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AceMath-7B-Instruct
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# AceMath-7B-Instruct ---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-generation
tags:
- nvidia
- AceMath
- math
- CoT
- pytorch
---
AceMath-7B-Instruct
## Introduction
We introduce AceMath, a family of frontier models designed for mathematical reasoning. The models in AceMath family, including AceMath-1.5B/7B/72B-Instruct and AceMath-7B/72B-RM, are <b>Improved using Qwen</b>.
The AceMath-1.5B/7B/72B-Instruct models excel at solving English mathematical problems using Chain-of-Thought (CoT) reasoning, while the AceMath-7B/72B-RM models, as outcome reward models, specialize in evaluating and scoring mathematical solutions.
The AceMath-1.5B/7B/72B-Instruct models are developed from the Qwen2.5-Math-1.5B/7B/72B-Base models, leveraging a multi-stage supervised fine-tuning (SFT) process: first with general-purpose SFT data, followed by math-specific SFT data. We are releasing all training data to support further research in this field.
We only recommend using the AceMath models for solving math problems. To support other tasks, we also release AceInstruct-1.5B/7B/72B, a series of general-purpose SFT models designed to handle code, math, and general knowledge tasks. These models are built upon the Qwen2.5-1.5B/7B/72B-Base.
For more information about AceMath, check our [website](https://research.nvidia.com/labs/adlr/acemath/) and [paper](https://arxiv.org/abs/2412.15084).
## All Resources
### AceMath Instruction Models
- [AceMath-1.5B-Instruct](https://huggingface.co/nvidia/AceMath-1.5B-Instruct), [AceMath-7B-Instruct](https://huggingface.co/nvidia/AceMath-7B-Instruct), [AceMath-72B-Instruct](https://huggingface.co/nvidia/AceMath-72B-Instruct)
### AceMath Reward Models
- [AceMath-7B-RM](https://huggingface.co/nvidia/AceMath-7B-RM), [AceMath-72B-RM](https://huggingface.co/nvidia/AceMath-72B-RM)
### Evaluation & Training Data
- [AceMath-RewardBench](https://huggingface.co/datasets/nvidia/AceMath-RewardBench), [AceMath-Instruct Training Data](https://huggingface.co/datasets/nvidia/AceMath-Instruct-Training-Data), [AceMath-RM Training Data](https://huggingface.co/datasets/nvidia/AceMath-RM-Training-Data)
### General Instruction Models
- [AceInstruct-1.5B](https://huggingface.co/nvidia/AceInstruct-1.5B), [AceInstruct-7B](https://huggingface.co/nvidia/AceInstruct-7B), [AceInstruct-72B](https://huggingface.co/nvidia/AceInstruct-72B)
## Benchmark Results (AceMath-Instruct + AceMath-72B-RM)
<p align="center">
<img src="./acemath-pic.png" alt="AceMath Benchmark Results" width="800">
</p>
We compare AceMath to leading proprietary and open-access math models in above Table. Our AceMath-7B-Instruct, largely outperforms the previous best-in-class Qwen2.5-Math-7B-Instruct (Average pass@1: 67.2 vs. 62.9) on a variety of math reasoning benchmarks, while coming close to the performance of 10× larger Qwen2.5-Math-72B-Instruct (67.2 vs. 68.2). Notably, our AceMath-72B-Instruct outperforms the state-of-the-art Qwen2.5-Math-72B-Instruct (71.8 vs. 68.2), GPT-4o (67.4) and Claude 3.5 Sonnet (65.6) by a margin. We also report the rm@8 accuracy (best of 8) achieved by our reward model, AceMath-72B-RM, which sets a new record on these reasoning benchmarks. This excludes OpenAI’s o1 model, which relies on scaled inference computation.
## How to use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "nvidia/AceMath-7B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
prompt = "Jen enters a lottery by picking $4$ distinct numbers from $S=\\{1,2,3,\\cdots,9,10\\}.$ $4$ numbers are randomly chosen from $S.$ She wins a prize if at least two of her numbers were $2$ of the randomly chosen numbers, and wins the grand prize if all four of her numbers were the randomly chosen numbers. The probability of her winning the grand prize given that she won a prize is $\\tfrac{m}{n}$ where $m$ and $n$ are relatively prime positive integers. Find $m+n$."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to("cuda")
generated_ids = model.generate(
**model_inputs,
max_new_tokens=2048
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
## Correspondence to
Zihan Liu (zihanl@nvidia.com), Yang Chen (yachen@nvidia.com), Wei Ping (wping@nvidia.com)
## Citation
If you find our work helpful, we’d appreciate it if you could cite us.
