From 601e424750e89bd7dd24a3ac0c7fd6a2b34ad4ba Mon Sep 17 00:00:00 2001 From: Manuel Romero Date: Tue, 10 Mar 2020 22:53:32 +0100 Subject: [PATCH] Update README.md --- .../README.md | 17 ++--------------- 1 file changed, 2 insertions(+), 15 deletions(-) diff --git a/model_cards/mrm8488/bert-multi-uncased-finetuned-xquadv1/README.md b/model_cards/mrm8488/bert-multi-uncased-finetuned-xquadv1/README.md index 79acde75b9..39368ef365 100644 --- a/model_cards/mrm8488/bert-multi-uncased-finetuned-xquadv1/README.md +++ b/model_cards/mrm8488/bert-multi-uncased-finetuned-xquadv1/README.md @@ -3,9 +3,9 @@ language: multilingual thumbnail: --- -# BERT (base-multilingual-uncased) fine-tuned on XQuAD +# BERT (base-multilingual-uncased) fine-tuned for multilingual Q&A -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-uncased') @@ -77,19 +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** | **93.03** | -| **F1** | **94.62** | - -## Comparison: - -| Model | Exact | F1 score | -| --------- | ----------- | ------- | -| [bert-multi-cased-finetuned-xquadv1](https://huggingface.co/mrm8488/bert-multi-cased-finetuned-xquadv1) | 91.43 | 94.14 | -|bert-multi-uncased-finetuned-xquadv1 | **93.03** | **94.62** ## Model in action