Claude 2 is a general-purpose large language model (LLM), and the most capable system released by Anthropic to date. And Claude 2 scored 76. 4 % percent 77. Typically, in the initial stage of program implementation, a. by removing non-empty lines of canonical solutions of HumanEval [Chen et al. EvalPlus transforms HumanEval to HumanEval + by adding 81 × unique test-cases and fixing incorrect ground-truth solutions from HumanEval. metallicamax • 6 mo. 5 (ChatGPT) at analyzing Solidity, it is still missing key features, such as the ability to reason about cross-function reentrancy and inter-function relationships in general. 6 test cases allocated to each problem. 6) or many other models specifically designed for coding. Please refer to the paper for more details. 3. Codex-002: 57. We evaluated the models based on compilation rates, test correctness, coverage, and test smells. 2 Scaling of Capabilities on HumanEval Having a sense of the capabilities of a model before training can improve decisions around alignment, safety, and deployment. We select the problem below and see how CodeParrot 🦜 (110M) performs and which code completions pass the unit tests:. Following the release of Codex and the HumanEval dataset (Chen et al. See below and the paper for information on the benchmarks available. This dataset contains 164 problems. Furthermore, we find that repeated sampling from the model is a. SE] 14 Jun 2022Improved coding skills — Claude 2 scored a 71. In a Python coding test called Codex HumanEval, Claude Instant 1. The performance degradation observed for these. The proposed Codex solves 28. On GSM8k, a set of grade-school math problems. Code generation models based on the pre-training and fine-tuning paradigm have been increasingly attempted by both academia and industry, resulting in well-known industrial models such as Codex, CodeGen, and PanGu-Coder. Masked Identifier Prediction (MIP). 5% on the multiple-choice section of the Bar exam. Codex 模型参数从12M到12B不等，是目前最强的编程语言预训练模型。Codex 能够帮助程序员根据函数名和注释自动补全代码、直接生成代码、自动补充测试样例，并支持多种编程语言。本期 Azure OpenAI 官方指南将详解 Codex 的模型结构如何帮助程序员实现自动代码生成。 We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. arXiv:2206. The evaluation covered a wide range of programming languages and yielded impressive results, helping to quantify the model’s performance in. Each problem included a function signature, docstring, body, and multiple unit tests, with an average of 7. - Claude 2 scored a 71. 8% of the problems, and Codex-S (further fine-tuned on correctly implemented standalone functions) solves 37. Codex 模型参数从12M到12B不等，是目前最强的编程语言预训练模型。Codex 能够帮助程序员根据函数名和注释自动补全代码、直接生成代码、自动补充测试样例，并支持多种编程语言。本期 Azure OpenAI 官方指南将详解 Codex 的模型结构如何帮助程序员实现自动代码生成。We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. 0%. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28. . S. 4 %, but a pass @ 1 @ 1 @1 @ 1 (correct rate of a single solution) of only 33. 1 and you find the settings in the following table: The training was executed on 16 x A100 (40GB) GPUs. 2% score in Codex HumanEval and Python coding tests. 2. 图2 HumanEval数据集中的三个编程问题例子. 8% at k=1, 46. 005. 5% pass@1 score on HumanEval. Keywords: test generation, unit testing, large language models, test smellsThe task of generating code solutions for a given programming problem can benefit from the use of pre-trained language models such as Codex, which can produce multiple diverse samples. Claude 2 has apparently improved its coding skills, scoring 71. Three example problems from the HumanEval dataset, where the probabilities that a single sample from Codex-12B passes unit tests are 0. Claude 2 has apparently improved its coding skills, scoring 71. 1We report the results on the HumanEval benchmark with the Codex model code-cushman-001. 2% on the Codex HumanEval Python coding test. 1We report the results on the HumanEval benchmark with the Codex model code-cushman-001. 2. 69. 17 20. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model. 2%. In this paper, we introduce CodeGeeX, a multilingual model with 13 billion parameters for code generation. One commonly used Python benchmark is HumanEval, which assesses whether the model can complete functions based on their signature and docstring. We evaluated the models on OpenAI's HumanEval benchmark that was introduced in the Codex paper. . Each problem includes a function signature, docstring, body, and several unit tests, with an average of 7. HumanEval-X is a new multilingual benchmark that contains 820 human-crafted coding problems in 5 programming languages (Python, C++, Java, JavaScript,. It consists of 164 original programming problems, assessing language comprehension, algorithms, and simple mathematics, with some comparable to simple software. 