datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
fomc_communication
Label Interpretation
LABEL_2: NeutralLABEL_1: HawkishLABEL_0: Dovish
Citation and Contact Information
Cite
Please cite our paper if you use any code, data, or models.
@inproceedings{shah-etal-2023-trillion,
title = "Trillion Dollar Words: A New Financial Dataset, Task {\&} Market Analysis",
author = "Shah, Agam and
Paturi, Suvan and
Chava, Sudheer",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for… See the full description on the dataset page: https://huggingface.co/datasets/gtfintechlab/fomc_communication.Environmental_Communication_XR
Dataset Description
This dataset contains annotated social media posts (tweet IDs) on environmental-related content from Extinction Rebellion. Each tweet has been manually annotated by three independent annotators (A1, A2, A3) across several dimensions related to environmental communication, emotions, and sentiment. The dataset also includes basic metadata for each tweet.
Columns
1. tweet ID: Unique identifier of the tweet.2. Topic: Integer label indicating the thematic… See the full description on the dataset page: https://huggingface.co/datasets/ChristinaBarz/Environmental_Communication_XR.fomc-communicationDataset adapted from original work by Shah et al.
About Dataset
The dataset is a collection of sentences from FOMC speeches, meeting minutes and press releases (see corresponding paper). A subset of the data has been manually annotated as hawkish, dovish, or neutral.
Label mapping
LABEL 2: Neutral
LABEL 1: Hawkish
LABEL 0: Dovish
fomc-communication-counterfactualDataset adapted from original work by Shah et al.
About Dataset
The dataset is a collection of sentences from FOMC speeches, meeting minutes and press releases (see corresponding paper). A subset of the data has been manually annotated as hawkish, dovish, or neutral.
Label mapping
LABEL 2: Neutral
LABEL 1: Hawkish
LABEL 0: Dovish
Counterfactual generation split
Additionally, for counterfactual generation tasks, we add a custom split with target classes in… See the full description on the dataset page: https://huggingface.co/datasets/TextCEsInFinance/fomc-communication-counterfactual.legal-engagement-letter-scope-fee-communication-coherence-risk-v0.1What this dataset does
You receive
scope text
exclusions
fee basis
objective
later work
client comms
You decide
coherent
or
incoherent
Daily use
scope drift detection
fee term mismatch detection
missing variation letter risk
Termes_technologies_information_et_communication
[!NOTE]
Dataset origin: https://www.eurotermbank.com/collections/935