Semantic Retrieval

A new shared task of semantic retrieval from legal texts, in which a so-called contract discovery is to be performed, where legal clauses are extracted from documents, given a few examples of similar clauses from other legal acts. Source: Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines

Phrase in Context is a curated benchmark for phrase understanding and semantic search, consisting of three tasks of increasing difficulty: Phrase Similarity (PS), Phrase Retrieval (PR) and Phrase Sense Disambiguation (PSD). The datasets are annotated by 13 linguistic experts on Upwork and verified by two groups: ~1000 AMT crowdworkers and another set of 5 linguistic experts. PiC benchmark is distributed under CC-BY-NC 4.0.

arXiv GitHub ## Dataset Summary Speech Brown is a comprehensive, synthetic, and diverse paired speech-text dataset in 15 categories, covering a wide range of topics from fiction to religion. This dataset consists of over 55,000 sentence-level samples. To train the CLASP model, we created this dataset based on the Brown Corpus. The synthetic speech was generated using the NVIDIA Tacotron 2 text-to-speech model. For more information about our proposed model, please refer to this paper. The dataset generation pipeline, along with code and usage instructions, is available on this GitHub page. ## Dataset Statistics 1. Total size: Approximately 30 GB. 2. Number of samples: 55,173 pairs of speech and text. 3. Average tokens per sample: 19.00. 4. Maximum tokens in a sample: 48. 5. Average characters per sample: 96.72. 6. Number of unique tokens: 50,667 7. Categories: 15 categories consist of adventure, belles_lettres, editorial, fiction, government, hobbies, humor, learned, lore, mystery, news, religion, reviews, romance, science_fiction. ## Dataset Structure To ensure ease of use, the dataset is partitioned into 10 parts. Each part can be used independently if it meets the requirements of your task and model. ### Metadata Files 1. global_metadata: A JSON file containing metadata for all 55,173 samples. 2. localized_metadata: A JSON file containing metadata for all samples, categorized into the 10 dataset partitions. ### Metadata Fields 1. id: The unique identifier for the sample. 2. audio_file_path: The file path for the audio in the dataset. 3. category: The category of the sample's text. 4. text: The corresponding text of the audio file. ## Usage Instructions To use this dataset, download the parts and metadata files as follows: #### Option 1: Manual Download Visit the dataset repository and download all dataset_partX.zip files and the global_metadata.json file. #### Option 2: Programmatic Download Use the huggingface_hub library to download the files programmatically: python from huggingface_hub import hf_hub_download from zipfile import ZipFile import os import json # Download dataset parts zip_file_path1 = hf_hub_download(repo_id="llm-lab/SpeechBrown", filename="dataset_part1.zip", repo_type="dataset") zip_file_path2 = hf_hub_download(repo_id="llm-lab/SpeechBrown", filename="dataset_part2.zip", repo_type="dataset") # Download other parts... # Download metadata metadata_file_path = hf_hub_download(repo_id="llm-lab/SpeechBrown", filename="global_metadata.json", repo_type="dataset") for i in range(1, 11): with ZipFile(f'dataset_part{i}.zip', 'r') as zip_ref: zip_ref.extractall(f'dataset_part{i}') os.remove(f'dataset_part{i}.zip') with open('global_metadata.json', 'r') as f: metadata = json.load(f) metadata.keys() ## Citations If you find our paper, code, data, or models useful, please cite the paper: @misc{abootorabi2024claspcontrastivelanguagespeechpretraining, title={CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information Retrieval}, author={Mohammad Mahdi Abootorabi and Ehsaneddin Asgari}, year={2024}, eprint={2412.13071}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.13071}, } ## Contact If you have questions, please email mahdi.abootorabi2@gmail.com or asgari@berkeley.edu.