17. 0% on the Codex HumanEval, a Python coding test. In the Codex HumanEval coding exam, it achieved a score of 71. Different with HumanEval, we need an evaluation platform to provide a ready runtime environment with automatic programs to execute and verify the code generated by code generation models, we choose to base it on a Linux Docker image, which can provide a virtual and safe sandbox to enable easy duplication and prevent harmful execution. Google has proposed PaLM-Coder [3]. [Why this matters] Claude 2's upgrades give it a big leg up on ChatGPT in many areas and make it a formidable contender as a leading chatbot. This goes to show how effective it is when it comes to writing computer codes. 4\% 77. and. 0%. , 2021 ) , it only consists of handcrafted programming problems in Python, thus cannot be directly applied to systematically evaluate the performance of multilingual code generation. 2 Its original version scored a 56% on the Codex HumanEval (a Python coding test) while the new version jumped to a 71%. Declarations, docstrings, and solutions are marked with red, green, and blue respectively. HumanEval-X is a benchmark for evaluating the multilingual ability of code generative models. HumanEval-X for Realistic Multilingual Benchmarking. 2. To ensure a thorough assessment of the functional correctness of LLM-synthesized code, HumanEval+ extends the number of test cases significantly, averaging at 774. The OpenAI research team. 0% up from 85. Claude 2 also scored a 71. To put it into perspective that is enough content to be. 1 and 4. Claude 2 excels at the core capabilities of. The generated tests also suffered from test smells, such as. 2% on the Codex HumanEval, a test designed to gauge Python coding proficiency. Google has proposed PaLM-Coder [3]. On the HumanEval dataset, we improved Codex’s pass@1 from 26% to 32% and on the MBPP dataset, we improved from 36% to 42%. 0% on the Codex HumanEval, a Python coding test. 2%, up from 56. How to Access Claude 2? Here is a step-by-step guide on how to access Claude 2:Here we have evaluated our python code models on the HumanEval codex dataset [CTJ+21] at temperature T= 0:6 and top P= 0:95. It outperforms GPT-3 and GPT-J on HumanEval,. According to the paper, each problem includes. 2 percent lower than Claud-2. The coding capabilities of Claude 2 have witnessed a substantial enhancement, evident from its score of 71. def anti_shuffle(s): """ Write a function that takes a string and returns an ordered version of it. It is not better than GPT-3. HumanEval/86. A case study using the HumanEval benchmark shows that an adaptive way of using multiple GPT models can achieve both much higher accuracy (from 68% to 90%) and lower inference cost (by 18%) than using GPT-4 for coding. 1 to get pass@1, and --temperature 0. I've been grinding at can-ai-code for 3 months and will continue grinding, the latest models are wiping the floor with my junior-v2 test so its time for an advanced interview. Claude 2 model has a 71. 在代码生成领域，当前最广泛被使用的是OpenAI在Codex论文中开源的HumanEval，该基准测试集由164道由OpenAI工程师手动编写的编程任务组成，以一定. We need more independent benchmarks. g. The evaluation covered a wide range of programming languages and yielded impressive results, helping to quantify the model’s performance in each. We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. 5 (48. For example, Codex shows that a 12B param-eters language model can solve 28:8% of standalone Python programming problems1. , 2021) and MBPP benchmark (Austin et al. 0%. OnHumanEval, a new evalua-tion set we release to measure functional correct-ness for synthesizing programs from docstrings, We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. GPT-4 vs Codex for Coding. From left to right: InCoder, CodeGen, Codex. 2% on the Codex HumanEval Python coding test compared to Claude 1. Max tokens: 100K. When it comes to writing, Llama-2 and GPT-4 are very different, too. The HumanEval dataset is a set of 164 handwritten programming problems which was used to evaluate functional correctness. Through the evaluation of three public available models (CodeGen, PanGu-Coder, and Codex) on CoderEval, we. Figure 1. , 2021) as an example, Codex has a pass @ 100 @ 100 @100 @ 100 (pass if one or more among 100 100 100 100 generated solutions for a given problem can pass the corresponding test cases) of 77. 