<pre>
@article{acemath2024,
title={AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling},
author={Liu, Zihan and Chen, Yang and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei},
journal={arXiv preprint},
year={2024}
}
</pre>
## License
All models in the AceMath family are for non-commercial use only, subject to [Terms of Use](https://openai.com/policies/row-terms-of-use/) of the data generated by OpenAI. We put the AceMath models under the license of [Creative Commons Attribution: Non-Commercial 4.0 International](https://spdx.org/licenses/CC-BY-NC-4.0).

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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151643,
"hidden_act": "silu",
"hidden_size": 3584,
"initializer_range": 0.02,
"intermediate_size": 18944,
"max_position_embeddings": 4096,
"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"rms_norm_eps": 1e-06,
"rope_theta": 10000,
"sliding_window": 4096,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.44.0",
"use_cache": true,
"use_mrope": false,
"use_sliding_window": false,
"vocab_size": 152064
}

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## Introduction
This is the evaluation script used to reproduce math benchmarks scores for AceMath-1.5B/7B/72B-Instruct models based on their outputs. The benchmark can be downloaded from [Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math/tree/main/evaluation/data).
## Calculate Scores
```console
python calculate_scores.py
```

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from grader import is_equal
import json
import re
def get_gold_list(datapath, dataset_name):
assert dataset_name in ["gsm8k", "math", "minerva_math", "gaokao2023en", "olympiadbench", "collegemath"]
gold_list = []
with open(datapath, "r") as f:
for line in f:
item = json.loads(line)
if dataset_name == "gsm8k":
gold = item['answer'].split("#### ")[-1]
elif dataset_name == "math":
gold = item['answer']
elif dataset_name == "minerva_math":
pattern = r"\\boxed\{((?:[^{}]|\{(?:[^{}]|\{[^{}]*\})*\})*)\}"
pattern_re = re.compile(pattern, re.DOTALL)
solution = item['solution']
matches = pattern_re.findall(solution)
if len(matches) == 0:
gold = None
else:
gold = matches[-1]
elif dataset_name == "gaokao2023en":
gold = re.sub(r'^\$(.*)\$$', r'\1', item['answer'])
elif dataset_name == "olympiadbench":
gold = re.sub(r'^\$(.*)\$$', r'\1', item['final_answer'][0])
elif dataset_name == "collegemath":
gold = re.sub(r'^\$(.*)\$$', r'\1', item['answer'])
gold_list.append(gold)
return gold_list
def get_scores_on_math_benchmarks(model_output_path, test_gold_path, dataset_name):
gold_list = get_gold_list(test_gold_path, dataset_name)
"""TODO
Get the output_list from model_output_path
output_list is a list of string (List[str])
Each string represents the model's response for a corresponding question in the benchmark
Therefore, the length of output_list must match the length of gold_list.
output_list = ...
