From 1b9e765b2113d834fb3fbc48710b66565232a02e Mon Sep 17 00:00:00 2001 From: Manuel Romero Date: Tue, 10 Mar 2020 22:51:49 +0100 Subject: [PATCH] Update README.md - Remove metrics until tested on other xquad benchmarks --- .../bert-multi-cased-finetuned-xquadv1/README.md | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/model_cards/mrm8488/bert-multi-cased-finetuned-xquadv1/README.md b/model_cards/mrm8488/bert-multi-cased-finetuned-xquadv1/README.md index 82756e8c95..e88e5dea8f 100644 --- a/model_cards/mrm8488/bert-multi-cased-finetuned-xquadv1/README.md +++ b/model_cards/mrm8488/bert-multi-cased-finetuned-xquadv1/README.md @@ -5,7 +5,7 @@ thumbnail: # BERT (base-multilingual-cased) fine-tuned on XQuAD -This model was created by [Google](https://github.com/google-research/bert/blob/master/multilingual.md) and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) for multilingual (`11 different languages`) **Q&A** downstream task. +This model was created by [Google](https://github.com/google-research/bert/blob/master/multilingual.md) and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) like data for multilingual (`11 different languages`) **Q&A** downstream task. ## Details of the language model('bert-base-multilingual-cased') @@ -77,14 +77,6 @@ As **XQuAD** is just an evaluation dataset, I used `Data augmentation techniques The model was trained on a Tesla P100 GPU and 25GB of RAM. The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/distillation/run_squad_w_distillation.py) -## Results: - -| Metric | # Value | -| --------- | ----------- | -| **Exact** | **91.43** | -| **F1** | **94.14** | - - ## Model in action