0% achieved by its predecessor, Claude-1. It consists of 820 high-quality human-crafted data samples (each with test. Make sure to use python 3. Claude 2’s coding skills have also seen a significant improvement, as it scored 71. From Source. Intended Use and Limitations As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. Codex (February 28, 1977 – August 20, 1984) was an American thoroughbred racehorse who won the 1980 Preakness Stakes. You switched accounts on another tab or window. Here is nearly functional example code (you just have to provide. 1 Introduction While EvalPlus is general, we extend the test-cases of the popular HumanEval benchmark by 80x to build HumanEval+. The repository provides installation instructions, usage examples, and citation information for the paper \"Evaluating Large Language Models Trained on Code\". Also, all the occurrences of the same identifier are masked using the same sentinel. Eval+ is an expanded version of OpenAI’s official standardized programming benchmark, HumanEval - first introduced in their Codex paper. The generated tests also suffered from test smells, such as. Figure 1. Steven Hoi. lm-evaluation-harness is undergoing a Big Refactor right now which. More results with different models and benchmarks can be found in Section 4. HumanEval Benchmark + Codex Models Evaluation: test case execution 164 hand-written examples Why human-written? “It is important for these tasks to be hand-written, since our models are trained on a large fraction of GitHub, which already contains solutions to problems from a variety of sources. We evaluate our models on two code generation benchmark: HumanEval and MTPB. 2% on Codex HumanEval, a test designed to evaluate Python coding skills. CodeGeeX is pre. Codex can read simple natural language commands and instructions and write code that matches the intention of the user. For Codex HumanEval, you need to use --temperature 0. 2 percent score on the Codex HumanEval, a Python coding test, up from 56 percent achieved by its previous version, Claude-1. fit from the use of pre-trained language models such as Codex, which can produce multiple diverse samples. In the GSM8K math problems for kids test, Claude Instant 1. 1We report the results on the HumanEval benchmark with the Codex model code-cushman-001. , 2021), a state-of-the-art pre-trained language model for code generation, can achieve a pass@100 (pass if one or more among 100 generated solutions for a given problem can pass the corresponding test cases) of 77:4%, but a pass@1 (correct rate of a single so- unveiled Codex [16] and Code-Davinci [38]. It aims to evaluate, Functional. Taking the HumanEval benchmark (Chen et al. 2% de Claude 1. HumanEval-X is a new multilingual benchmark that contains 820 human-crafted coding problems in 5 programming languages (Python, C++, Java, JavaScript, and Go. Its score on the Codex HumanEval, a. We also find that LLM-generated robotic plans using Parsel are more than twice as likely to be considered accurate than directly generated plans. Code Generation is an important field to predict explicit code or program structure from multimodal data sources such as incomplete code, programs in another programming language, natural language descriptions or execution examples. We found that the Codex model achieved above 80% coverage for the HumanEval dataset, but no model had more than 2% coverage for the EvoSuite SF110 benchmark. Max tokens: 100K. Code Llama reaches state-of-the-art performance among open models on several code benchmarks, with scores of up to 53% and 55% on HumanEval and MBPP, respectively. e. 2% on the Codex HumanEval Python coding test and 88. Claude 2 scored a 71. We apply SCoT prompting to two LLMs (i. When we omit the. We found that the Codex model achieved above 80% coverage for the HumanEval dataset, but no model had more than 2% coverage for the EvoSuite SF110 benchmark. 0% on the Codex HumanEval, a Python coding test. We evaluate two state-of-the-art code generation mod-els on MultiPL-E: Codex (Chen et al. Notably, all the mentioned models generate code solutions for each problem utilizing a single attempt, and the resulting pass rate percentage is reported. