--- 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: "\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 disability \ \ 0.88 0.46 0.61 80\n feminine 0.94 0.91\ \ 0.92 80\n neutral 0.37 0.93 0.53 80\n\ \ masculine 0.85 0.65 0.74 80\n sexuality \ \ 1.00 0.74 0.85 80\n racial 0.92 0.76 \ \ 0.84 80\n age 0.90 0.66 0.76 80\n\ \ general 0.78 0.57 0.66 80\n \n micro avg\ \ 0.73 0.71 0.72 640\n macro avg 0.83 0.71\ \ 0.74 640\n weighted avg 0.83 0.71 0.74 640\n\ \ samples avg 0.74 0.76 0.75 640\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-v2 type: mix_human-eval_synthetic metrics: - type: loss value: 0.5446141958236694 - type: accuracy value: 0.7157534246575342 - type: f1_micro value: 0.7205067300079177 - type: precision_micro value: 0.7303370786516854 - type: recall_micro value: 0.7109375 - type: roc_auc_micro value: 0.8346354166666666 - type: f1_macro value: 0.7384575431979781 - type: precision_macro value: 0.8304220819578764 - type: recall_macro value: 0.7109375 - type: roc_auc_macro value: 0.8346354166666667 - type: f1_samples value: 0.7474722765818657 - type: precision_samples value: 0.7448630136986302 - type: recall_samples value: 0.7575627853881278 - type: roc_auc_samples value: 0.8572794357469016 - type: f1_weighted value: 0.738457543197978 - type: precision_weighted value: 0.8304220819578763 - type: recall_weighted value: 0.7109375 - type: roc_auc_weighted value: 0.8346354166666666 - type: runtime value: 125.4002 - type: samples_per_second value: 4.657 - type: steps_per_second value: 0.582 - type: epoch value: 3.0 --- # Model Card for flan-t5-xl-job-bias-seq2seq-cls ## Model Details The model is a multi-label classifier designed to detect various types of bias within job descriptions. NOTE: This model was not used in the paper and it was trained without the use of QLoRA. It serves only as a comparison for the model trained using QLoRA: [flan-t5-xl-job-bias-qlora-seq2seq-cls](https://huggingface.co/2024-mcm-everitt-ryan/flan-t5-xl-job-bias-qlora-seq2seq-cls) - **Developed by:** Tristan Everitt and Paul Ryan - **Model type:** Encoder-Decoder - **Language(s) (NLP):** en - **License:** apache-2.0 - **Finetuned from model:** google/flan-t5-xl ### Model Sources - **Repository:** https://github.com/2024-mcm-everitt-ryan - **Paper:** In Progress ## Uses The primary target audience for these models are researchers dedicated to identifying biased language in job descriptions. ### Out-of-Scope 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. 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 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} ]] ## Training Details ### Training Data - [2024-mcm-everitt-ryan/benchmark](https://huggingface.co/datasets/2024-mcm-everitt-ryan/benchmark) ### Results 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 ## Environmental Impact 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:** x86_64 - **Hours used:** 2.08 - **Cloud Provider:** N/A - **Compute Region:** N/A - **Carbon Emitted:** N/A ### Compute Infrastructure - Linux 5.15.0-78-generic x86_64 - MemTotal: 1056619068 kB - 256 X AMD EPYC 7702 64-Core Processor - GPU_0: NVIDIA L40S #### Software python 3.10.12, accelerate 0.32.1, aiohttp 3.9.5, aiosignal 1.3.1, anyio 4.2.0, argon2-cffi 23.1.0, argon2-cffi-bindings 21.2.0, arrow 1.3.0, asttokens 2.4.1, async-lru 2.0.4, async-timeout 4.0.3, attrs 23.2.0, awscli 1.33.26, Babel 2.14.0, beautifulsoup4 4.12.3, bitsandbytes 0.43.1, bleach 6.1.0, blinker 1.4, botocore 1.34.144, certifi 2024.2.2, cffi 1.16.0, charset-normalizer 3.3.2, click 8.1.7, cloudpickle 