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Updating pair classification model cards (#160)
* updating pair classification model cards * Update allennlp_models/modelcards/pair-classification-esim.json Co-authored-by: Evan Pete Walsh <[email protected]> Co-authored-by: Evan Pete Walsh <[email protected]>
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allennlp_models/modelcards/pair-classification-decomposable-attention-elmo.json
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| { | ||
| "id": "pair-classification-decomposable-attention-elmo", | ||
| "registered_model_name": "decomposable_attention", | ||
| "registered_predictor_name": "textual_entailment", | ||
| "display_name": "ELMo-based Decomposable Attention", | ||
| "archive_file": "decomposable-attention-elmo-2020.04.09.tar.gz" | ||
| "archive_file": "decomposable-attention-elmo-2020.04.09.tar.gz", | ||
| "model_details": { | ||
| "description": "This `Model` implements the Decomposable Attention model described in [A Decomposable | ||
| Attention Model for Natural Language Inference](https://api.semanticscholar.org/CorpusID:8495258) | ||
| by Parikh et al., 2016, with some optional enhancements before the decomposable attention | ||
| actually happens. Parikh's original model allowed for computing an \"intra-sentence\" attention | ||
| before doing the decomposable entailment step. We generalize this to any | ||
| [`Seq2SeqEncoder`](../modules/seq2seq_encoders/seq2seq_encoder.md) that can be applied to | ||
| the premise and/or the hypothesis before computing entailment. | ||
|
|
||
| The basic outline of this model is to get an embedded representation of each word in the | ||
| premise and hypothesis, align words between the two, compare the aligned phrases, and make a | ||
| final entailment decision based on this aggregated comparison. Each step in this process uses | ||
| a feedforward network to modify the representation. | ||
|
|
||
| This model uses ELMo embeddings.", | ||
| "developed_by": "Parikh et al", | ||
| "contributed_by": "Dirk Groeneveld", | ||
| "date": "2020-04-09", | ||
| "version": "1", | ||
| "model_type": "Seq2Seq", | ||
| "paper": "[A Decomposable Attention Model for Natural Language Inference](https://api.semanticscholar.org/CorpusID:8495258)", | ||
| "citation": "@article{Parikh2016ADA, | ||
| title={A Decomposable Attention Model for Natural Language Inference}, | ||
| author={Ankur P. Parikh and Oscar T{\"a}ckstr{\"o}m and Dipanjan Das and Jakob Uszkoreit}, | ||
| journal={ArXiv}, | ||
| year={2016}, | ||
| volume={abs/1606.01933}}", | ||
| "license": null, | ||
| "contact": "[email protected]", | ||
| "training_config": "decomposable_attention_elmo.jsonnet", | ||
| }, | ||
| "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", | ||
| "decision_thresholds": null, | ||
| "variation_approaches": null | ||
| }, | ||
| "evaluation_data": { | ||
| "dataset": "[Stanford Natural Language Inference (SNLI)](https://nlp.stanford.edu/projects/snli/) dev set", | ||
| "motivation": null, | ||
| "preprocessing": null | ||
| }, | ||
| "training_data": { | ||
| "dataset": "[Stanford Natural Language Inference (SNLI)](https://nlp.stanford.edu/projects/snli/) train set", | ||
| "motivation": null, | ||
| "preprocessing": null | ||
| }, | ||
| "quantitative_analyses": { | ||
| "unitary_results": null, | ||
| "intersectional_results": null | ||
| }, | ||
| "ethical_considerations": { | ||
| "ethical_considerations": null | ||
| }, | ||
| "caveats_and_recommendations": { | ||
| "caveats_and_recommendations": null | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,59 @@ | ||
| { | ||
| "id": "pair-classification-esim", | ||
| "registered_model_name": "esim", | ||
| "registered_predictor_name": "textual_entailment", | ||
| "display_name": "Enhanced LSTM for Natural Language Inference", | ||
| "archive_file": "decomposable-attention-elmo-2020.04.09.tar.gz" | ||
| "archive_file": "esim-elmo-2020.02.10.tar.gz", | ||
| "model_details": { | ||
| "description": "This `Model` implements the ESIM model, which is a sequential neural inference model | ||
| based on chain LSTMs.", | ||
| "developed_by": "Chen et al", | ||
| "contributed_by": "Dirk Groeneveld", | ||
| "date": "2020-04-09", | ||
| "version": "1", | ||
| "model_type": "LSTM", | ||
| "paper": "[Enhanced LSTM for Natural Language Inference](https://api.semanticscholar.org/CorpusID:34032948)", | ||
| "citation": "@inproceedings{Chen2017EnhancedLF, | ||
| title={Enhanced LSTM for Natural Language Inference}, | ||
| author={Qian Chen and Xiao-Dan Zhu and Z. Ling and Si Wei and Hui Jiang and Diana Inkpen}, | ||
| booktitle={ACL}, | ||
| year={2017}}", | ||
| "license": null, | ||
| "contact": "[email protected]", | ||
| "training_config": "esim.jsonnet", | ||
| }, | ||
| "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", | ||
| "decision_thresholds": null, | ||
| "variation_approaches": null | ||
| }, | ||
| "evaluation_data": { | ||
| "dataset": "[Stanford Natural Language Inference (SNLI)](https://nlp.stanford.edu/projects/snli/) dev set", | ||
| "motivation": null, | ||
| "preprocessing": null | ||
| }, | ||
