--- library_name: transformers tags: [] --- # Model Card for Model ID ## Model Details ### Model Description This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Environmental Impact 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] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] Agieval | Task | Version | Metric | Value | | StdErr | |-------------------------------------------|---------|--------|-------|---|---------| | agieval\_aqua\_rat | 0 | acc | 24.02 | _ | 2.69 | | agieval\_aqua\_rat | 0 | acc\_norm | 24.02 | _ | 2.69 | | agieval\_logiqa\_en | 0 | acc | 23.20 | _ | 1.66 | | agieval\_logiqa\_en | 0 | acc\_norm | 24.42 | _ | 1.69 | | agieval\_lsat\_ar | 0 | acc | 18.26 | _ | 2.55 | | agieval\_lsat\_ar | 0 | acc\_norm | 18.70 | _ | 2.58 | | agieval\_lsat\_lr | 0 | acc | 22.35 | _ | 1.85 | | agieval\_lsat\_lr | 0 | acc\_norm | 23.53 | _ | 1.88 | | agieval\_lsat\_rc | 0 | acc | 20.82 | _ | 2.48 | | agieval\_lsat\_rc | 0 | acc\_norm | 20.07 | _ | 2.45 | | agieval\_sat\_en | 0 | acc | 32.52 | _ | 3.27 | | agieval\_sat\_en | 0 | acc\_norm | 32.52 | _ | 3.27 | | agieval\_sat\_en\_without\_passage | 0 | acc | 25.73 | _ | 3.05 | | agieval\_sat\_en\_without\_passage | 0 | acc\_norm | 24.27 | _ | 2.99 | | agieval\_sat\_math | 0 | acc | 25.00 | _ | 2.93 | | agieval\_sat\_math | 0 | acc\_norm | 20.91 | _ | 2.75 | Average: 24.11 GPT4ALL | Task | Version | Metric | Value | | StdErr | |----------------------|---------|--------|-------|---|---------| | arc\_challenge | 0 | acc | 21.77 | _ | 1.21 | | arc\_challenge | 0 | acc\_norm | 24.15 | _ | 1.25 | | arc\_easy | 0 | acc | 37.37 | _ | 0.99 | | arc\_easy | 0 | acc\_norm | 36.95 | _ | 0.99 | | boolq | 1 | acc | 65.60 | _ | 0.83 | | hellaswag | 0 | acc | 34.54 | _ | 0.47 | | hellaswag | 0 | acc\_norm | 40.54 | _ | 0.49 | | openbookqa | 0 | acc | 15.00 | _ | 1.59 | | openbookqa | 0 | acc\_norm | 27.40 | _ | 2.00 | | piqa | 0 | acc | 60.88 | _ | 1.14 | | piqa | 0 | acc\_norm | 60.55 | _ | 1.14 | | winogrande | 0 | acc | 50.91 | _ | 1.41 | Average: 40.01 BigBench | Task | Version | Metric | Value | Std Err | |-----------------------------------|---------|--------|--------|---------| | bigbench\_causal\_judgement | 0 | MCG | 50 | 2.26 | | bigbench\_date\_understanding | 0 | MCG | 49.14 | 2.18 | | bigbench\_disambiguation\_qa | 0 | MCG | 49.31 | 2.74 | | bigbench\_geometric\_shapes | 0 | MCG | 14.18 | 1.37 | | bigbench\_logical\_deduction\_5objs | 0 | MCG | 49.41 | 2.73 | | bigbench\_logical\_deduction\_7objs | 0 | MCG | 41.48 | 2.46 | | bigbench\_logical\_deduction\_3objs | 0 | MCG | 69.33 | 2.75 | | bigbench\_movie\_recommendation | 0 | MCG | 51.71 | 2.25 | | bigbench\_navigate | 0 | MCG | 50 | 1.58 | | bigbench\_reasoning\_colored\_obj | 0 | MCG | 51.92 | 0.99 | | bigbench\_ruin\_names | 0 | MCG | 48.14 | 2.01 | | bigbench\_salient\_trans\_err\_detec | 0 | MCG | 39.92 | 1.2 | | bigbench\_snarks | 0 | MCG | 64.14 | 3.71 | | bigbench\_sports\_understanding | 0 | MCG | 55.31 | 1.59 | | bigbench\_temporal\_sequences | 0 | MCG | 46.92 | 1.4 | | bigbench\_tsk\_shuff\_objs\_5 | 0 | MCG | 25.04 | 1.01 | | bigbench\_tsk\_shuff\_objs\_7 | 0 | MCG | 15.04 | 0.72 | | bigbench\_tsk\_shuff\_objs\_3 | 0 | MCG | 55.33 | 2.75 | Average: 44.75 TruthfulQA | Task | Version | Metric | Value | Std Err | |----------------------------------|---------|--------|--------|----------| | truthfulqa\_mc | 1 | mc1 | 30.11 | 1.61 | | truthfulqa\_mc | 1 | mc2 | 47.69 | 1.61 | Average: 38.90 # Openllm Benchmark | Task |Version| Metric |Value| |Stderr| |-------------|------:|--------|----:|---|-----:| |arc_challenge| 0|acc |40.44|± | 1.43| | | |acc_norm|43.81|± | 1.34| |hellaswag | 0|acc |48.1 |± | 0.45| | | |acc_norm|62.73|± | 0.32| |gsm8k | 0|acc |5.6 |± | 0.6 | |winogrande | 0|acc |60.91|± | 1.3 | |mmlu | 0|acc |37.62 |±| 0.6 | Average: 73.5% ### TruthfulQA | Task |Version|Metric|Value| |Stderr| |-------------|------:|------|----:|---|-----:| |truthfulqa_mc| 1|mc1 |29.00|± | 1.58| | | |mc2 |45.83|± | 1.59|