Web1 day ago · Contrast sets provide a local view of a model’s decision boundary, which can be used to more accurately evaluate a model’s true linguistic capabilities. We … WebApr 6, 2024 · An illustration of how contrast sets provide a more comprehensive model evaluation when datasets have systematic gaps. Figures - available via license: …
Handling Missing Annotations in Supervised Learning Data
WebJan 1, 2024 · While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only instantiate limited types of perturbations such as paraphrases or word substitutions. We present Polyjuice, a general-purpose counterfactual generator … WebApr 6, 2024 · Evaluating NLP Models via Contrast Sets. Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's ... freezer anchorage
Evaluating NLP Models via Contrast Sets
WebHuggingface released its newest library called NLP, which gives you easy access to almost any NLP dataset and metric in one convenient interface. We will combine this with a BERT model from Huggingface's Transformers library to build a sentiment classifier for IMDB. OUTLINE: 0:00 - Intro; 1:30 - Boilerplate; 3:20 - PyTorch Lightning Module WebCurrent NLP models are often "cheating" on supervised learning tasks by exploiting correlations that arise from the particularities of the dataset … Press J to jump to the … WebEvaluating nlp models via contrast sets. M Gardner, Y Artzi, V Basmova, J Berant, B Bogin, S Chen, P Dasigi, ... EMNLP Findings 2024, 2024. 301 * 2024: Train large, then compress: Rethinking model size for efficient training and inference of transformers. freezer amps chart