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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- maths |
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- gpt2 |
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- mathgpt2 |
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datasets: |
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- meta-math/MetaMathQA |
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- ArtifactAI/arxiv-math-instruct-50k |
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- Sharathhebbar24/Open-Platypus |
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widget: |
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- text: Which motion is formed by an incident particle? |
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example_title: Example 1 |
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- text: What type of diffusional modeling is used for diffusion? |
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example_title: Example 2 |
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--- |
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|
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This model is a finetuned version of ```Sharathhebbar24/math_gpt2``` using ```meta-math/MetaMathQA``` |
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|
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## Model description |
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|
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GPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This |
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means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots |
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of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, |
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it was trained to guess the next word in sentences. |
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|
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More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, |
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shifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the |
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predictions for the token `i` only use the inputs from `1` to `i` but not the future tokens. |
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|
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This way, the model learns an inner representation of the English language that can then be used to extract features |
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useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a |
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prompt. |
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|
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### To use this model |
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|
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```python |
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>>> from transformers import AutoTokenizer, AutoModelForCausalLM |
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>>> model_name = "Sharathhebbar24/math_gpt2_sft" |
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>>> model = AutoModelForCausalLM.from_pretrained(model_name) |
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>>> tokenizer = AutoTokenizer.from_pretrained(model_name) |
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>>> def generate_text(prompt): |