"""
correct = 0
for output, gold in zip(output_list, gold_list):
if is_equal(output, gold, dataset_name):
correct += 1
print("accuracy on %s is %.4f" % (dataset_name, correct / len(gold_list)))
if __name__ == "__main__":
"""TODO
Download test benchmarks from Qwen2.5-Math
https://github.com/QwenLM/Qwen2.5-Math/tree/main/evaluation/data
Prepare model_output_path and test_gold_path for each dataset
"""
test_gold_path = "PATH_OF_THE_BENCHMARK"
model_output_path = "PATH_OF_YOUR_MODEL_OUTPUTS"
dataset_name = "DATASET_NAME" # e.g., gsm8k, math, "minerva_math", "gaokao2023en", "olympiadbench", "collegemath"
get_scores_on_math_benchmarks(model_output_path, test_gold_path, dataset_name)

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"""
This script is adapted from Qwen2.5-Math
https://github.com/QwenLM/Qwen2.5-Math/blob/main/evaluation/grader.py
"""
import re
import regex
import multiprocessing
from math import isclose
from typing import Union
from collections import defaultdict
from sympy import simplify, N
from sympy.parsing.sympy_parser import parse_expr
from sympy.parsing.latex import parse_latex
def latex2sympy(sympy: str, variable_values={}):
# record frac
global frac_type
if sympy.find(r'\frac') != -1:
frac_type = r'\frac'
if sympy.find(r'\dfrac') != -1:
frac_type = r'\dfrac'
if sympy.find(r'\tfrac') != -1:
frac_type = r'\tfrac'
sympy = sympy.replace(r'\dfrac', r'\frac')
sympy = sympy.replace(r'\tfrac', r'\frac')
# Translate Transpose
sympy = sympy.replace(r'\mathrm{T}', 'T', -1)
# Translate Derivative
sympy = sympy.replace(r'\mathrm{d}', 'd', -1).replace(r'{\rm d}', 'd', -1)
# Translate Matrix
sympy = sympy.replace(r'\left[\begin{matrix}', r'\begin{bmatrix}', -1).replace(r'\end{matrix}\right]', r'\end{bmatrix}', -1)
# Translate Permutation
sympy = re.sub(r"\(([a-zA-Z0-9+\-*/\\ ]+?)\)_{([a-zA-Z0-9+\-*/\\ ]+?)}", r"\\frac{(\1)!}{((\1)-(\2))!}", sympy)
# Remove \displaystyle
sympy = sympy.replace(r'\displaystyle', ' ', -1)
# Remove \quad
sympy = sympy.replace(r'\quad', ' ', -1).replace(r'\qquad', ' ', -1).replace(r'~', ' ', -1).replace(r'\,', ' ', -1)
# Remove $
sympy = sympy.replace(r'$', ' ', -1)
# variable values
global VARIABLE_VALUES
if len(variable_values) > 0:
VARIABLE_VALUES = variable_values
else:
VARIABLE_VALUES = {}
# setup listener
matherror = MathErrorListener(sympy)
# stream input
stream = InputStream(sympy)
lex = PSLexer(stream)
lex.removeErrorListeners()
lex.addErrorListener(matherror)
tokens = CommonTokenStream(lex)
parser = PSParser(tokens)
# remove default console error listener
parser.removeErrorListeners()
parser.addErrorListener(matherror)
# process the input
return_data = None
math = parser.math()
# if a list
if math.relation_list():
return_data = []
# go over list items
relation_list = math.relation_list().relation_list_content()
for list_item in relation_list.relation():
expr = convert_relation(list_item)
return_data.append(expr)
# if not, do default
else:
relation = math.relation()
return_data = convert_relation(relation)
return return_data
def math_answer_cleaning(answer, dataset_name):
"""
remove irrelevant strings and unify the answer format before checking whether the answers are equal
"""
def _is_completely_wrapped_by_text(input_string):
pattern = r'^\\text{(.*)}$'
match = re.match(pattern, input_string)
if match:
## input_string is completely wrapped by \text{}
extracted_content = match.group(1)
extracted_content = extracted_content.replace("(", "").replace(")", "").replace(",", "")
return extracted_content
else:
return None
## remove irrelevant \\text and space
extracted_content = _is_completely_wrapped_by_text(answer)
answer = extracted_content if extracted_content else answer
## e.g., convert 5,\!460 into 5460; convert 14{,}916 into 14916 convert \$4 into 4
answer = answer.replace(",\!", "").replace("{,}", "").replace("\$", "")
## e.g., convert \dfrac{3}{2} into frac{3}{2}
answer = answer.replace("dfrac{", "frac{").replace("tfrac{", "frac{")