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. Salesforce has introduced Codex is a GPT language model finetuned on publicly available code from GitHub. 8. Do you have any plans to publish the raw GPT-Neo on HumanEval? In addition, are there any tricks in the process of reproducing this? Thanks! Our re-produce results:Codex davinci-002 Introductory Pass@1 29. The prompt partImproved Coding Skills: Claude 2 scored 71. 3, thanks to. 8% of the problems, and Codex-S (further fine-tuned on correctly implemented standalone functions) solves 37. 3, scored only 56% on these tests. To address this, we started the EvalPlus project -- a rigourous evaluation framework for LLM4Code that: improves code benchmarks by adding up to thousands of new tests! (81x new tests for HumanEval!) crafts a set utility tools to sanitize, visualize and inspect LLM-generated code and evaluation results! accelerates LLM4Code research by open. the results on Multilingual HumanEval and can also be found in Appendix D. 0% on GSM8k grade-school math problems, revealing. 2. However, a major challenge for this task is to select. CodeGeeX is pre-trained on 850 billion tokens of 23. More results with different models and benchmarks can be found in Section 4. It outperforms GPT-3 and GPT-J on HumanEval, a new evaluation set for functional correctness, and reveals its limitations and potential impacts. It consists of 820 high-quality human-crafted data samples (each with test cases) in Python, C++, Java, JavaScript, and Go, and can be used for various tasks, such as code generation and translation. HumanEval: Hand-Written Evaluation Set . 0% of the older version. from publication: MultiPL-E: A Scalable and. 3B) on the HumanEval dataset, and found that it was much lower than that reported in the Codex paper. g. 2% on the Codex HumanEval Python coding test and 88. GPT-4, though, is almost like a “Coder Buddy” that can help you. Also, it scored 88. 2%. Pricing and Availability. Our Reflexion-based agent was benchmarked on the HumanEval dataset and achieved 88% accuracy, surpassing GPT-4 (67%), CodeT (65. Training Data. 2% on Codex HumanEval. 9. We provide example_problem. Figure 1. 17. Home : CoH Demo Info : CoH Demo Content Resources CoH Demo Content Resources. , HumanEval, MBPP,. , variable name, function names, etc. 2%. SkyCode是一个多语言开源编程大模型，采用GPT3模型结构，支持Java, JavaScript, C, C++, Python, Go, shell等多种主流编程语言，并能理解中文注释。模型可以对代码进行补全，拥有强大解题能力，使您从编程中解放出来，专心于解决更重要的问题。| SkyCode is an open source programming model, which adopts the GPT3 model structure. Return the greatest integer that is greater than zero, and has a frequency greater than or equal to the value of the integer itself. HumanEval-X is a new multilingual benchmark that contains 820 human-crafted coding problems in 5 programming languages (Python, C++, Java, JavaScript,. This extension is made possible by performing large-scale. 2%. 0% on the Codex HumanEval, a Python coding test. However, a major challenge for this task is to select. Choosing the Right Model The choice of model largely depends on the specific requirements. Hi, we reproduced the performance of the raw GPT-Neo (125M and 1. On GSM8k, a large set of grade-school math problems, Claude 2 scored. 在标准基准上评估测试了 Claude 2、Claude Instant 1. 8:. Improved math skills: Claude 2 scored 88. dataset contains 164. BLEU and ROGUE both work by comparing a candidate (ie, model output) to reference text (ie, training data). These. 2 percent on the Codex HumanEval benchmark, up from 56 percent. 7% of the problems. 2022). En el examen de codificación Codex HumanEval, Claude 2 obtuvo una puntuación del 71. We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. 2% on the Codex HumanEval, a Python coding test. All models are evaluated on the HumanEval dataset that consists of 164 prompts with description in the form of code, comments, etc. However, these models are closed-source. Claude 2. We evaluated the models on OpenAI's HumanEval benchmark that was introduced in the Codex paper. CodeGeeX2 is a base model for multilingual code generation, which has been significantly improved in its coding ability compared to the previous generation. LLMs like Codex Chen et al. Our benchmarks also support other code completion tasks such as code insertion or translation in many languages. ) are hidden in this task. 1We report the results on the HumanEval benchmark with the Codex model code-cushman-001. 