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>>> inputs = tokenizer.encode(prompt, return_tensors='pt') |
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>>> outputs = model.generate(inputs, max_length=64, pad_token_id=tokenizer.eos_token_id) |
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>>> generated = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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>>> return generated[:generated.rfind(".")+1] |
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>>> prompt = "Gracie and Joe are choosing numbers on the complex plane. Joe chooses the point $1+2i$. Gracie chooses $-1+i$. How far apart are Gracie and Joe's points?" |
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>>> res = generate_text(prompt) |
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>>> res |
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``` |
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# Benchmark / Evaluation |
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|
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| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8k | |
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| ------- | -------- | -------- | ------- | -------- | -------- | ------- | -------- | |
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| Sharathhebbar24/math_gpt2_sft | 28.503 | 22.87 | 30.41 | 25.06 | 37.62 | 51.54 | 0.68 | |
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|
|
```python |
|
{ |
|
"all": { |
|
"acc": 0.25082189621988066, |
|
"acc_stderr": 0.030526589726831692, |
|
"acc_norm": 0.25112870356236633, |
|
"acc_norm_stderr": 0.03129390389566968, |
|
"mc1": 0.24112607099143207, |
|
"mc1_stderr": 0.014974827279752334, |
|
"mc2": 0.3762297840067963, |
|
"mc2_stderr": 0.01445991036363257 |
|
}, |
|
"harness|arc:challenge|25": { |
|
"acc": 0.20563139931740615, |
|
"acc_stderr": 0.01181074526074258, |
|
"acc_norm": 0.22866894197952217, |
|
"acc_norm_stderr": 0.012272853582540799 |
|
}, |
|
"harness|hellaswag|10": { |
|
"acc": 0.2884883489344752, |
|
"acc_stderr": 0.004521334761709224, |
|
"acc_norm": 0.30412268472415854, |
|
"acc_norm_stderr": 0.00459094683972719 |
|
}, |
|
"harness|hendrycksTest-abstract_algebra|5": { |
|
"acc": 0.19, |
|
"acc_stderr": 0.03942772444036625, |
|
"acc_norm": 0.19, |
|
"acc_norm_stderr": 0.03942772444036625 |
|
}, |
|
"harness|hendrycksTest-anatomy|5": { |
|
"acc": 0.2074074074074074, |
|
"acc_stderr": 0.03502553170678319, |
|
"acc_norm": 0.2074074074074074, |
|
"acc_norm_stderr": 0.03502553170678319 |
|
}, |
|
"harness|hendrycksTest-astronomy|5": { |
|
"acc": 0.17763157894736842, |
|
"acc_stderr": 0.031103182383123398, |
|
"acc_norm": 0.17763157894736842, |
|
"acc_norm_stderr": 0.031103182383123398 |
|
}, |
|
"harness|hendrycksTest-business_ethics|5": { |
|
"acc": 0.19, |
|
"acc_stderr": 0.03942772444036622, |
|
"acc_norm": 0.19, |
|
"acc_norm_stderr": 0.03942772444036622 |
|
}, |
|
"harness|hendrycksTest-clinical_knowledge|5": { |
|
"acc": 0.2188679245283019, |
|
"acc_stderr": 0.025447863825108618, |
|
"acc_norm": 0.2188679245283019, |
|
"acc_norm_stderr": 0.025447863825108618 |
|
}, |
|
"harness|hendrycksTest-college_biology|5": { |
|
"acc": 0.25, |
|
"acc_stderr": 0.03621034121889507, |
|
"acc_norm": 0.25, |
|
"acc_norm_stderr": 0.03621034121889507 |
|
}, |
|
"harness|hendrycksTest-college_chemistry|5": { |
|
"acc": 0.2, |
|
"acc_stderr": 0.04020151261036845, |
|
"acc_norm": 0.2, |
|
"acc_norm_stderr": 0.04020151261036845 |
|
}, |
|
