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--- |
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base_model: google/flan-t5-xl |
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datasets: |
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- 2024-mcm-everitt-ryan/job-bias-synthetic-human-benchmark |
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language: en |
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license: apache-2.0 |
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model_id: flan-t5-xl-job-bias-seq2seq-cls |
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model_description: The model is a multi-label classifier designed to detect various |
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types of bias within job descriptions. |
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developers: Tristan Everitt and Paul Ryan |
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model_card_authors: See developers |
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model_card_contact: See developers |
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repo: https://gitlab.computing.dcu.ie/everitt2/2024-mcm-everitt-ryan |
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compute_infrastructure: Linux 6.5.0-35-generic x86_64 |
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software: Python 3.10.12 |
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hardware_type: x86_64 |
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hours_used: N/A |
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cloud_provider: N/A |
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cloud_region: N/A |
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co2_emitted: N/A |
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direct_use: "\n ```python\n from transformers import pipeline\n\n pipe =\ |
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\ pipeline(\"text-classification\", model=\"2024-mcm-everitt-ryan/flan-t5-xl-job-bias-seq2seq-cls\"\ |
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, return_all_scores=True)\n\n results = pipe(\"Join our dynamic and fast-paced\ |
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\ team as a Junior Marketing Specialist. We seek a tech-savvy and energetic individual\ |
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\ who thrives in a vibrant environment. Ideal candidates are digital natives with\ |
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\ a fresh perspective, ready to adapt quickly to new trends. You should have recent\ |
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\ experience in social media strategies and a strong understanding of current digital\ |
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\ marketing tools. We're looking for someone with a youthful mindset, eager to bring\ |
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\ innovative ideas to our young and ambitious team. If you're a recent graduate\ |
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\ or early in your career, this opportunity is perfect for you!\")\n print(results)\n\ |
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\ ```\n >> [[\n {'label': 'age', 'score': 0.9883460402488708}, \n {'label':\ |
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\ 'disability', 'score': 0.00787709467113018}, \n {'label': 'feminine', 'score':\ |
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\ 0.007224376779049635}, \n {'label': 'general', 'score': 0.09967829287052155},\ |
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\ \n {'label': 'masculine', 'score': 0.0035264550242573023}, \n {'label':\ |
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\ 'racial', 'score': 0.014618005603551865}, \n {'label': 'sexuality', 'score':\ |
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\ 0.005568435415625572}\n ]]\n\n\n Classification Report:\n \n \ |
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\ precision recall f1-score support\n \n age \ |
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\ 0.72 0.57 0.63 81\n sexuality 0.84 0.79\ |
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\ 0.82 81\n disability 0.70 0.60 0.65 81\n\ |
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\ masculine 0.64 0.62 0.63 81\n feminine \ |
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\ 0.84 0.89 0.86 81\n general 0.28 0.44 \ |
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\ 0.34 82\n racial 0.62 0.86 0.72 78\n\ |
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\ \n micro avg 0.63 0.68 0.65 565\n macro avg\ |
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\ 0.66 0.68 0.66 565\n weighted avg 0.66 0.68\ |
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\ 0.66 565\n samples avg 0.31 0.35 0.32 565\n\ |
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\ \n " |
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model-index: |
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- name: flan-t5-xl-job-bias-seq2seq-cls |
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results: |
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- task: |
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type: multi_label_classification |
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dataset: |
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name: 2024-mcm-everitt-ryan/job-bias-synthetic-human-benchmark |
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type: mix_human-eval_synthetic |
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metrics: |
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- type: loss |
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value: 0.6297690868377686 |
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- type: accuracy |
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value: 0.724596391263058 |
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- type: f1_micro |
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value: 0.6541737649063032 |
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- type: f1_macro |
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value: 0.6649871410336159 |
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- type: f1_samples |
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value: 0.7891104779993668 |
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- type: f1_weighted |
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value: 0.6641255154887347 |
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- type: precision_micro |
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value: 0.6305418719211823 |
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- type: precision_macro |
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value: 0.6632750205440888 |
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- type: precision_samples |
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value: 0.8839822728711617 |
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- type: precision_weighted |
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value: 0.6628306019545424 |
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- type: recall_micro |
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value: 0.679646017699115 |
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- type: recall_macro |
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value: 0.6810192216696281 |
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- type: recall_samples |
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value: 0.8638809749920862 |
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- type: recall_weighted |
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value: 0.679646017699115 |
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- type: roc_auc_micro |
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value: 0.8232934760843503 |
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- type: roc_auc_macro |
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value: 0.8239776029004718 |
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- type: runtime |
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value: 228.4627 |
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- type: samples_per_second |
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value: 4.609 |
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- type: steps_per_second |
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value: 0.578 |
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- type: epoch |
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value: 1.0 |
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--- |
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# Model Card for flan-t5-xl-job-bias-seq2seq-cls |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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The model is a multi-label classifier designed to detect various types of bias within job descriptions. |
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- **Developed by:** Tristan Everitt and Paul Ryan |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** en |
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- **License:** apache-2.0 |
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- **Finetuned from model [optional]:** google/flan-t5-xl |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://gitlab.computing.dcu.ie/everitt2/2024-mcm-everitt-ryan |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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```python |
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from transformers import pipeline |
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pipe = pipeline("text-classification", model="2024-mcm-everitt-ryan/flan-t5-xl-job-bias-seq2seq-cls", return_all_scores=True) |
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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!") |
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print(results) |
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``` |
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>> [[ |
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{'label': 'age', 'score': 0.9883460402488708}, |
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{'label': 'disability', 'score': 0.00787709467113018}, |
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{'label': 'feminine', 'score': 0.007224376779049635}, |
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{'label': 'general', 'score': 0.09967829287052155}, |
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{'label': 'masculine', 'score': 0.0035264550242573023}, |
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{'label': 'racial', 'score': 0.014618005603551865}, |
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{'label': 'sexuality', 'score': 0.005568435415625572} |
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]] |
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Classification Report: |
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precision recall f1-score support |
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age 0.72 0.57 0.63 81 |
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sexuality 0.84 0.79 0.82 81 |
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disability 0.70 0.60 0.65 81 |
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masculine 0.64 0.62 0.63 81 |
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feminine 0.84 0.89 0.86 81 |
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general 0.28 0.44 0.34 82 |
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racial 0.62 0.86 0.72 78 |
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micro avg 0.63 0.68 0.65 565 |
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macro avg 0.66 0.68 0.66 565 |
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weighted avg 0.66 0.68 0.66 565 |
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samples avg 0.31 0.35 0.32 565 |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** x86_64 |
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- **Hours used:** N/A |
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- **Cloud Provider:** N/A |
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- **Compute Region:** N/A |
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- **Carbon Emitted:** N/A |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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Linux 6.5.0-35-generic x86_64 |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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Python 3.10.12 |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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See developers |
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## Model Card Contact |
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See developers |