#7 fixed

README.md

@@ -1,8 +1,10 @@
 # Reasoning for Sentiment Analysis
-![](https://img.shields.io/badge/Python-3.8-brightgreen.svg)
+![](https://img.shields.io/badge/Python-3.10-brightgreen.svg)
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/Reasoning_for_Sentiment_Analysis_Framework.ipynb)
 [![arXiv](https://img.shields.io/badge/arXiv-2404.12342-b31b1b.svg)](https://arxiv.org/abs/2404.12342)
 
+> **Update 06 March 2024**: 🔓 `attrdict` represents the main limitation for code launching in `Python 3.10` and hence been switched to `addict` (see [Issue#7](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/issues/7)).
+
 > **🔥 Update 24/04/2024:** We released fine-tuning log for the [prompt-based](DOC_FLANT5_PROMPT_FT.md) and [THoR-based](DOC_FLANT5_THOR_FT.md) techniques applied for the `train` competition data as well as **[checkpoints](#fine-tuned-flan-t5)** for downloading. [More ...](#fine-tuned-flan-t5)
 
 > **💻 Update 19/04/2024:** We open [quick_cot](https://github.com/nicolay-r/quick_cot) code repository for lauching quick CoT zero-shot-learning / few-shot-learning experiments with LLM, utilied in this studies. [More ...](https://github.com/nicolay-r/quick_cot)

dependencies_ft.txt

@@ -1,3 +1,3 @@
-attrdict==2.0.1
+addict
 PyYAML==6.0
 scikit_learn

thor_finetune.py

@@ -1,7 +1,7 @@
 import argparse
 import os
 from os.path import join
-from attrdict import AttrDict
+from addict import Dict
 
 import yaml
 import torch
@@ -19,8 +19,9 @@
 
 
 class Template:
+
     def __init__(self, args):
-        config = AttrDict(yaml.load(open(args.config, 'r', encoding='utf-8'), Loader=yaml.FullLoader))
+        config = Dict(yaml.load(open(args.config, 'r', encoding='utf-8'), Loader=yaml.FullLoader))
         names = []
         for k, v in vars(args).items():
             setattr(config, k, v)