StarCoder 和 StarCoderBase 的训练集来自于公开数据集 The Stack v1.2 (https://huggingface.co/datasets/bigcode/the-stack),其中包含 6TB 的授权数据,覆盖358种编程语言。 StarCoder团队经过启发式过滤、人工检查筛选、清洗等处理之后还剩余 783GB 的代码数据,包含86种编程语言,其中有54GB的github issues数据和...
Scaling results for dataset size and deduplication time 图例: 数据去重时间与原始数据集规模的关系。测试基于 GCP 上的 15 个 c2d-standard-16 实例,每个实例每小时的成本约为 0.7 美元。 CPU usage screenshot for the cluster during processing JSON dataset 图例: 集群在处理 JSON 数据集时的 CPU 使用率。
pip3 install git+https://github.com/huggingface/transformers.git@main accelerate -i https://mirror...
System Info transformers version: 4.41.0.dev0 Platform: Linux-6.5.0-28-generic-x86_64-with-glibc2.35 Python version: 3.10.12 Huggingface_hub version: 0.23.0 Safetensors version: 0.4.3 Accelerate version: 0.30.1.dev0 Accelerate config: no...
Add the access token to environment variables: export HF_TOKEN="your huggingface access token" Run the Gradio App: python3 chatbot.py --path "the model name of opencodeinterpreter model family. e.g., m-a-p/OpenCodeInterpreter-DS-6.7B" Video demo.mp4 Contact If you have any inquiries,...
Unseen words are split into subwords, which are derived during the training stage of the tokenizer (more details on this here). Let’s now import few sentences from 20newsgroups dataset and tokenize them from sklearn.datasets import fetch_20newsgroupsnewsgroups_train = fetch_20newsgroups(subset...
Paraphrase Adversaries from Word Scrambling (PAWS) is a dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identificat
Dataset Loaders Edit huggingface/datasets (visual_genome) 19,222 huggingface/datasets (visual_genome) 19,222 Tasks Edit Object Detection Visual Question Answering (VQA) Layout-to-Image Generation Show all Similar Datasets Visual7W Visual Question Answering v2.0 GQA Visual Question Answering...
To run on SQuAD, you will first need to download the dataset. TheSQuAD websitedoes not seem to link to the v1.1 datasets any longer, but the necessary files can be found here: train-v1.1.json dev-v1.1.json evaluate-v1.1.py
在我们的实验中,CodeT5 770M 指的是以因果语言建模为目标的版本huggingface.co/Salesfor。为了实现良好的可重复性和进一步的研究,我们在网站上公开了我们的代码以及在 HumanEval 和 MBPP 上生成的 LLM 结果。 C.上下文窗口与性能 最近的研究表明,上下文窗口的大小对提高 NL2Code 的 LLM 性能起着至关重要的作用。