Update app.py

app.py

@@ -10,10 +10,14 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipe
 import tensorflow as tf
 tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
 
-# LICESNE.tensorflow - https://github.com/tensorflow/tensorflow/blob/master/LICENSE
-# LICENSE.Roberta -https://github.com/cardiffnlp/xlm-t/blob/main/LICENSE
-# LICENSE.streamlit - https://github.com/streamlit/streamlit/blob/develop/LICENSE
-# LICENSE.tranformers - https://github.com/huggingface/huggingface_hub/blob/main/LICENSE
+# LICENSE.streamlit.Apachev2 	- 	Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022-2024) (https://github.com/streamlit/streamlit/blob/develop/LICENSE)
+
+# LICENSE.tranformers.Apachev2	- 	Copyright 2020 The HuggingFace Team. All rights reserved. (https://github.com/huggingface/huggingface_hub/blob/main/LICENSE)
+
+# LICESNE.tensorflow.Apachev2	- 	Copyright 2015 The TensorFlow Authors. All Rights Reserved. (https://github.com/tensorflow/tensorflow/blob/master/LICENSE)
+
+# LICENSE.Roberta.Apachev2 		-	@InProceedings{barbieri-espinosaanke-camachocollados:2022:LREC,author    = {Barbieri, Francesco  and  Espinosa Anke, Luis  and  Camacho-Collados, Jose}, title     = {XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond}, booktitle      = {Proceedings of the Language Resources and Evaluation Conference}, month          = {June}, year           = {2022}, address        = {Marseille, France}, publisher      = {European Language Resources Association}, pages     = {258--266}, abstract  = {Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention. However, current analyses have almost exclusively focused on (multilingual variants of) standard benchmarks, and have relied on clean pre-training and task-specific corpora as multilingual signals. In this paper, we introduce XLM-T, a model to train and evaluate multilingual language models in Twitter. In this paper we provide: (1) a new strong multilingual baseline consisting of an XLM-R (Conneau et al. 2020) model pre-trained on millions of tweets in over thirty languages, alongside starter code to subsequently fine-tune on a target task; and (2) a set of unified sentiment analysis Twitter datasets in eight different languages and a XLM-T model trained on this dataset.}, url       = {https://aclanthology.org/2022.lrec-1.27}} (https://github.com/cardiffnlp/xlm-t/blob/main/LICENSE)
+
 
 # Load pre-trained model and tokenizer
 saved_directory = "orYx-models/finetuned-roberta-leadership-sentiment-analysis"