"harness|hendrycksTest-college_computer_science|5": { |
|
"acc": 0.32, |
|
"acc_stderr": 0.046882617226215034, |
|
"acc_norm": 0.32, |
|
"acc_norm_stderr": 0.046882617226215034 |
|
}, |
|
"harness|hendrycksTest-college_mathematics|5": { |
|
"acc": 0.24, |
|
"acc_stderr": 0.042923469599092816, |
|
"acc_norm": 0.24, |
|
"acc_norm_stderr": 0.042923469599092816 |
|
}, |
|
"harness|hendrycksTest-college_medicine|5": { |
|
"acc": 0.21965317919075145, |
|
"acc_stderr": 0.031568093627031744, |
|
"acc_norm": 0.21965317919075145, |
|
"acc_norm_stderr": 0.031568093627031744 |
|
}, |
|
"harness|hendrycksTest-college_physics|5": { |
|
"acc": 0.23529411764705882, |
|
"acc_stderr": 0.04220773659171453, |
|
"acc_norm": 0.23529411764705882, |
|
"acc_norm_stderr": 0.04220773659171453 |
|
}, |
|
"harness|hendrycksTest-computer_security|5": { |
|
"acc": 0.23, |
|
"acc_stderr": 0.04229525846816505, |
|
"acc_norm": 0.23, |
|
"acc_norm_stderr": 0.04229525846816505 |
|
}, |
|
"harness|hendrycksTest-conceptual_physics|5": { |
|
"acc": 0.2680851063829787, |
|
"acc_stderr": 0.028957342788342347, |
|
"acc_norm": 0.2680851063829787, |
|
"acc_norm_stderr": 0.028957342788342347 |
|
}, |
|
"harness|hendrycksTest-econometrics|5": { |
|
"acc": 0.24561403508771928, |
|
"acc_stderr": 0.040493392977481404, |
|
"acc_norm": 0.24561403508771928, |
|
"acc_norm_stderr": 0.040493392977481404 |
|
}, |
|
"harness|hendrycksTest-electrical_engineering|5": { |
|
"acc": 0.2482758620689655, |
|
"acc_stderr": 0.036001056927277716, |
|
"acc_norm": 0.2482758620689655, |
|
"acc_norm_stderr": 0.036001056927277716 |
|
}, |
|
"harness|hendrycksTest-elementary_mathematics|5": { |
|
"acc": 0.24074074074074073, |
|
"acc_stderr": 0.0220190800122179, |
|
"acc_norm": 0.24074074074074073, |
|
"acc_norm_stderr": 0.0220190800122179 |
|
}, |
|
"harness|hendrycksTest-formal_logic|5": { |
|
"acc": 0.23015873015873015, |
|
"acc_stderr": 0.03764950879790605, |
|
"acc_norm": 0.23015873015873015, |
|
"acc_norm_stderr": 0.03764950879790605 |
|
}, |
|
"harness|hendrycksTest-global_facts|5": { |
|
"acc": 0.18, |
|
"acc_stderr": 0.038612291966536934, |
|
"acc_norm": 0.18, |
|
"acc_norm_stderr": 0.038612291966536934 |
|
}, |
|
"harness|hendrycksTest-high_school_biology|5": { |
|
"acc": 0.25483870967741934, |
|
"acc_stderr": 0.024790118459332208, |
|
"acc_norm": 0.25483870967741934, |
|
"acc_norm_stderr": 0.024790118459332208 |
|
}, |
|
"harness|hendrycksTest-high_school_chemistry|5": { |
|
"acc": 0.19704433497536947, |
|
"acc_stderr": 0.02798672466673622, |
|
"acc_norm": 0.19704433497536947, |
|
"acc_norm_stderr": 0.02798672466673622 |
|
}, |
|
"harness|hendrycksTest-high_school_computer_science|5": { |
|
"acc": 0.22, |
|
"acc_stderr": 0.041633319989322695, |
|
"acc_norm": 0.22, |
|
"acc_norm_stderr": 0.041633319989322695 |
|
}, |
|
"harness|hendrycksTest-high_school_european_history|5": { |
|
"acc": 0.19393939393939394, |
|
"acc_stderr": 0.0308741451365621, |
|
"acc_norm": 0.19393939393939394, |
|
"acc_norm_stderr": 0.0308741451365621 |
|
}, |
|
"harness|hendrycksTest-high_school_geography|5": { |
|
"acc": 0.3484848484848485, |
|
"acc_stderr": 0.033948539651564025, |
|
"acc_norm": 0.3484848484848485, |
|
"acc_norm_stderr": 0.033948539651564025 |
|
}, |
|
"harness|hendrycksTest-high_school_government_and_politics|5": { |
|
"acc": 0.32124352331606215, |
|
"acc_stderr": 0.033699508685490674, |
|
"acc_norm": 0.32124352331606215, |
|
"acc_norm_stderr": 0.033699508685490674 |
|
}, |
|
"harness|hendrycksTest-high_school_macroeconomics|5": { |
|
"acc": 0.23333333333333334, |
|
