Hugging Face model: 2024-mcm-everitt-ryan/flan-t5-xl-job-bias-seq2seq-cls
Author: 2024-mcm-everitt-ryan
Created: 2024-07-12 00:50:52
Last modified: 2024-07-30 12:02:35
Library: transformers
Likes: 0 | Downloads: 3
Tags: transformers, safetensors, t5, text2text-generation, en, dataset:2024-mcm-everitt-ryan/job-bias-synthetic-human-benchmark-v2, arxiv:1910.09700, base_model:google/flan-t5-xl, base_model:finetune:google/flan-t5-xl, license:apache-2.0, model-index, text-generation-inference, endpoints_compatible, region:us
Base model relation: finetune
Base models: id: google/flan-t5-xl
Card metadata:
base_model: google/flan-t5-xl
datasets: 2024-mcm-everitt-ryan/job-bias-synthetic-human-benchmark-v2
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: 
    ```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
    
      disability       0.88      0.46      0.61        80
        feminine       0.94      0.91      0.92        80
         neutral       0.37      0.93      0.53        80
       masculine       0.85      0.65      0.74        80
       sexuality       1.00      0.74      0.85        80
          racial       0.92      0.76      0.84        80
             age       0.90      0.66      0.76        80
         general       0.78      0.57      0.66        80
    
       micro avg       0.73      0.71      0.72       640
       macro avg       0.83      0.71      0.74       640
    weighted avg       0.83      0.71      0.74       640
     samples avg       0.74      0.76      0.75       640