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README.md

@@ -1,9 +1,101 @@
 ---
-library_name: transformers
-tags: []
+base_model: google/flan-t5-xl
+datasets:
+- 2024-mcm-everitt-ryan/job-bias-synthetic-human-benchmark
+language: en
+license: apache-2.0
+model_id: flan-t5-xl-job-bias-seq2seq-cls
+model_description: The model is a multi-label classifier designed to detect various
+  types of bias within job descriptions.
+developers: Tristan Everitt and Paul Ryan
+model_card_authors: See developers
+model_card_contact: See developers
+repo: https://gitlab.computing.dcu.ie/everitt2/2024-mcm-everitt-ryan
+compute_infrastructure: Linux 6.5.0-35-generic x86_64
+software: Python 3.10.12
+hardware_type: x86_64
+hours_used: N/A
+cloud_provider: N/A
+cloud_region: N/A
+co2_emitted: N/A
+direct_use: "\n    ```python\n    from transformers import pipeline\n\n    pipe =\
+  \ pipeline(\"text-classification\", model=\"2024-mcm-everitt-ryan/flan-t5-xl-job-bias-seq2seq-cls\"\
+  , return_all_scores=True)\n\n    results = pipe(\"Join our dynamic and fast-paced\
+  \ team as a Junior Marketing Specialist. We seek a tech-savvy and energetic individual\
+  \ who thrives in a vibrant environment. Ideal candidates are digital natives with\
+  \ a fresh perspective, ready to adapt quickly to new trends. You should have recent\
+  \ experience in social media strategies and a strong understanding of current digital\
+  \ marketing tools. We're looking for someone with a youthful mindset, eager to bring\
+  \ innovative ideas to our young and ambitious team. If you're a recent graduate\
+  \ or early in your career, this opportunity is perfect for you!\")\n    print(results)\n\
+  \    ```\n    >> [[\n    {'label': 'age', 'score': 0.9883460402488708}, \n    {'label':\
+  \ 'disability', 'score': 0.00787709467113018}, \n    {'label': 'feminine', 'score':\
+  \ 0.007224376779049635}, \n    {'label': 'general', 'score': 0.09967829287052155},\
+  \ \n    {'label': 'masculine', 'score': 0.0035264550242573023}, \n    {'label':\
+  \ 'racial', 'score': 0.014618005603551865}, \n    {'label': 'sexuality', 'score':\
+  \ 0.005568435415625572}\n    ]]\n\n\n    Classification Report:\n    \n        \
+  \              precision    recall  f1-score   support\n    \n             age \
+  \      0.72      0.57      0.63        81\n       sexuality       0.84      0.79\
+  \      0.82        81\n      disability       0.70      0.60      0.65        81\n\
+  \       masculine       0.64      0.62      0.63        81\n        feminine   \
+  \    0.84      0.89      0.86        81\n         general       0.28      0.44 \
+  \     0.34        82\n          racial       0.62      0.86      0.72        78\n\
+  \    \n       micro avg       0.63      0.68      0.65       565\n       macro avg\
+  \       0.66      0.68      0.66       565\n    weighted avg       0.66      0.68\
+  \      0.66       565\n     samples avg       0.31      0.35      0.32       565\n\
+  \    \n    "
+model-index:
+- name: flan-t5-xl-job-bias-seq2seq-cls
+  results:
+  - task:
+      type: multi_label_classification
+    dataset:
+      name: 2024-mcm-everitt-ryan/job-bias-synthetic-human-benchmark
+      type: mix_human-eval_synthetic
+    metrics:
+    - type: loss
+      value: 0.6297690868377686
+    - type: accuracy
+      value: 0.724596391263058
+    - type: f1_micro
+      value: 0.6541737649063032
+    - type: f1_macro
+      value: 0.6649871410336159
+    - type: f1_samples
+      value: 0.7891104779993668
+    - type: f1_weighted
+      value: 0.6641255154887347
+    - type: precision_micro
+      value: 0.6305418719211823
+    - type: precision_macro
+      value: 0.6632750205440888
+    - type: precision_samples
+      value: 0.8839822728711617
+    - type: precision_weighted
+      value: 0.6628306019545424
+    - type: recall_micro
+      value: 0.679646017699115
+    - type: recall_macro
+      value: 0.6810192216696281
+    - type: recall_samples
+      value: 0.8638809749920862
+    - type: recall_weighted
+      value: 0.679646017699115
+    - type: roc_auc_micro
+      value: 0.8232934760843503
+    - type: roc_auc_macro
+      value: 0.8239776029004718
+    - type: runtime
+      value: 228.4627
+    - type: samples_per_second
+      value: 4.609
+    - type: steps_per_second
+      value: 0.578
+    - type: epoch
+      value: 1.0
 ---
 