"acc_stderr": 0.021444547301560476, |
|
"acc_norm": 0.23333333333333334, |
|
"acc_norm_stderr": 0.021444547301560476 |
|
}, |
|
"harness|hendrycksTest-high_school_mathematics|5": { |
|
"acc": 0.2851851851851852, |
|
"acc_stderr": 0.027528599210340492, |
|
"acc_norm": 0.2851851851851852, |
|
"acc_norm_stderr": 0.027528599210340492 |
|
}, |
|
"harness|hendrycksTest-high_school_microeconomics|5": { |
|
"acc": 0.29831932773109243, |
|
"acc_stderr": 0.029719142876342856, |
|
"acc_norm": 0.29831932773109243, |
|
"acc_norm_stderr": 0.029719142876342856 |
|
}, |
|
"harness|hendrycksTest-high_school_physics|5": { |
|
"acc": 0.2781456953642384, |
|
"acc_stderr": 0.03658603262763744, |
|
"acc_norm": 0.2781456953642384, |
|
"acc_norm_stderr": 0.03658603262763744 |
|
}, |
|
"harness|hendrycksTest-high_school_psychology|5": { |
|
"acc": 0.26788990825688075, |
|
"acc_stderr": 0.018987462257978652, |
|
"acc_norm": 0.26788990825688075, |
|
"acc_norm_stderr": 0.018987462257978652 |
|
}, |
|
"harness|hendrycksTest-high_school_statistics|5": { |
|
"acc": 0.4351851851851852, |
|
"acc_stderr": 0.03381200005643525, |
|
"acc_norm": 0.4351851851851852, |
|
"acc_norm_stderr": 0.03381200005643525 |
|
}, |
|
"harness|hendrycksTest-high_school_us_history|5": { |
|
"acc": 0.2647058823529412, |
|
"acc_stderr": 0.0309645179269234, |
|
"acc_norm": 0.2647058823529412, |
|
"acc_norm_stderr": 0.0309645179269234 |
|
}, |
|
"harness|hendrycksTest-high_school_world_history|5": { |
|
"acc": 0.28270042194092826, |
|
"acc_stderr": 0.029312814153955927, |
|
"acc_norm": 0.28270042194092826, |
|
"acc_norm_stderr": 0.029312814153955927 |
|
}, |
|
"harness|hendrycksTest-human_aging|5": { |
|
"acc": 0.31390134529147984, |
|
"acc_stderr": 0.031146796482972465, |
|
"acc_norm": 0.31390134529147984, |
|
"acc_norm_stderr": 0.031146796482972465 |
|
}, |
|
"harness|hendrycksTest-human_sexuality|5": { |
|
"acc": 0.2595419847328244, |
|
"acc_stderr": 0.03844876139785271, |
|
"acc_norm": 0.2595419847328244, |
|
"acc_norm_stderr": 0.03844876139785271 |
|
}, |
|
"harness|hendrycksTest-international_law|5": { |
|
"acc": 0.2231404958677686, |
|
"acc_stderr": 0.03800754475228733, |
|
"acc_norm": 0.2231404958677686, |
|
"acc_norm_stderr": 0.03800754475228733 |
|
}, |
|
"harness|hendrycksTest-jurisprudence|5": { |
|
"acc": 0.25925925925925924, |
|
"acc_stderr": 0.042365112580946336, |
|
"acc_norm": 0.25925925925925924, |
|
"acc_norm_stderr": 0.042365112580946336 |
|
}, |
|
"harness|hendrycksTest-logical_fallacies|5": { |
|
"acc": 0.25153374233128833, |
|
"acc_stderr": 0.03408997886857529, |
|
"acc_norm": 0.25153374233128833, |
|
"acc_norm_stderr": 0.03408997886857529 |
|
}, |
|
"harness|hendrycksTest-machine_learning|5": { |
|
"acc": 0.29464285714285715, |
|
"acc_stderr": 0.043270409325787296, |
|
"acc_norm": 0.29464285714285715, |
|
"acc_norm_stderr": 0.043270409325787296 |
|
}, |
|
"harness|hendrycksTest-management|5": { |
|
"acc": 0.17475728155339806, |
|
"acc_stderr": 0.037601780060266224, |
|
"acc_norm": 0.17475728155339806, |
|
"acc_norm_stderr": 0.037601780060266224 |
|
}, |
|
"harness|hendrycksTest-marketing|5": { |
|
"acc": 0.20085470085470086, |
|
"acc_stderr": 0.026246772946890488, |
|
"acc_norm": 0.20085470085470086, |
|
"acc_norm_stderr": 0.026246772946890488 |
|
}, |
|
"harness|hendrycksTest-medical_genetics|5": { |
|
"acc": 0.3, |
|
"acc_stderr": 0.046056618647183814, |
|
"acc_norm": 0.3, |
|
"acc_norm_stderr": 0.046056618647183814 |
|
}, |
|
"harness|hendrycksTest-miscellaneous|5": { |
|
"acc": 0.23499361430395913, |
|
"acc_stderr": 0.01516202415227844, |