2%. Bard (Google)HumanEval-X for Realistic Multilingual Benchmarking. Pass rates of our models on the HumanEval dataset as a function of model size. 3. Installation. CodeT then executes the code samples using the generated test cases, and performs a dual execution agreement, which considers both the consistency of the outputs against the generated test cases and the agreement of the outputs with other code samples. A distinct production version of Codex powers GitHub Copilot. On your course’s homepage, click Assignments (left sidebar) and then Create Assignment (bottom right). This is a. We use MultiPL-E to extend the HumanEval benchmark (Chen et al. , GPT-4 and ChatGPT) demonstrates that HumanEval+ is able to catch significant amounts of previously undetected wrong code synthesized by LLMs, reducing. Codex can also make mistakes binding operations to variables, especially when the. On GSM8k, a large set of grade-school math problems, Claude 2 scored 88. (2) Human evaluation shows that human developers prefer programs generated by SCoT prompting. It also displays surprising emergent properties compared to phi-1-base, our model before our finetuning stage on a dataset of coding exercises, and phi-1-small, a smaller model with 350M parameters trained with the same pipeline as phi-1 that still achieves 45% on HumanEval. Uh, so 1) SalesForce Codegen is also open source (BSD licensed, so more open than StarCoder's OpenRAIL ethical license). 2% on the Codex HumanEval for assessing Python coding skills, up 15 percentage points from Claude 1. 7 or later:The model was trained on the cleaned CodeParrot 🦜 dataset in two steps. HumanEval consists of 164 hand. 2% up from 56. We have an exciting roadmap of capability improvements planned for Claude 2 and will be slowly and iteratively deploying them in the coming months. 2% on the Codex HumanEval, a Python coding test. On GSM8k, a large set of grade-school math problems, Claude 2 scored 88. 1 and 4. Claude 2 also showcased enhanced coding skills, achieving an impressive score of 71. 🌐 English . Eval+ in particular adds thousands of. To help standardize the evaluation of multilingual code generation and translation, we develop and release the HumanEval-X Benchmark. In addition to predicting final loss, we developed methodology to predict more interpretable metrics of capability. But, considering that Llama-2 has. 8 test cases per problem. It consists of 164 hand-written programming prob-lems and solutions in Python, each of which includes a function signature, docstring, body, and multiple unit testsThe HumanEval dataset is a collection of Python problems, each in the same format as the example above. 0%. Anthropic has been working to improve the underlying safety of Claude 2, making it more harmless and harder to prompt to produce offensive or. This is compared to 67% of GPT-4. 2 to 88. Request PDF | On Aug 4, 2023, Qinkai Zheng and others published CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X | Find, read and cite all the. To associate your repository with the codex topic, visit your repo's landing page and select "manage topics. Codex is based on the GPT-3 language model and can solve over 70% of the problems in OpenAI's publicly available HumanEval test dataset, compared to 0% for GPT-3. Similarly, on GSM8k , a test comprising grade-school math problems, it improved from 85. 后面作者又收集了一个跟HumanEval更相近的训练集，在上面训练得到的模型叫Codex-S. Codex：fine-tune GPT models containing up to 12B parameters on code to produce Codex. son of all existing models on the HumanEval benchmark. 0%. According to Anthropic, Claude 2 scored 71. Since ChatGPT has any specialized coding or mathematical ability, it frequently fails to generate accurate or coherent results. 2％のスコアを持っています。その前身であるクロード1. This is compared to 67% of GPT-4. 3. “Claude 2 scored a 71. , 2021 ) and APPS (Hendrycks et al. 2% on the Codex HumanEval, a Python test. We show that measuring uncertainty in natural language is challenging because of "semantic equivalence" -- different sentences can. Its coding capability score has also increased from 56% to 71. When a single sample is generated for each problem, GPT-12B solves no problems, but Codex (fine-tuned on code) solves 28. Remarkably, Claude 2 excels in coding proficiency, surpassing its previous version by demonstrating superior skills in the Codex HumanEval Python programming test. 0% on the Codex HumanEval, a Python coding test. Hi, we reproduced the performance of the raw GPT-Neo (125M and 1. 