pair-classification-binary-gender-bias-mitigated-roberta-snli.json

{
    "id": "pair-classification-binary-gender-bias-mitigated-roberta-snli",
    "registered_model_name": "bias_mitigator_applicator",
    "registered_predictor_name": "textual_entailment",
    "display_name": "Binary Gender Bias-Mitigated RoBERTa SNLI",
    "task_id": "textual_entailment",
    "model_details": {
        "description": "This `Model` implements a basic text classifier with a bias mitigator applicator wrapper. The text is embedded into a text field using a RoBERTa-large model. Following the static embedding layer, the embeddings are projected onto the subspace orthogonal to the binary gender bias subspace. The resulting sequence is pooled using a cls_pooler `Seq2VecEncoder` and then passed to a linear classification layer, which projects into the label space.",
        "short_description": "RoBERTa finetuned on SNLI with binary gender bias mitigation.",
        "developed_by": "Dev at al",
        "contributed_by": "Arjun Subramonian",
        "date": "2021-05-20",
        "version": "1",
        "model_type": "RoBERTa",
        "paper": {
            "citation": "\n@article{Dev2020OnMA,\ntitle={On Measuring and Mitigating Biased Inferences of Word Embeddings},\nauthor={Sunipa Dev and Tao Li and J. M. Phillips and Vivek Srikumar},\njournal={Proceedings of the AAAI Conference on Artificial Intelligence},\nyear={2020},\nvolume={34},\nnumber={05},\npages={7659-7666},\nDOI={10.1609/aaai.v34i05.6267}\n",
            "title": "On Measuring and Mitigating Biased Inferences of Word Embeddings",
            "url": "https://api.semanticscholar.org/CorpusID:201670701"
        },
        "license": null,
        "contact": "allennlp-contact@allenai.org"
    },
    "intended_use": {
        "primary_uses": null,
        "primary_users": null,
        "out_of_scope_use_cases": null
    },
    "factors": {
        "relevant_factors": null,
        "evaluation_factors": null
    },
    "metrics": {
        "model_performance_measures": "Accuracy, Net Neutral, Fraction Neutral, Threshold:tau",
        "decision_thresholds": null,
        "variation_approaches": null
    },
    "evaluation_data": {
        "dataset": {
            "name": "On Measuring and Mitigating Biased Gender-Occupation Inferences SNLI Dataset",
            "url": "https://github.com/sunipa/On-Measuring-and-Mitigating-Biased-Inferences-of-Word-Embeddings",
            "processed_url": "https://storage.googleapis.com/allennlp-public-models/binary-gender-bias-mitigated-snli-dataset.jsonl"
        },
        "motivation": null,
        "preprocessing": null
    },
    "training_data": {
        "dataset": {
            "name": "Stanford Natural Language Inference (SNLI) train set",
            "url": "https://nlp.stanford.edu/projects/snli/",
            "processed_url": "https://allennlp.s3.amazonaws.com/datasets/snli/snli_1.0_train.jsonl"
        },
        "motivation": null,
        "preprocessing": null
    },
    "quantitative_analyses": {
        "unitary_results": "Net Neutral: 0.6417539715766907, Fraction Neutral: 0.7002295255661011, Threshold:0.5: 0.6902161836624146, Threshold:0.7: 0.49243637919425964",
        "intersectional_results": null
    },
    "model_caveats_and_recommendations": {
        "caveats_and_recommendations": null
    },
    "model_ethical_considerations": {
        "ethical_considerations": "Binary gender bias mitigation has been applied to this model. Nonetheless, the model will contain residual biases and bias mitigation does not guarantee entirely bias-free inferences."
    },
    "model_usage": {
        "archive_file": "binary-gender-bias-mitigated-snli-roberta.2021-05-20.tar.gz",
        "training_config": "pair_classification/binary_gender_bias_mitigated_snli_roberta.jsonnet",
        "install_instructions": "pip install allennlp==2.5.0 allennlp-models==2.5.0"
    }
}