## e.g., convert 121^\circ into 121
answer = answer.replace("^\circ", "")
answer = answer.replace("^{\circ}", "")
## remove \quad
answer = answer.replace("\quad", "")
## remove space
answer = answer.replace(" ", "")
## remove \n
answer = answer.replace("\n", "").replace("\\n", "")
## e.g., convert 3.54\times10^{10} into 3.54e10
answer = re.sub(r'([+-]?\d*\.?\d+)[\\]times10\^{([+-]?\d+)}', r'\1e\2', answer)
## e.g., convert 3.54\times10^10 into 3.54e10
answer = re.sub(r'([+-]?\d*\.?\d+)[\\]times10\^([+-]?\d+)', r'\1e\2', answer)
## e.g., convert 558\,\text{nm} into 558
answer = re.sub(r'\\,\\text\{.*?\}', '', answer)
## e.g., convert 558\text{nm} into 558
answer = re.sub(r'\\text\{.*?\}', '', answer)
## e.g., convert 2^{10} into 2^10
answer = re.sub(r'(\d+)\^{(\d+)}', r'\1^\2', answer)
## lowercase
answer = answer.lower()
if dataset_name == "collegemath":
## convert 558\mathrm{ft} into 558
answer = re.sub(r'\\mathrm\{.*?\}', '', answer)
## clean noisy answer
answer = re.sub(r'\$\([^)]*\)', '', answer)
if answer.endswith("-"):
answer = answer[:-1]
if answer.endswith("."):
answer = answer[:-1]
if answer.endswith("hours"):
answer = answer[:-len("hours")]
## extract final answer after '=' or ':'
if "=" in answer:
answer = answer.split("=", 1)[1]
if ":" in answer:
answer = answer.split(":", 1)[1]
## \emptyset and \oslash both reprsent empty set in latex
answer = answer.replace("\\emptyset", "\\oslash")
if dataset_name == "gsm8k":
# Example: 5,600 -> 5600
answer = answer.replace(',', '')
if dataset_name == "gaokao2023en":
unit_strings = ['students', 'dollars', 'boxes', 'feet', 'kilometers', 'meters', 'degreesontheBreadusscale', '$', 'a.m.', 'am', 'minutes']
for unit in unit_strings:
answer = answer.replace(unit, "")
return answer
def extract_final_answer(output):
pattern_re = re.compile(r"\\boxed\{((?:[^{}]|\{(?:[^{}]|\{[^{}]*\})*\})*)\}", re.DOTALL)
all_matches = pattern_re.findall(output)
if len(all_matches) >= 1:
extracted_answer = all_matches[-1]
else:
extracted_answer = None
return extracted_answer, all_matches
def round_number(answer):
def _is_float(string):
try:
float(string)
return True
except:
return False
if _is_float(answer) and float(answer) < 1:
## to consider the case like 5.56e-10 (convert 5.56e-10 into 5.6e-10)
## still return a string type
return f"{float(answer):.2g}"
return answer
def choice_answer_clean(pred: str):
pred = pred.strip("\n").rstrip(".").rstrip("/").strip(" ").lstrip(":")
# Clean the answer based on the dataset
tmp = re.findall(r"\b(A|B|C|D|E)\b", pred.upper())
if tmp:
pred = tmp
else:
pred = [pred.strip().strip(".")]
pred = pred[-1]
# Remove the period at the end, again!
pred = pred.rstrip(".").rstrip("/")
return pred
def parse_digits(num):
num = regex.sub(",", "", str(num))
try:
return float(num)
except:
if num.endswith("%"):
num = num[:-1]
if num.endswith("\\"):
num = num[:-1]
try:
return float(num) / 100
except:
pass
return None
def is_digit(num):
# paired with parse_digits
return parse_digits(num) is not None
def str_to_pmatrix(input_str):
input_str = input_str.strip()
matrix_str = re.findall(r"\{.*,.*\}", input_str)
pmatrix_list = []
for m in matrix_str:
m = m.strip("{}")
pmatrix = r"\begin{pmatrix}" + m.replace(",", "\\") + r"\end{pmatrix}"
pmatrix_list.append(pmatrix)
return ", ".join(pmatrix_list)
def math_equal(
prediction: Union[bool, float, str],
reference: Union[float, str],
include_percentage: bool = True,
is_close: bool = True,
timeout: bool = False,
) -> bool:
"""
Exact match of math if and only if:
1. numerical equal: both can convert to float and are equal
2. symbolic equal: both can convert to sympy expression and are equal
"""
if prediction is None or reference is None:
return False
if str(prediction.strip().lower()) == str(reference.strip().lower()):
return True
if (
reference in ["A", "B", "C", "D", "E"]
and choice_answer_clean(prediction) == reference
):
return True
# fraction equal
if fraction_equal(prediction, reference):
