GreenNode/zalo-ai-legal-text-retrieval-vn

GreenNodeTableMarkdownRetrieval An MTEB dataset Massive Text Embedding Benchmark GreenNodeTable documents Task category t2t Domains Financial, Encyclopaedic, Non-fiction Reference https://huggingface.co/GreenNode Source datasets: GreenNode/GreenNode-Table-Markdown-Retrieval-VN 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/GreenNode/GreenNode-Table-Markdown-Retrieval-VN.

Dataset Card for GSM8K Dataset Summary GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning. These problems take between 2 and 8 steps to solve. Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k.

MassiveIntentClassification An MTEB dataset Massive Text Embedding Benchmark MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages Task category t2c Domains Spoken Reference https://arxiv.org/abs/2204.08582 How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["MassiveIntentClassification"]) evaluator… See the full description on the dataset page: https://huggingface.co/datasets/mteb/amazon_massive_intent.

Tatoeba An MTEB dataset Massive Text Embedding Benchmark 1,000 English-aligned sentence pairs for each language based on the Tatoeba corpus Task category t2t Domains Written Reference https://github.com/facebookresearch/LASER/tree/main/data/tatoeba/v1 How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["Tatoeba"]) evaluator = mteb.MTEB(task) model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/tatoeba-bitext-mining.

MassiveScenarioClassification An MTEB dataset Massive Text Embedding Benchmark MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages Task category t2c Domains Spoken Reference https://arxiv.org/abs/2204.08582 How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["MassiveScenarioClassification"])… See the full description on the dataset page: https://huggingface.co/datasets/mteb/amazon_massive_scenario.

FlagEmbedding Model List | FAQ | Usage | Evaluation | Train | Contact | Citation | License More details please refer to our Github: FlagEmbedding(https://github.com/FlagOpen/FlagEmbedding). If you are looking for a model that supports more languages,...

Dataset Card for SQuAD Dataset Summary Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD 1.1 contains 100,000+ question-answer pairs on 500+ articles. Supported Tasks and Leaderboards Question… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad.

Banking77Classification An MTEB dataset Massive Text Embedding Benchmark Dataset composed of online banking queries annotated with their corresponding intents. Task category t2c Domains Written Reference https://arxiv.org/abs/2003.04807 How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["Banking77Classification"]) evaluator = mteb.MTEB(task) model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/banking77.

STS12 An MTEB dataset Massive Text Embedding Benchmark SemEval-2012 Task 6. Task category t2t Domains Encyclopaedic, News, Written Reference https://www.aclweb.org/anthology/S12-1051.pdf How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["STS12"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To learn more about how… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts12-sts.