FinEAS: Financial Embedding Analysis of Sentiment šŸ“ˆ

Overview

FinEAS: Financial Embedding Analysis of Sentiment šŸ“ˆ

(SentenceBERT for Financial News Sentiment Regression)

This repository contains the code for generating three models for Finance News Sentiment Analysis.

The models implemented are:

  • A SentenceBERT with simple classifier.
  • A BERT with simple classifier.
  • A simple Bi-directional Long-Short Term Memory (LSTM) network.
  • FinBERT from HuggingFace.
  • SentenceBERT from HuggingFace

Models šŸ¤–

Results āœ…

We used three partitions of the datasets from the February 11th, 2021. 6 months previous to that date, 1 year previous to that date and 2 years previous to the date mentioned.

We also evaluated the models 2 weeks later that date; that is to say, we evaluated from February 12th, 2021 to February 26th, 2021.

The table below shows the results:

FinEAS BERT BiLSTM
6 months 0.0556 0.2124 0.2108
6 months
Next 2 weeks
0.1061 0.2190 0.2194
1 year 0.0654 0.2137 0.2140
1 year
Next 2 weeks
0.1058 0.2191 0.2194
2 years 0.0671 0.2087 0.2086
2 years
Next 2 weeks
0.1065 0.2188 0.2185

The table below shows the results for the HuggingFace models

Dates FinEAS FinBERT
6 months 0.0044 0.0050
12 months 0.0036 0.0034
24 months 0.0033 0.0040

Citing šŸ“£

@misc{gutierrezfandino2021fineas,
      title={FinEAS: Financial Embedding Analysis of Sentiment}, 
      author={Asier GutiƩrrez-FandiƱo and Miquel Noguer i Alonso and Petter Kolm and Jordi Armengol-EstapƩ},
      year={2021},
      eprint={2111.00526},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

License šŸ¤

MIT License.

Copyright 2021 Asier GutiƩrrez-FandiƱo & Jordi Armengol-EstapƩ.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Owner
LHF Labs
https://huggingface.co/lhf
LHF Labs
YOLOv3 in PyTorch > ONNX > CoreML > TFLite

This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices

Ultralytics 9.3k Jan 07, 2023
113 Nov 28, 2022
Finetune the base 64 px GLIDE-text2im model from OpenAI on your own image-text dataset

Finetune the base 64 px GLIDE-text2im model from OpenAI on your own image-text dataset

Clay Mullis 82 Oct 13, 2022
A list of multi-task learning papers and projects.

This page contains a list of papers on multi-task learning for computer vision. Please create a pull request if you wish to add anything. If you are interested, consider reading our recent survey pap

svandenh 297 Dec 17, 2022
This repo implements several applications of the proposed generalized Bures-Wasserstein (GBW) geometry on symmetric positive definite matrices.

GBW This repo implements several applications of the proposed generalized Bures-Wasserstein (GBW) geometry on symmetric positive definite matrices. Ap

Andi Han 0 Oct 22, 2021
Non-Attentive-Tacotron - This is Pytorch Implementation of Google's Non-attentive Tacotron.

Non-attentive Tacotron - PyTorch Implementation This is Pytorch Implementation of Google's Non-attentive Tacotron, text-to-speech system. There is som

Jounghee Kim 46 Dec 19, 2022
WatermarkRemoval-WDNet-WACV2021

WatermarkRemoval-WDNet-WACV2021 Thank you for your attention. Citation Please cite the related works in your publications if it helps your research: @

LUYI 63 Dec 05, 2022
This repository is maintained for the scientific paper tittled " Study of keyword extraction techniques for Electric Double Layer Capacitor domain using text similarity indexes: An experimental analysis "

kwd-extraction-study This repository is maintained for the scientific paper tittled " Study of keyword extraction techniques for Electric Double Layer

ping 543f 1 Dec 05, 2022
Distributed Deep learning with Keras & Spark

Elephas: Distributed Deep Learning with Keras & Spark Elephas is an extension of Keras, which allows you to run distributed deep learning models at sc

Max Pumperla 1.6k Jan 05, 2023
Data and code for ICCV 2021 paper Distant Supervision for Scene Graph Generation.

Distant Supervision for Scene Graph Generation Data and code for ICCV 2021 paper Distant Supervision for Scene Graph Generation. Introduction The pape

THUNLP 23 Dec 31, 2022
Fast Neural Representations for Direct Volume Rendering

Fast Neural Representations for Direct Volume Rendering Sebastian Weiss, Philipp Hermüller, Rüdiger Westermann This repository contains the code and s

Sebastian Weiss 20 Dec 03, 2022
Sematic-Segmantation - Semantic Segmentation on MIT ADE20K dataset in PyTorch

Semantic Segmentation on MIT ADE20K dataset in PyTorch This is a PyTorch impleme

Berat Eren Terzioğlu 4 Mar 22, 2022
Implementation of Memformer, a Memory-augmented Transformer, in Pytorch

Memformer - Pytorch Implementation of Memformer, a Memory-augmented Transformer, in Pytorch. It includes memory slots, which are updated with attentio

Phil Wang 60 Nov 06, 2022
Hub is a dataset format with a simple API for creating, storing, and collaborating on AI datasets of any size.

Hub is a dataset format with a simple API for creating, storing, and collaborating on AI datasets of any size. The hub data layout enables rapid transformations and streaming of data while training m

Activeloop 5.1k Jan 08, 2023
A bunch of random PyTorch models using PyTorch's C++ frontend

PyTorch Deep Learning Models using the C++ frontend Gettting started Clone the repo 1. https://github.com/mrdvince/pytorchcpp 2. cd fashionmnist or

Vince 0 Jul 13, 2021
Prototype for Baby Action Detection and Classification

Baby Action Detection Table of Contents About Install Run Predictions Demo About An attempt to harness the power of Deep Learning to come up with a so

Shreyas K 30 Dec 16, 2022
The reference baseline of final exam for XMU machine learning course

Mini-NICO Baseline The baseline is a reference method for the final exam of machine learning course. Requirements Installation we use /python3.7 /torc

JoaquinChou 3 Dec 29, 2021
GUPNet - Geometry Uncertainty Projection Network for Monocular 3D Object Detection

GUPNet This is the official implementation of "Geometry Uncertainty Projection Network for Monocular 3D Object Detection". citation If you find our wo

Yan Lu 103 Dec 28, 2022
Implement of homography net by pytorch

HomographyNet Implement of homography net by pytorch Brief Introduction This project is based on the work Homography-Net: @article{detone2016deep, t

ronghao_CN 4 May 19, 2022
We will release the code of "ConTNet: Why not use convolution and transformer at the same time?" in this repo

ConTNet Introduction ConTNet (Convlution-Tranformer Network) is proposed mainly in response to the following two issues: (1) ConvNets lack a large rec

93 Nov 08, 2022