Update README.md

README.md

@@ -194,28 +194,41 @@ model-index:
 The model is a multi-label classifier designed to detect various types of bias within job descriptions.
 
 - **Developed by:** Tristan Everitt and Paul Ryan
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
+- **Model type:** Encoder
 - **Language(s) (NLP):** en
 - **License:** apache-2.0
 - **Finetuned from model [optional]:** google-bert/bert-base-uncased
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** https://gitlab.computing.dcu.ie/everitt2/2024-mcm-everitt-ryan
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://github.com/2024-mcm-everitt-ryan
+- **Paper:** In Progress
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+The primary target audience for these models are researchers dedicated to identifying biased language in job descriptions.
+
+### Out-of-Scope Use
 
-### Direct Use
+Due to the limitations inherent in large-scale language models, they should not be utilised in applications requiring factual or accurate outputs. These models do not distinguish between fact and fiction, and implicit biases are inherently subjective.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Moreover, as language models mirror the biases present in their training data, they should not be deployed in systems that directly interact with humans unless the deployers have first conducted a thorough analysis of relevant biases for the specific use case.
+
+## Bias, Risks, and Limitations
+
+It is imperative that all users, both direct and downstream, are aware of the risks, biases, and limitations associated with this model. Important considerations include:
+
+- Bias in Training Data: The model may inherit and perpetuate biases from the data it was trained on.
+- Subjectivity of Bias: Bias detection is inherently subjective, and perceptions of bias can differ across contexts and users.
+- Accuracy Concerns: The model’s outputs are not guaranteed to be true or accurate, making it unsuitable for applications that require reliable information.
+- Human Interaction Risks: When incorporated into systems that interact with humans, the model’s biases may affect interactions and decision-making, potentially leading to unintended consequences.
+
+It is crucial for users to conduct comprehensive evaluations and consider these factors when applying the model in any context.
+
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
 
 
     ```python
@@ -235,37 +248,6 @@ The model is a multi-label classifier designed to detect various types of bias w
     {'label': 'racial', 'score': 0.014618005603551865}, 
     {'label': 'sexuality', 'score': 0.005568435415625572}
     ]]
-    
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
 
 ## Training Details