The app summarizes your input text. Saving your time for reading long texts.
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Updated
Jul 12, 2024 - Python
The app summarizes your input text. Saving your time for reading long texts.
Reproducing baseline model from ACL-2022 paper X-GEAR for Zero-shot Cross-Lingual EAE
Project based on PyTorch-lightning and Transformers for training Seq2SeqLM models, with a primary focus on MT5 and FLAN-T5, yet not limited to them
This is the backend of a trading application, built with Django and PostgreSQL. It provides RESTful APIs to enable users to perform trading transactions and retrieve market data.
Small program that shows account information from MT5 (always on top)
Automatic detection of languages in text utilizing machine learning and Deep learning.
Python framework designed for algorithmic trading on the MetaTrader 5 platform. This repository provides tools and scripts to automate trading strategies, manage positions, and analyze historical data. It empowers traders to implement and backtest various strategies, enhancing their ability to navigate dynamic market conditions.
[EMNLP 2023] FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization. Support BART, PEGASUS, T5, mT5, BioBART, etc.
This repository contains a number of experiments with Multi Lingual Transformer models (Multi-Lingual BERT, DistilBERT, XLM-RoBERTa, mT5 and ByT5) focussed on the Dutch language.
Fine-tuned Transformer models from Hugging Face
State of the art open-source translation for Indic languages.
In this repo you'll learn to use some of the main MetaTrader 5 functions, all explained in brazilian portuguese
A python script to visualise MT5 trade history data, such as cumulative profits, wins, and losses, using matplotlib.
A toolkit for composing self-learning algorithmic trading agents
A concise summary generator for Amazon product reviews built using Transformers which maintains the original semantic essence and user sentiment
[EMNLP 2022] Discovering Language-neutral Sub-networks in Multilingual Language Models.
Simple version of auto forex trader build upon the concept of DQN
Easy-to-use framework for evaluating cross-lingual consistency of factual knowledge (Supported LLaMA, BLOOM, mT5, RoBERTa, etc.) Paper here: https://aclanthology.org/2023.emnlp-main.658/
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