Update README.md

README.md

@@ -9,11 +9,11 @@ pipeline_tag: text-classification
 
 # Model Card for orYx-models/finetuned-roberta-leadership-sentiment-analysis 
 
-<!-- This model is a finetuned version of, roberta text classifier.
-The finetuning has been done on the dataset which includes inputs from corporate executives to their therapist.
-The sole purpose of the model is to determine wether the statement made from the corporate executives is "Positive, Negative, or Neutral" with which we will also see "Confidence level, i.e the percentage of the sentiment involved in a statement.
-The sentiment analysis tool has been particularly built for our client firm called "LDS".
-Since it is prototype tool by orYx Models, all the feedback and insights from LDS will be used to finetune the model further.-->
+- This model is a finetuned version of, roberta text classifier.
+- The finetuning has been done on the dataset which includes inputs from corporate executives to their therapist.
+- The sole purpose of the model is to determine wether the statement made from the corporate executives is "Positive, Negative, or Neutral" with which we will also see "Confidence level, i.e the percentage of the sentiment involved in a statement.
+- The sentiment analysis tool has been particularly built for our client firm called "LDS".
+- Since it is prototype tool by orYx Models, all the feedback and insights from LDS will be used to finetune the model further.
 
 
 
@@ -21,10 +21,9 @@ Since it is prototype tool by orYx Models, all the feedback and insights from LD
 
 ### Model Description
 
-<!-- This model is finetuned on a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, 
-and finetuned for sentiment analysis with the TweetEval benchmark.
-The original Twitter-based RoBERTa model can be found here and the original reference paper is TweetEval.
-This model is suitable for English. -->
+- This model is finetuned on a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021,and finetuned for sentiment analysis with the TweetEval benchmark.
+- The original Twitter-based RoBERTa model can be found here and the original reference paper is TweetEval.
+- This model is suitable for English.
 
 
 
@@ -37,18 +36,18 @@ This model is suitable for English. -->
 
 ### Model Sources [optional]
 
-<!--This is HuggingFace modelID - cardiffnlp/twitter-roberta-base-2021-124m-->
+This is HuggingFace modelID - cardiffnlp/twitter-roberta-base-2021-124m
 
 - **Repository:** More Information Needed
 - **Paper [optional]:** TimeLMs - https://arxiv.org/abs/2202.03829
 
 ## Uses
 
-<!-- The Sentiment Analysis tool is made domain specific, however since it is a protoype, the depths into domain are still to be ventured.
-Use case: We can analyse the text from any executive, employee, client of an organization and attach a sentiment to it.
- The outcomes of this will be a "Scored sentiment" upon which we can look for likeliness of an event occurring or vice versa.
- The resultant scenario to this will be to generate a rating system based on the sentiments generated by texts from an entity.
--->
+-The Sentiment Analysis tool is made domain specific, however since it is a protoype, the depths into domain are still to be ventured.
+
+- **Use case:** We can analyse the text from any executive, employee, client of an organization and attach a sentiment to it.
+- The outcomes of this will be a "Scored sentiment" upon which we can look for likeliness of an event occurring or vice versa.
+- The resultant scenario to this will be to generate a rating system based on the sentiments generated by texts from an entity.
 
 ### Direct Use
 
@@ -59,12 +58,6 @@ nlp("The results don't match but the effort seems to be always high")
 Out[7]: [{'label': 'Positive', 'score': 0.9996090531349182}]
 
 
-## Bias, Risks, and Limitations
-
-<!-- The model is sometimes prone to misinterpret the sentiments. -->
-
-{{ bias_risks_limitations | default("[More Information Needed]", true)}}
-
 ### Recommendations
 
 
@@ -80,6 +73,9 @@ Out[7]: [{'label': 'Positive', 'score': 0.9996090531349182}]
 
 X_train, X_val, y_train, y_val = train_test_split(X,y, test_size = 0.2, stratify = y)
 
+- **Train data:** 80% of 4396 records = 3516
+- **Test data:** 20% of 4396 records = 789
+
 
 ### Training Procedure
 
@@ -94,7 +90,7 @@ X_train, X_val, y_train, y_val = train_test_split(X,y, test_size = 0.2, stratify
 
 #### Training Hyperparameters
 
-args = TrainingArguments(
+- args = TrainingArguments(
     output_dir="output",
     do_train = True,
     do_eval = True,
@@ -201,7 +197,7 @@ Google Colab - T4 GPU
 
 ### References 
 ```
-@inproceedings{camacho-collados-etal-2022-tweetnlp,
+- @inproceedings{camacho-collados-etal-2022-tweetnlp,
     title = "{T}weet{NLP}: Cutting-Edge Natural Language Processing for Social Media",
     author = "Camacho-collados, Jose  and
       Rezaee, Kiamehr  and