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- README.md +0 -156
- data/train-00000-of-00001.parquet → default/train/0000.parquet +0 -0
- model_scores.png +0 -3
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default/train/0000.parquet filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: apache-2.0
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dataset_info:
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features:
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- name: task_id
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dtype: string
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- name: prompt
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dtype: string
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- name: entry_point
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dtype: string
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- name: test
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dtype: string
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- name: description
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dtype: string
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- name: language
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dtype: string
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- name: canonical_solution
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sequence: string
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splits:
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- name: train
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num_bytes: 505355
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num_examples: 161
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download_size: 174830
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dataset_size: 505355
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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---
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# Benchmark summary
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We introduce HumanEval for Kotlin, created from scratch by human experts.
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Solutions and tests for all 161 HumanEval tasks are written by an expert olympiad programmer with 6 years of experience in Kotlin, and independently checked by a programmer with 4 years of experience in Kotlin.
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The tests we implement are eqivalent to the original HumanEval tests for Python.
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# How to use
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The benchmark is prepared in a format suitable for MXEval and can be easily integrated into the MXEval pipeline.
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When testing models on this benchmark, during the code generation step we use early stopping on the `}\n}` sequence to expedite the process. We also perform some code post-processing before evaluation — specifically, we remove all comments and signatures.
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The code for running an example model on the benchmark using the early stopping and post-processing is available below.
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```python
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import json
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import re
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from datasets import load_dataset
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import jsonlines
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import torch
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from transformers import (
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AutoTokenizer,
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AutoModelForCausalLM,
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StoppingCriteria,
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StoppingCriteriaList,
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)
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from tqdm import tqdm
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from mxeval.evaluation import evaluate_functional_correctness
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class StoppingCriteriaSub(StoppingCriteria):
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def __init__(self, stops, tokenizer):
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(StoppingCriteria.__init__(self),)
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self.stops = rf"{stops}"
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self.tokenizer = tokenizer
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def __call__(
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self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
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) -> bool:
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last_three_tokens = [int(x) for x in input_ids.data[0][-3:]]
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decoded_last_three_tokens = self.tokenizer.decode(last_three_tokens)
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return bool(re.search(self.stops, decoded_last_three_tokens))
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def generate(problem):
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criterion = StoppingCriteriaSub(stops="\n}\n", tokenizer=tokenizer)
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stopping_criteria = StoppingCriteriaList([criterion])
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problem = tokenizer.encode(problem, return_tensors="pt").to('cuda')
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sample = model.generate(
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problem,
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max_new_tokens=256,
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min_new_tokens=128,
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pad_token_id=tokenizer.eos_token_id,
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do_sample=False,
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num_beams=1,
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stopping_criteria=stopping_criteria,
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)
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answer = tokenizer.decode(sample[0], skip_special_tokens=True)
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return answer
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def clean_asnwer(code):
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# Clean comments
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code_without_line_comments = re.sub(r"//.*", "", code)
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code_without_all_comments = re.sub(
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r"/\*.*?\*/", "", code_without_line_comments, flags=re.DOTALL
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)
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#Clean signatures
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lines = code.split("\n")
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for i, line in enumerate(lines):
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if line.startswith("fun "):
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return "\n".join(lines[i + 1:])
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return code
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model_name = "JetBrains/CodeLlama-7B-Kexer"
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dataset = load_dataset("jetbrains/Kotlin_HumanEval")['train']
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problem_dict = {problem['task_id']: problem for problem in dataset}
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to('cuda')
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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output = []
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for key in tqdm(list(problem_dict.keys()), leave=False):
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problem = problem_dict[key]["prompt"]
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answer = generate(problem)
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answer = clean_asnwer(answer)
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output.append({"task_id": key, "completion": answer, "language": "kotlin"})
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output_file = f"answers"
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with jsonlines.open(output_file, mode="w") as writer:
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for line in output:
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writer.write(line)
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evaluate_functional_correctness(
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sample_file=output_file,
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k=[1],
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n_workers=16,
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timeout=15,
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problem_file=problem_dict,
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)
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with open(output_file + '_results.jsonl') as fp:
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total = 0
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correct = 0
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for line in fp:
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sample_res = json.loads(line)
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print(sample_res)
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total += 1
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correct += sample_res['passed']
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print(f'Pass rate: {correct/total}')
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```
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# Results
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We evaluated multiple coding models using this benchmark, and the results are presented in the figure below:
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![results](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval/resolve/main/model_scores.png)
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data/train-00000-of-00001.parquet → default/train/0000.parquet
RENAMED
File without changes
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model_scores.png
DELETED
Git LFS Details
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