return True
try: # numerical equal
if round_number(prediction) == round_number(reference):
return True
if is_digit(prediction) and is_digit(reference):
prediction = parse_digits(prediction)
reference = parse_digits(reference)
# number questions
if include_percentage:
gt_result = [reference / 100, reference, reference * 100]
else:
gt_result = [reference]
for item in gt_result:
try:
if is_close:
if numeric_equal(prediction, item):
return True
else:
if item == prediction:
return True
except Exception:
continue
return False
except:
pass
if not prediction and prediction not in [0, False]:
return False
# symbolic equal
reference = str(reference).strip()
prediction = str(prediction).strip()
## pmatrix (amps)
if "pmatrix" in prediction and not "pmatrix" in reference:
reference = str_to_pmatrix(reference)
## deal with [], (), {}
pred_str, ref_str = prediction, reference
if (
prediction.startswith("[")
and prediction.endswith("]")
and not reference.startswith("(")
) or (
prediction.startswith("(")
and prediction.endswith(")")
and not reference.startswith("[")
):
pred_str = pred_str.strip("[]()")
ref_str = ref_str.strip("[]()")
for s in ["{", "}", "(", ")"]:
ref_str = ref_str.replace(s, "")
pred_str = pred_str.replace(s, "")
if pred_str.lower() == ref_str.lower():
return True
## [a, b] vs. [c, d], return a==c and b==d
if (
regex.match(r"(\(|\[).+(\)|\])", prediction) is not None
and regex.match(r"(\(|\[).+(\)|\])", reference) is not None
):
pred_parts = prediction[1:-1].split(",")
ref_parts = reference[1:-1].split(",")
if len(pred_parts) == len(ref_parts):
if all(
[
math_equal(
pred_parts[i], ref_parts[i], include_percentage, is_close
)
for i in range(len(pred_parts))
]
):
return True
if (
(
prediction.startswith("\\begin{pmatrix}")
or prediction.startswith("\\begin{bmatrix}")
)
and (
prediction.endswith("\\end{pmatrix}")
or prediction.endswith("\\end{bmatrix}")
)
and (
reference.startswith("\\begin{pmatrix}")
or reference.startswith("\\begin{bmatrix}")
)
and (
reference.endswith("\\end{pmatrix}") or reference.endswith("\\end{bmatrix}")
)
):
pred_lines = [
line.strip()
for line in prediction[
len("\\begin{pmatrix}") : -len("\\end{pmatrix}")
].split("\\\\")
if line.strip()
]
ref_lines = [
line.strip()
for line in reference[
len("\\begin{pmatrix}") : -len("\\end{pmatrix}")
].split("\\\\")
if line.strip()
]
matched = True
if len(pred_lines) == len(ref_lines):
for pred_line, ref_line in zip(pred_lines, ref_lines):
pred_parts = pred_line.split("&")
ref_parts = ref_line.split("&")
if len(pred_parts) == len(ref_parts):
if not all(
[
math_equal(
pred_parts[i],
ref_parts[i],
include_percentage,
is_close,
)
for i in range(len(pred_parts))
]
):
matched = False
break
else:
matched = False
if not matched:
break
else:
matched = False
if matched:
return True
if prediction.count("=") == 1 and reference.count("=") == 1:
pred = prediction.split("=")
pred = f"{pred[0].strip()} - ({pred[1].strip()})"
ref = reference.split("=")
ref = f"{ref[0].strip()} - ({ref[1].strip()})"
if symbolic_equal(pred, ref) or symbolic_equal(f"-({pred})", ref):
return True
elif (
prediction.count("=") == 1
and len(prediction.split("=")[0].strip()) <= 2
and "=" not in reference
):
if math_equal(
prediction.split("=")[1], reference, include_percentage, is_close
):
return True
elif (
reference.count("=") == 1
and len(reference.split("=")[0].strip()) <= 2
and "=" not in prediction
):
if math_equal(
prediction, reference.split("=")[1], include_percentage, is_close
):
return True
# symbolic equal with sympy
if timeout:
if call_with_timeout(symbolic_equal_process, prediction, reference):
return True
else:
if symbolic_equal(prediction, reference):
return True
return False
def numeric_equal(prediction: float, reference: float):
# Note that relative tolerance has significant impact
# on the result of the synthesized GSM-Hard dataset
# if reference.is_integer():
# return isclose(reference, round(prediction), abs_tol=1e-4)
# else:
# prediction = round(prediction, len(str(reference).split(".")[-1]))
return isclose(reference, prediction, rel_tol=1e-4)
