rc | 0 | acc\_norm | 20.07 | _ | 2.45 | |
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| agieval\_sat\_en | 0 | acc | 32.52 | _ | 3.27 | |
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| agieval\_sat\_en | 0 | acc\_norm | 32.52 | _ | 3.27 | |
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| agieval\_sat\_en\_without\_passage | 0 | acc | 25.73 | _ | 3.05 | |
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| agieval\_sat\_en\_without\_passage | 0 | acc\_norm | 24.27 | _ | 2.99 | |
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| agieval\_sat\_math | 0 | acc | 25.00 | _ | 2.93 | |
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| agieval\_sat\_math | 0 | acc\_norm | 20.91 | _ | 2.75 | |
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Average: 24.11 |
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GPT4ALL |
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| Task | Version | Metric | Value | | StdErr | |
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|----------------------|---------|--------|-------|---|---------| |
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| arc\_challenge | 0 | acc | 21.77 | _ | 1.21 | |
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| arc\_challenge | 0 | acc\_norm | 24.15 | _ | 1.25 | |
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| arc\_easy | 0 | acc | 37.37 | _ | 0.99 | |
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| arc\_easy | 0 | acc\_norm | 36.95 | _ | 0.99 | |
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| boolq | 1 | acc | 65.60 | _ | 0.83 | |
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| hellaswag | 0 | acc | 34.54 | _ | 0.47 | |
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| hellaswag | 0 | acc\_norm | 40.54 | _ | 0.49 | |
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| openbookqa | 0 | acc | 15.00 | _ | 1.59 | |
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| openbookqa | 0 | acc\_norm | 27.40 | _ | 2.00 | |
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| piqa | 0 | acc | 60.88 | _ | 1.14 | |
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| piqa | 0 | acc\_norm | 60.55 | _ | 1.14 | |
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| winogrande | 0 | acc | 50.91 | _ | 1.41 | |
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Average: 40.01 |
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BigBench |
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| Task | Version | Metric | Value | Std Err | |
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|-----------------------------------|---------|--------|--------|---------| |
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| bigbench\_causal\_judgement | 0 | MCG | 50 | 2.26 | |
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| bigbench\_date\_understanding | 0 | MCG | 49.14 | 2.18 | |
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| bigbench\_disambiguation\_qa | 0 | MCG | 49.31 | 2.74 | |
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| bigbench\_geometric\_shapes | 0 | MCG | 14.18 | 1.37 | |
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| bigbench\_logical\_deduction\_5objs | 0 | MCG | 49.41 | 2.73 | |
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| bigbench\_logical\_deduction\_7objs | 0 | MCG | 41.48 | 2.46 | |
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| bigbench\_logical\_deduction\_3objs | 0 | MCG | 69.33 | 2.75 | |
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| bigbench\_movie\_recommendation | 0 | MCG | 51.71 | 2.25 | |
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| bigbench\_navigate | 0 | MCG | 50 | 1.58 | |
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| bigbench\_reasoning\_colored\_obj | 0 | MCG | 51.92 | 0.99 | |
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| bigbench\_ruin\_names | 0 | MCG | 48.14 | 2.01 | |
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| bigbench\_salient\_trans\_err\_detec | 0 | MCG | 39.92 | 1.2 | |
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| bigbench\_snarks | 0 | MCG | 64.14 | 3.71 | |
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| bigbench\_sports\_understanding | 0 | MCG | 55.31 | 1.59 | |
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| bigbench\_temporal\_sequences | 0 | MCG | 46.92 | 1.4 | |
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| bigbench\_tsk\_shuff\_objs\_5 | 0 | MCG | 25.04 | 1.01 | |
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| bigbench\_tsk\_shuff\_objs\_7 | 0 | MCG | 15.04 | 0.72 | |
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| bigbench\_tsk\_shuff\_objs\_3 | 0 | MCG | 55.33 | 2.75 | |
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Average: 44.75 |
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TruthfulQA |
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| Task | Version | Metric | Value | Std Err | |
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|----------------------------------|---------|--------|--------|----------| |
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| truthfulqa\_mc | 1 | mc1 | 30.11 | 1.61 | |
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| truthfulqa\_mc | 1 | mc2 | 47.69 | 1.61 | |
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Average: 38.90 |
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# Openllm Benchmark |
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| Task |Version| Metric |Value| |Stderr| |
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|-------------|------:|--------|----:|---|-----:| |
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|arc_challenge| 0|acc |40.44|± | 1.43| |
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| | |acc_norm|43.81|± | 1.34| |
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|hellaswag | 0|acc |48.1 |± | 0.45| |
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| | |acc_norm|62.73|± | 0.32| |
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|gsm8k | 0|acc |5.6 |± | 0.6 | |
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|winogrande | 0|acc |60.91|± | 1.3 | |
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|mmlu | 0|acc |37.62 |±| 0.6 | |
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Average: 73.5% |
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### TruthfulQA |
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| Task |Version|Metric|Value| |Stderr| |
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|-------------|------:|------|----:|---|-----:| |
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|truthfulqa_mc| 1|mc1 |29.00|± | 1.58| |
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| | |mc2 |45.83|± | 1.59| |
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