From 9d14dfd78f107eb428b49a9f6155c461b050c41a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Aleksandar=20Toma=C5=A1evi=C4=87?=
 <39856297+atomashevic@users.noreply.github.com>
Date: Tue, 20 Aug 2024 15:23:27 +0200
Subject: [PATCH] Update README.md

---
 README.md | 28 ++++++++++++++++++++++++----
 1 file changed, 24 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index d361719..58e74bc 100644
--- a/README.md
+++ b/README.md
@@ -129,20 +129,40 @@ The rag function supports various large language models (LLMs), including TinyLL
 Here's an example based on the decription of this package. First, we specify the text data.
 
 ```R
-text <- "With `transforEmotion` you can use cutting-edge transformer models for zero-shot emotion classification of text, image, and video in R, *all without the need for a GPU, subscriptions, paid services, or using Python. Implements sentiment analysis using [huggingface](https://huggingface.co/) transformer zero-shot classification model pipelines. The default pipeline for text is [Cross-Encoder's DistilRoBERTa](https://huggingface.co/cross-encoder/nli-distilroberta-base) trained on the [Stanford Natural Language Inference](https://huggingface.co/datasets/snli) (SNLI) and [Multi-Genre Natural Language Inference](https://huggingface.co/datasets/multi_nli) (MultiNLI) datasets. Using similar models, zero-shot classification transformers have demonstrated superior performance relative to other natural language processing models (Yin, Hay, & Roth, [2019](https://arxiv.org/abs/1909.00161)). All other zero-shot classification model pipelines can be implemented using their model name from https://huggingface.co/models?pipeline_tag=zero-shot-classification." 
+text <- "With `transforEmotion` you can use cutting-edge transformer models for zero-shot emotion
+        classification of text, image, and video in R, *all without the need for a GPU,
+        subscriptions, paid services, or using Python. Implements sentiment analysis
+        using [huggingface](https://huggingface.co/) transformer zero-shot classification model pipelines.
+        The default pipeline for text is
+        [Cross-Encoder's DistilRoBERTa](https://huggingface.co/cross-encoder/nli-distilroberta-base)
+        trained on the [Stanford Natural Language Inference](https://huggingface.co/datasets/snli) (SNLI) and
+        [Multi-Genre Natural Language Inference](https://huggingface.co/datasets/multi_nli) (MultiNLI) datasets.
+        Using similar models, zero-shot classification transformers have demonstrated superior performance
+        relative to other natural language processing models
+        (Yin, Hay, & Roth, [2019](https://arxiv.org/abs/1909.00161)).
+        All other zero-shot classification model pipelines can be implemented using their model name
+        from https://huggingface.co/models?pipeline_tag=zero-shot-classification." 
 ```
 
 And then we run the `rag` function.
 
 ```R
- rag(text, query = "What is the use case for transforEmotion package?"
-+ )
+ rag(text, query = "What is the use case for transforEmotion package?")
 ```
 
 This code will provide the output similar to this one.
 
 ```
-The use case for transforEmotion package is to use cutting-edge transformer models for zero-shot emotion classification of text, image, and video in R, without the need for a GPU, subscriptions, paid services, or using Python. This package implements sentiment analysis using the Cross-Encoder's DistilRoBERTa model trained on the Stanford Natural Language Inference (SNLI) and MultiNLI datasets. Using similar models, zero-shot classification transformers have demonstrated superior performance relative to other natural language processing models (Yin, Hay, & Roth, [2019](https://arxiv.org/abs/1909.00161)). The transforEmotion package can be used to implement these models and other zero-shot classification model pipelines from the HuggingFace library.> 
+The use case for transforEmotion package is to use cutting-edge transformer
+models forzero-shot emotion classification of text, image, and video in R,
+without the need for a GPU, subscriptions, paid services, or using Python.
+This package implements sentiment analysis using the Cross-Encoder's DistilRoBERTa
+model trained on the Stanford Natural Language Inference (SNLI) and MultiNLI datasets.
+Using similar models, zero-shot classification transformers have demonstrated
+superior performance relative to other natural language processing models
+(Yin, Hay, & Roth, [2019](https://arxiv.org/abs/1909.00161)).
+The transforEmotion package can be used to implement these models and other
+zero-shot classification model pipelines from the HuggingFace library.> 
 ```
 
 ## Image Example