def fraction_equal(prediction, reference):
def _calculate_numbers(input_string):
try:
result = eval(input_string)
return result
except:
return None
reference = re.sub(r'\\frac{(.*?)}{(.*?)}', r'(\1/\2)', reference)
prediction = re.sub(r'\\frac{(.*?)}{(.*?)}', r'(\1/\2)', prediction)
if reference == prediction:
return True
reference = _calculate_numbers(reference)
prediction = _calculate_numbers(prediction)
if reference and reference == prediction:
return True
return False
def symbolic_equal(a, b):
def _parse(s):
for f in [parse_latex, parse_expr, latex2sympy]:
try:
return f(s.replace("\\\\", "\\"))
except:
try:
return f(s)
except:
pass
return s
a = _parse(a)
b = _parse(b)
# direct equal
try:
if str(a) == str(b) or a == b:
return True
except:
pass
# simplify equal
try:
if a.equals(b) or simplify(a - b) == 0:
return True
except:
pass
# equation equal
try:
if (abs(a.lhs - a.rhs)).equals(abs(b.lhs - b.rhs)):
return True
except:
pass
try:
if numeric_equal(float(N(a)), float(N(b))):
return True
except:
pass
# matrix
try:
# if a and b are matrix
if a.shape == b.shape:
_a = a.applyfunc(lambda x: round(x, 3))
_b = b.applyfunc(lambda x: round(x, 3))
if _a.equals(_b):
return True
except:
pass
return False
def symbolic_equal_process(a, b, output_queue):
result = symbolic_equal(a, b)
output_queue.put(result)
def math_equal_process(prediction, reference, output_queue):
result = math_equal(prediction, reference, timeout=True)
output_queue.put(result)
def call_with_timeout(func, *args, timeout=1, **kwargs):
output_queue = multiprocessing.Queue()
process_args = args + (output_queue,)
process = multiprocessing.Process(target=func, args=process_args, kwargs=kwargs)
process.start()
process.join(timeout)
if process.is_alive():
process.terminate()
process.join()
return False
return output_queue.get()
def check_correctness_of_multiple_answer_cases(prediction, reference, all_matches):
if prediction.replace(",", "").replace("$", "") == reference.replace(",", "").replace("$", ""):
return True
if not prediction.split("=")[-1] == reference.split("=")[-1].replace("$", ""):
return False
if "," in reference or "or" in reference or "and" in reference:
## there are multiple answers
if len(all_matches) <= 1:
return False
prediction1 = prediction.split("=")[-1]
prediction2 = all_matches[-2].split("=")[-1]
reference = reference.replace("$", "")
if "or" in reference:
gold_list = reference.split("or", 1)
elif "and" in reference:
gold_list = reference.split("and", 1)
else:
gold_list = reference.split(",", 1)
reference1 = gold_list[-1].split("=")[-1]
reference2 = gold_list[-2].split("=")[-1]
if math_equal(prediction1, reference1) and math_equal(prediction2, reference2):
return True
elif math_equal(prediction2, reference1) and math_equal(prediction1, reference2):
return True
return False
else:
return True
def is_equal(model_output, reference, dataset_name):
extracted_model_answer, all_matches = extract_final_answer(model_output)
if extracted_model_answer is None or reference is None:
return False
extracted_model_answer = math_answer_cleaning(extracted_model_answer, dataset_name)
reference = math_answer_cleaning(reference, dataset_name)
# if math_equal(prediction, reference, timeout=True):
if call_with_timeout(math_equal_process, extracted_model_answer, reference):
return True
if dataset_name == "collegemath":
return check_correctness_of_multiple_answer_cases(extracted_model_answer, reference, all_matches)
return False

7
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{
"bos_token_id": 151643,
"do_sample": false,
"eos_token_id": 151643,
"max_new_tokens": 2048,
"transformers_version": "4.37.0"
}

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model-00004-of-00004.safetensors (Stored with Git LFS) Normal file
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