1 和 Claude 1. Codex (Chen et al. in HumanEval, 12. HumanEval-X is a new multilingual benchmark that contains 820 human-crafted coding problems in 5 programming languages (Python, C++, Java, JavaScript,. Codex 300Ma 13. Make sure to use python 3. HumanEval only evaluates the natural language to Python synthesis, we curate an unseen evaluation dataset3 in each of the 12 languages, to evaluate the perplexity of different models. 0% on the GSM8k, a large set of grade-school math problems. We found similar performance boosts with other code generation models such as GPT-J and GPT-Neo. 6% on HumanEval and 55. 0% in the GSM8k mathematics problem set, compared to Claude 1. 2% to 88. Regarding the temperature parameter, in Codex paper, the authors observed that the best performing. 1We report the results on the HumanEval benchmark with the Codex model code-cushman-001. 0% on the Codex HumanEval, a Python coding test. Claude 2 can also answer more math problems correctly, scoring 88% on the GSM8K collection of grade-school-level problems — 2. Katz (Stanford CodeX), M. 2% score on the Codex HumanEval, a Python coding test, up from 56. 2022. 2%. 2%). On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28. We evaluated the models based on compilation rates, test correctness, coverage, and test smells. When a single sample is generated for each problem, GPT-12B solves no problems, but Codex (fine-tuned on code) solves 28. fit from the use of pre-trained language models such as Codex, which can produce multiple diverse samples. On GSM8k, a large set of grade-school math problems, Claude 2 scored 88. AI. 31% in MBPP, and 6. According to Anthropic, Claude 2 scored 71. TL;DR: CodeT5+ is a new family of open code large language models (LLMs) with improved model architectures and training techniques. Arredondo (Casetext/Stanford CodeX), D. 8% pass@1 on HumanEval is good, GPT-4 gets a 67. Competitive with OpenAI Codex. Codex (Chen et al. 3’s score of 85. While GPT-4 is considerably better than GPT-3. It consists of 164 hand-written programming problems and solutions in Python, each of which includes a function signature, docstring, body, and multiple unit. Eval+ in particular adds thousands of test cases to the same 163 problems in. The model is also proficient in math: on GSM8k, a large set of grade-school math problems, Claude 2 scored 88. Claude 2 is also significantly safer. jsonl under data to illustrate the format and help with debugging. F or our experiment, we use the HumanEval dataset proposed by Chen et al. 8%), and PaLM (26. Claude 2 can perform many kinds of text-processing tasks. Anthropic has released Claude 2, an advanced AI model that outperforms Claude 1. Codex powers AI pair. 1 and 4. Its original version scored a 56% on the Codex HumanEval (a Python coding test) while the new version jumped to a 71%. Recently, DS-1000 [16] HumanEval-X for Realistic Multilingual Benchmarking. The pass@k value is then the fraction of problems that were solved. In this paper, we introduce CodeGeeX, a multilingual model with 13 billion parameters for code generation. A core component of this project was developing infrastructure and optimization methods that behave predictably across a. It implements the evaluation harness for the HumanEval problem solving dataset described in the paper "Evaluating Large Language Models Trained on Code". We conduct comprehensive experiments on four benchmarks, HumanEval, MBPP, APPS and. 3，包括用于 python 函数合成的 Codex HumanEval、用于解决小学数学问题的 GSM8k、用于多学科问答的 MMLU、针对长故事问答的 QuALITY、用于科学问题的 ARC-Challenge、用于阅读理解的 TriviaQA 和用于中学水平阅读理解与推理的 RACE-H，具体的评估结果如下. Advanced Computational Skills: Claude 2 also scored a 71. A random sample of 100 examples was taken to evaluate each engine. Pass rates of our models on the HumanEval dataset as a function of model size. 0%, frente al 85. Large pre-trained code generation models, such as OpenAI Codex, can generate syntax- and. 8% higher than the second-best open-source Code LLM, Codex. Building upon HumanEval (Python only), we develop the HumanEval-X benchmark for evaluating multilingual models by hand-writing the solutions in C++, Java, JavaScript,. 4%. The prompt provided to the model is shown. The HumanEval Dataset "HumanEval" refers to a hand-crafted dataset comprising 164 programming challenges. 1 和 Claude 1.