|
"acc_norm": 0.23499361430395913, |
|
"acc_norm_stderr": 0.01516202415227844 |
|
}, |
|
"harness|hendrycksTest-moral_disputes|5": { |
|
"acc": 0.23699421965317918, |
|
"acc_stderr": 0.02289408248992599, |
|
"acc_norm": 0.23699421965317918, |
|
"acc_norm_stderr": 0.02289408248992599 |
|
}, |
|
"harness|hendrycksTest-moral_scenarios|5": { |
|
"acc": 0.23798882681564246, |
|
"acc_stderr": 0.014242630070574915, |
|
"acc_norm": 0.23798882681564246, |
|
"acc_norm_stderr": 0.014242630070574915 |
|
}, |
|
"harness|hendrycksTest-nutrition|5": { |
|
"acc": 0.23202614379084968, |
|
"acc_stderr": 0.024170840879341005, |
|
"acc_norm": 0.23202614379084968, |
|
"acc_norm_stderr": 0.024170840879341005 |
|
}, |
|
"harness|hendrycksTest-philosophy|5": { |
|
"acc": 0.1864951768488746, |
|
"acc_stderr": 0.02212243977248077, |
|
"acc_norm": 0.1864951768488746, |
|
"acc_norm_stderr": 0.02212243977248077 |
|
}, |
|
"harness|hendrycksTest-prehistory|5": { |
|
"acc": 0.24074074074074073, |
|
"acc_stderr": 0.02378858355165854, |
|
"acc_norm": 0.24074074074074073, |
|
"acc_norm_stderr": 0.02378858355165854 |
|
}, |
|
"harness|hendrycksTest-professional_accounting|5": { |
|
"acc": 0.2695035460992908, |
|
"acc_stderr": 0.026469036818590627, |
|
"acc_norm": 0.2695035460992908, |
|
"acc_norm_stderr": 0.026469036818590627 |
|
}, |
|
"harness|hendrycksTest-professional_law|5": { |
|
"acc": 0.2529335071707953, |
|
"acc_stderr": 0.011102268713839989, |
|
"acc_norm": 0.2529335071707953, |
|
"acc_norm_stderr": 0.011102268713839989 |
|
}, |
|
"harness|hendrycksTest-professional_medicine|5": { |
|
"acc": 0.4411764705882353, |
|
"acc_stderr": 0.030161911930767102, |
|
"acc_norm": 0.4411764705882353, |
|
"acc_norm_stderr": 0.030161911930767102 |
|
}, |
|
"harness|hendrycksTest-professional_psychology|5": { |
|
"acc": 0.25, |
|
"acc_stderr": 0.01751781884501444, |
|
"acc_norm": 0.25, |
|
"acc_norm_stderr": 0.01751781884501444 |
|
}, |
|
"harness|hendrycksTest-public_relations|5": { |
|
"acc": 0.21818181818181817, |
|
"acc_stderr": 0.03955932861795833, |
|
"acc_norm": 0.21818181818181817, |
|
"acc_norm_stderr": 0.03955932861795833 |
|
}, |
|
"harness|hendrycksTest-security_studies|5": { |
|
"acc": 0.20408163265306123, |
|
"acc_stderr": 0.025801283475090506, |
|
"acc_norm": 0.20408163265306123, |
|
"acc_norm_stderr": 0.025801283475090506 |
|
}, |
|
"harness|hendrycksTest-sociology|5": { |
|
"acc": 0.24378109452736318, |
|
"acc_stderr": 0.03036049015401465, |
|
"acc_norm": 0.24378109452736318, |
|
"acc_norm_stderr": 0.03036049015401465 |
|
}, |
|
"harness|hendrycksTest-us_foreign_policy|5": { |
|
"acc": 0.24, |
|
"acc_stderr": 0.04292346959909281, |
|
"acc_norm": 0.24, |
|
"acc_norm_stderr": 0.04292346959909281 |
|
}, |
|
"harness|hendrycksTest-virology|5": { |
|
"acc": 0.22289156626506024, |
|
"acc_stderr": 0.03240004825594687, |
|
"acc_norm": 0.22289156626506024, |
|
"acc_norm_stderr": 0.03240004825594687 |
|
}, |
|
"harness|hendrycksTest-world_religions|5": { |
|
"acc": 0.3216374269005848, |
|
"acc_stderr": 0.03582529442573122, |
|
"acc_norm": 0.3216374269005848, |
|
"acc_norm_stderr": 0.03582529442573122 |
|
}, |
|
"harness|truthfulqa:mc|0": { |
|
"mc1": 0.24112607099143207, |
|
"mc1_stderr": 0.014974827279752334, |
|
"mc2": 0.3762297840067963, |
|
"mc2_stderr": 0.01445991036363257 |
|
}, |
|
"harness|winogrande|5": { |
|
"acc": 0.5153906866614049, |
|
"acc_stderr": 0.014045826789783668 |
|
}, |
|
"harness|gsm8k|5": { |
|
"acc": 0.006823351023502654, |
|
"acc_stderr": 0.0022675371022544823 |
|
} |
|
} |
|
|
|
|
|
``` |