3.0.0, colorama 0.4.6, comm 0.2.1, cryptography 3.4.8, dask 2024.7.0, datasets 2.20.0, dbus-python 1.2.18, debugpy 1.8.0, decorator 5.1.1, defusedxml 0.7.1, dill 0.3.8, distro 1.7.0, docutils 0.16, einops 0.8.0, entrypoints 0.4, evaluate 0.4.2, exceptiongroup 1.2.0, executing 2.0.1, fastjsonschema 2.19.1, filelock 3.13.1, flash-attn 2.6.1, fqdn 1.5.1, frozenlist 1.4.1, fsspec 2024.2.0, h11 0.14.0, hf_transfer 0.1.6, httpcore 1.0.2, httplib2 0.20.2, httpx 0.26.0, huggingface-hub 0.23.4, idna 3.6, importlib_metadata 8.0.0, iniconfig 2.0.0, ipykernel 6.29.0, ipython 8.21.0, ipython-genutils 0.2.0, ipywidgets 8.1.1, isoduration 20.11.0, jedi 0.19.1, jeepney 0.7.1, Jinja2 3.1.3, jmespath 1.0.1, joblib 1.4.2, json5 0.9.14, jsonpointer 2.4, jsonschema 4.21.1, jsonschema-specifications 2023.12.1, jupyter-archive 3.4.0, jupyter_client 7.4.9, jupyter_contrib_core 0.4.2, jupyter_contrib_nbextensions 0.7.0, jupyter_core 5.7.1, jupyter-events 0.9.0, jupyter-highlight-selected-word 0.2.0, jupyter-lsp 2.2.2, jupyter-nbextensions-configurator 0.6.3, jupyter_server 2.12.5, jupyter_server_terminals 0.5.2, jupyterlab 4.1.0, jupyterlab_pygments 0.3.0, jupyterlab_server 2.25.2, jupyterlab-widgets 3.0.9, keyring 23.5.0, launchpadlib 1.10.16, lazr.restfulclient 0.14.4, lazr.uri 1.0.6, locket 1.0.0, lxml 5.1.0, MarkupSafe 2.1.5, matplotlib-inline 0.1.6, mistune 3.0.2, more-itertools 8.10.0, mpmath 1.3.0, multidict 6.0.5, multiprocess 0.70.16, nbclassic 1.0.0, nbclient 0.9.0, nbconvert 7.14.2, nbformat 5.9.2, nest-asyncio 1.6.0, networkx 3.2.1, nltk 3.8.1, notebook 6.5.5, notebook_shim 0.2.3, numpy 1.26.3, nvidia-cublas-cu12 12.1.3.1, nvidia-cuda-cupti-cu12 12.1.105, nvidia-cuda-nvrtc-cu12 12.1.105, nvidia-cuda-runtime-cu12 12.1.105, nvidia-cudnn-cu12 8.9.2.26, nvidia-cufft-cu12 11.0.2.54, nvidia-curand-cu12 10.3.2.106, nvidia-cusolver-cu12 11.4.5.107, nvidia-cusparse-cu12 12.1.0.106, nvidia-nccl-cu12 2.19.3, nvidia-nvjitlink-cu12 12.3.101, nvidia-nvtx-cu12 12.1.105, oauthlib 3.2.0, overrides 7.7.0, packaging 23.2, pandas 2.2.2, pandocfilters 1.5.1, parso 0.8.3, partd 1.4.2, peft 0.11.1, pexpect 4.9.0, pillow 10.2.0, pip 24.1.2, platformdirs 4.2.0, pluggy 1.5.0, polars 1.1.0, prometheus-client 0.19.0, prompt-toolkit 3.0.43, protobuf 5.27.2, psutil 5.9.8, ptyprocess 0.7.0, pure-eval 0.2.2, pyarrow 16.1.0, pyarrow-hotfix 0.6, pyasn1 0.6.0, pycparser 2.21, Pygments 2.17.2, PyGObject 3.42.1, PyJWT 2.3.0, pyparsing 2.4.7, pytest 8.2.2, python-apt 2.4.0+ubuntu3, python-dateutil 2.8.2, python-json-logger 2.0.7, pytz 2024.1, PyYAML 6.0.1, pyzmq 24.0.1, referencing 0.33.0, regex 2024.5.15, requests 2.32.3, rfc3339-validator 0.1.4, rfc3986-validator 0.1.1, rpds-py 0.17.1, rsa 4.7.2, s3transfer 0.10.2, safetensors 0.4.3, scikit-learn 1.5.1, scipy 1.14.0, SecretStorage 3.3.1, Send2Trash 1.8.2, sentence-transformers 3.0.1, sentencepiece 0.2.0, setuptools 69.0.3, six 1.16.0, sniffio 1.3.0, soupsieve 2.5, stack-data 0.6.3, sympy 1.12, tabulate 0.9.0, terminado 0.18.0, threadpoolctl 3.5.0, tiktoken 0.7.0, tinycss2 1.2.1, tokenizers 0.19.1, tomli 2.0.1, toolz 0.12.1, torch 2.2.0, torchaudio 2.2.0, torchdata 0.7.1, torchtext 0.17.0, torchvision 0.17.0, tornado 6.4, tqdm 4.66.4, traitlets 5.14.1, transformers 4.42.4, triton 2.2.0, types-python-dateutil 2.8.19.20240106, typing_extensions 4.9.0, tzdata 2024.1, uri-template 1.3.0, urllib3 2.2.2, wadllib 1.3.6, wcwidth 0.2.13, webcolors 1.13, webencodings 0.5.1, websocket-client 1.7.0, wheel 0.42.0, widgetsnbextension 4.0.9, xxhash 3.4.1, yarl 1.9.4, zipp 1.0.0 ## Citation **BibTeX:** In Progress