-# Model Card for Model ID
+# Model Card for flan-t5-xl-job-bias-seq2seq-cls
 
 <!-- Provide a quick summary of what the model is/does. -->
 
@@ -15,21 +107,21 @@ tags: []
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+The model is a multi-label classifier designed to detect various types of bias within job descriptions.
 
-- **Developed by:** [More Information Needed]
+- **Developed by:** Tristan Everitt and Paul Ryan
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
 - **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Language(s) (NLP):** en
+- **License:** apache-2.0
+- **Finetuned from model [optional]:** google/flan-t5-xl
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
+- **Repository:** https://gitlab.computing.dcu.ie/everitt2/2024-mcm-everitt-ryan
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
@@ -41,7 +133,44 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+
+    ```python
+    from transformers import pipeline
+
+    pipe = pipeline("text-classification", model="2024-mcm-everitt-ryan/flan-t5-xl-job-bias-seq2seq-cls", return_all_scores=True)
+
+    results = pipe("Join our dynamic and fast-paced team as a Junior Marketing Specialist. We seek a tech-savvy and energetic individual who thrives in a vibrant environment. Ideal candidates are digital natives with a fresh perspective, ready to adapt quickly to new trends. You should have recent experience in social media strategies and a strong understanding of current digital marketing tools. We're looking for someone with a youthful mindset, eager to bring innovative ideas to our young and ambitious team. If you're a recent graduate or early in your career, this opportunity is perfect for you!")
+    print(results)
+    ```
+    >> [[
+    {'label': 'age', 'score': 0.9883460402488708}, 
+    {'label': 'disability', 'score': 0.00787709467113018}, 
+    {'label': 'feminine', 'score': 0.007224376779049635}, 
+    {'label': 'general', 'score': 0.09967829287052155}, 
+    {'label': 'masculine', 'score': 0.0035264550242573023}, 
+    {'label': 'racial', 'score': 0.014618005603551865}, 
+    {'label': 'sexuality', 'score': 0.005568435415625572}
+    ]]
+
+
+    Classification Report:
+    
+                      precision    recall  f1-score   support
+    
+             age       0.72      0.57      0.63        81
+       sexuality       0.84      0.79      0.82        81
+      disability       0.70      0.60      0.65        81
+       masculine       0.64      0.62      0.63        81
+        feminine       0.84      0.89      0.86        81
+         general       0.28      0.44      0.34        82
+          racial       0.62      0.86      0.72        78
+    
+       micro avg       0.63      0.68      0.65       565
+       macro avg       0.66      0.68      0.66       565
+    weighted avg       0.66      0.68      0.66       565
+     samples avg       0.31      0.35      0.32       565
+    
+    
 
 ### Downstream Use [optional]
 
@@ -144,11 +273,11 @@ Use the code below to get started with the model.
 
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- **Hardware Type:** x86_64
+- **Hours used:** N/A
+- **Cloud Provider:** N/A
+- **Compute Region:** N/A
+- **Carbon Emitted:** N/A
 
 ## Technical Specifications [optional]
 
@@ -158,7 +287,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ### Compute Infrastructure
 
-[More Information Needed]
+Linux 6.5.0-35-generic x86_64
 
 #### Hardware
 
@@ -166,7 +295,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 #### Software
 
-[More Information Needed]
+Python 3.10.12
 
 ## Citation [optional]
 
@@ -192,8 +321,8 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+See developers
 
 ## Model Card Contact
 
-[More Information Needed]
+See developers