datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Code-Feedback OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
[🏠Homepage]
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[🛠️Code]
Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/Code-Feedback.ultra-feedback-pairedIELTS-writing-feedback-reasoning
Dataset Card for IELTS Writing Task 2 – Reasoning-Based Evaluation Dataset
Dataset Summary
This dataset is an IELTS Writing Task 2 automated scoring and feedback dataset based on explicit reasoning. It contains writing prompts, student essays, and a complete scoring process with professional-grade feedback generated by GLM-4.7, one of the top-tier Large Language Models (LLMs) in the current open-source ecosystem known for its strong reasoning capabilities.
Unlike… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/IELTS-writing-feedback-reasoning.FeedbackQARetrieval
FeedbackQARetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment
Task category
t2t
Domains
Web, Government, Medical, Written
Reference
https://arxiv.org/abs/2204.03025
Source datasets:
lt2c/fqa
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/FeedbackQARetrieval.Code-Feedback-decontaminationA decontaminated version of m-a-p/Code-Feedback.
The excluded (28) files are "contaminated" with only two code segments:
simple GCD function: while b: a, b = b, a % b return a
sum_to_n solution: return sum(range(n + 1))
And reformated to sharegpt.
Decontamination is done in the same way as Magicoder (ie., bigcode decontamination process), which uses a substring-match-finding method to find overlaps between a target dataset and the following standard benchmarks:
HumanEval
MBPP… See the full description on the dataset page: https://huggingface.co/datasets/Leon-Leee/Code-Feedback-decontamination.Feedback_Friction_Dataset
Feedback Friction Dataset
This dataset contains the LLaMA-4 Maverick results from the iterative feedback experiments described in the paper: FEEDBACK FRICTION: LLMs Struggle to Fully Incorporate External Feedback.
Github Repository: https://github.com/JHU-CLSP/Feedback-Friction
Note: While the paper evaluated multiple frontier models including LLaMA-3.3-70B-Instruct, LLaMA-4-Scout-17B-16E-Instruct, Claude 3.7 Sonnet, and Claude 3.7 Sonnet with Extended Thinking, this dataset… See the full description on the dataset page: https://huggingface.co/datasets/Dongwei/Feedback_Friction_Dataset.single-turn-eval-meta_feedback_qwen3-4b_step2_gpt-5.4_gepa-n32
Single-turn eval — violetxi/meta_feedback_qwen3-4b_step2_gpt-5.4_gepa
Generated by teaching/inference/single_turn_eval_vllm.py. One row per problem; samples is the list of model responses, scores is per-sample correctness, and mean/best/worst are the aggregates used by mean@N / best@N / worst@N.
Eval results (n_samples_per_example = 32)
Overall
metric
value
n_examples
1006
mean@32
0.1796
best@32
0.3588
worst@32
0.0477
pass_rate
0.3588… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/single-turn-eval-meta_feedback_qwen3-4b_step2_gpt-5.4_gepa-n32.Code-Feedback OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
[🏠Homepage]
|
[🛠️Code]
Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related… See the full description on the dataset page: https://huggingface.co/datasets/Mgmgrand420/Code-Feedback.falcon_feedback_instraction_TurkishThis dataset created from falcon instruction dataset, I used facebook nllb-200-distilled-600M model to translate some of it from English language to Turkish.
Code-Feedback OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
[🏠Homepage]
|
[🛠️Code]
Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and… See the full description on the dataset page: https://huggingface.co/datasets/Smileoua/Code-Feedback.Code-Feedback OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
[🏠Homepage]
|
[🛠️Code]
Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and… See the full description on the dataset page: https://huggingface.co/datasets/tenghao/Code-Feedback.Code-Feedbackuxr OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
[🏠Homepage]
|
[🛠️Code]
Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and… See the full description on the dataset page: https://huggingface.co/datasets/MichaelBoll/Code-Feedbackuxr.single-turn-eval-meta_feedback_qwen3-4b_step2_gpt-5-nano_gepa-n32
Single-turn eval — violetxi/meta_feedback_qwen3-4b_step2_gpt-5-nano_gepa
Generated by teaching/inference/single_turn_eval_vllm.py. One row per problem; samples is the list of model responses, scores is per-sample correctness, and mean/best/worst are the aggregates used by mean@N / best@N / worst@N.
Eval results (n_samples_per_example = 32)
Overall
metric
value
n_examples
1006
mean@32
0.1804
best@32
0.3569
worst@32
0.0398
pass_rate… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/single-turn-eval-meta_feedback_qwen3-4b_step2_gpt-5-nano_gepa-n32.user-feedback-dpo
📝 ChatTune-DPO (farabi-lab/user-feedback-dpo)
📌 Dataset Summary
ChatTune-DPO is a dataset containing structured user interactions with a Large Language Model (LLM), focusing on user feedback for Direct Preference Optimization (DPO) training. The dataset includes dialogues where users interact with the model, provide likes/dislikes, and corrections to responses.
This dataset is designed to enhance model alignment with human preferences by leveraging both explicit and… See the full description on the dataset page: https://huggingface.co/datasets/farabi-lab/user-feedback-dpo.