Update README.md

README.md

@@ -41,10 +41,9 @@ The model has been trained using an efficient few-shot learning technique that i
 
 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
 2. Training a classification head with features from the fine-tuned Sentence Transformer.
+3. The Training data can be downloaded from [here](https://docs.google.com/spreadsheets/d/1BVDardwVs04ZWmc5_Eg62Lyr_w_OuXysQwhne8ErkoA/edit?usp=sharing)
 
-The Training data can be downloaded from [here](https://docs.google.com/spreadsheets/d/1BVDardwVs04ZWmc5_Eg62Lyr_w_OuXysQwhne8ErkoA/edit?usp=sharing)
-
-## Usage
+## Usage and Inference
 
 To use this model for inference, first install the SetFit library:
 
@@ -56,17 +55,15 @@ pip install -U sentence_transformers
 
 For a global overview of the pipeline used for inference please refer to this [colab notebook](https://colab.research.google.com/drive/1GgEGrhQZfA1pbcB9Zl0VtV7L5wXdh6vj?usp=sharing)
 
+
 ## BibTeX entry and citation info
 
 ```bibtex
-@article{https://doi.org/10.48550/arxiv.2209.11055,
-doi = {10.48550/ARXIV.2209.11055},
-url = {https://arxiv.org/abs/2209.11055},
-author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+author = {HasiMichael, Solofo},
 keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
-title = {Efficient Few-Shot Learning Without Prompts},
+title = {Sentiment Classification toward Entity and Topics},
 publisher = {arXiv},
-year = {2022},
+year = {2023},
 copyright = {Creative Commons Attribution 4.0 International}
 }
 ```