| "training_data": { | ||
| "dataset": "[Stanford Natural Language Inference (SNLI)](https://nlp.stanford.edu/projects/snli/) train set", | ||
| "motivation": null, | ||
| "preprocessing": null | ||
| }, | ||
| "quantitative_analyses": { | ||
| "unitary_results": null, | ||
| "intersectional_results": null | ||
| }, | ||
| "ethical_considerations": { | ||
| "ethical_considerations": null | ||
| }, | ||
| "caveats_and_recommendations": { | ||
| "caveats_and_recommendations": null | ||
| } | ||
| } |
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|
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| "display_name": "RoBERTa MNLI", | ||
| "archive_file": "mnli-roberta-2020-07-29.tar.gz", | ||
| "model_details": { | ||
| "paper": "https://www.semanticscholar.org/paper/RoBERTa%3A-A-Robustly-Optimized-BERT-Pretraining-Liu-Ott/077f8329a7b6fa3b7c877a57b81eb6c18b5f87de#paper-header" | ||
| "description": "This `Model` implements a basic text classifier. The text is embedded into a text field | ||
| using a RoBERTa-large model. The resulting sequence is pooled using a cls_pooler | ||
| `Seq2VecEncoder` and then passed to a linear classification layer, which projects | ||
| into the label space.", | ||
| "developed_by": "Liu et al", | ||
| "contributed_by": "Dirk Groeneveld", | ||
| "date": "2020-07-29", | ||
| "version": "1", | ||
| "model_type": "RoBERTa", | ||
| "paper": "[RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://api.semanticscholar.org/CorpusID:198953378)", | ||
| "citation": "@article{Liu2019RoBERTaAR, | ||
| title={RoBERTa: A Robustly Optimized BERT Pretraining Approach}, | ||
| author={Y. Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and M. Lewis and Luke Zettlemoyer and Veselin Stoyanov}, | ||
| journal={ArXiv}, | ||
| year={2019}, | ||
| volume={abs/1907.11692}}", | ||
| "license": null, | ||
| "contact": "[email protected]", | ||
| "training_config": "snli_roberta.jsonnet", | ||
| }, | ||
| "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", | ||
| "decision_thresholds": null, | ||
| "variation_approaches": null | ||
| }, | ||
| "evaluation_data": { | ||
| "dataset": "[Multi-genre Natural Language Inference (MultiNLI)](https://cims.nyu.edu/~sbowman/multinli/) dev set", | ||
| "motivation": null, | ||
| "preprocessing": null | ||
| }, | ||
| "training_data": { | ||
| "dataset": "[Multi-genre Natural Language Inference (MultiNLI)](https://cims.nyu.edu/~sbowman/multinli/) train set", | ||
| "motivation": null, | ||
| "preprocessing": null | ||
| }, | ||
| "quantitative_analyses": { | ||
| "unitary_results": null, | ||
| "intersectional_results": null | ||
| }, | ||
| "ethical_considerations": { | ||
| "ethical_considerations": null | ||
| }, | ||
| "caveats_and_recommendations": { | ||
| "caveats_and_recommendations": null | ||
| } | ||
| } | ||
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|---|---|---|
|
|
@@ -5,6 +5,58 @@ | |
| "display_name": "RoBERTa SNLI", | ||
| "archive_file": "snli-roberta-2020-07-29.tar.gz", | ||
| "model_details": { | ||
| "paper": "https://www.semanticscholar.org/paper/RoBERTa%3A-A-Robustly-Optimized-BERT-Pretraining-Liu-Ott/077f8329a7b6fa3b7c877a57b81eb6c18b5f87de#paper-header" | ||
| "description": "This `Model` implements a basic text classifier. The text is embedded into a text field | ||
| using a RoBERTa-large model. The resulting sequence is pooled using a cls_pooler | ||
| `Seq2VecEncoder` and then passed to a linear classification layer, which projects | ||
| into the label space.", | ||
| "developed_by": "Liu et al", | ||
| "contributed_by": "Dirk Groeneveld", | ||
| "date": "2020-07-29", | ||
| "version": "1", | ||
| "model_type": "RoBERTa", | ||
| "paper": "[RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://api.semanticscholar.org/CorpusID:198953378)", | ||
| "citation": "@article{Liu2019RoBERTaAR, | ||
| title={RoBERTa: A Robustly Optimized BERT Pretraining Approach}, | ||
| author={Y. Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and M. Lewis and Luke Zettlemoyer and Veselin Stoyanov}, | ||
| journal={ArXiv}, | ||
| year={2019}, | ||
| volume={abs/1907.11692}}", | ||
| "license": null, | ||
| "contact": "[email protected]", | ||
| "training_config": "snli_roberta.jsonnet", | ||
| }, | ||
| "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", | ||
| "decision_thresholds": null, | ||
| "variation_approaches": null | ||
| }, | ||
| "evaluation_data": { | ||
| "dataset": "[Stanford Natural Language Inference (SNLI)](https://nlp.stanford.edu/projects/snli/) dev set", | ||
| "motivation": null, | ||
| "preprocessing": null | ||
| }, | ||
| "training_data": { | ||
| "dataset": "[Stanford Natural Language Inference (SNLI)](https://nlp.stanford.edu/projects/snli/) train set", | ||
| "motivation": null, | ||
| "preprocessing": null | ||
| }, | ||
| "quantitative_analyses": { | ||
| "unitary_results": null, | ||
| "intersectional_results": null | ||
| }, | ||
| "ethical_considerations": { | ||
| "ethical_considerations": null | ||
| }, | ||
| "caveats_and_recommendations": { | ||
| "caveats_and_recommendations": null | ||
| } | ||
| } | ||
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