
xtremedistil-l12-h384 trained on SQuAD 2.0

"eval_exact": 75.45691906005221  
"eval_f1": 79.32502968532793
{"tags": ["question-answering"], "datasets": ["squad_v2"], "metrics": ["f1", "exact"], "widget": [{"context": "While deep and large pre-trained models are the state-of-the-art for various natural language processing tasks, their huge size poses significant challenges for practical uses in resource constrained settings. Recent works in knowledge distillation propose task-agnostic as well as task-specific methods to compress these models, with task-specific ones often yielding higher compression rate. In this work, we develop a new task-agnostic distillation framework XtremeDistilTransformers that leverages the advantage of task-specific methods for learning a small universal model that can be applied to arbitrary tasks and languages. To this end, we study the transferability of several source tasks, augmentation resources and model architecture for distillation. We evaluate our model performance on multiple tasks, including the General Language Understanding Evaluation (GLUE) benchmark, SQuAD question answering dataset and a massive multi-lingual NER dataset with 41 languages.", "example_title": "xtremedistil q1", "text": "What is XtremeDistil?"}, {"context": "While deep and large pre-trained models are the state-of-the-art for various natural language processing tasks, their huge size poses significant challenges for practical uses in resource constrained settings. Recent works in knowledge distillation propose task-agnostic as well as task-specific methods to compress these models, with task-specific ones often yielding higher compression rate. In this work, we develop a new task-agnostic distillation framework XtremeDistilTransformers that leverages the advantage of task-specific methods for learning a small universal model that can be applied to arbitrary tasks and languages. To this end, we study the transferability of several source tasks, augmentation resources and model architecture for distillation. We evaluate our model performance on multiple tasks, including the General Language Understanding Evaluation (GLUE) benchmark, SQuAD question answering dataset and a massive multi-lingual NER dataset with 41 languages.", "example_title": "xtremedistil q2", "text": "On what is the model validated?"}], "model-index": [{"name": "nbroad/xdistil-l12-h384-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 75.4591, "name": "Exact Match", "verified": true}, {"type": "f1", "value": 79.3321, "name": "F1", "verified": true}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 81.8604, "name": "Exact Match", "verified": true}, {"type": "f1", "value": 89.6654, "name": "F1", "verified": true}]}]}]}
transformers pytorch tf jax tensorboard bert question-answering dataset:squad_v2 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #jax #tensorboard #bert #question-answering #dataset-squad_v2 #model-index #endpoints_compatible #has_space #region-us 


xtremedistil-l12-h384 trained on SQuAD 2.0

"eval_exact": 75.45691906005221  
"eval_f1": 79.32502968532793
TAGS
#transformers #pytorch #tf #jax #tensorboard #bert #question-answering #dataset-squad_v2 #model-index #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #tf #jax #tensorboard #bert #question-answering #dataset-squad_v2 #model-index #endpoints_compatible #has_space #region-us 
