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mrm8488/bert-multi-cased-finedtuned-xquad-tydiqa-goldp mrm8488/bert-multi-cased-finedtuned-xquad-tydiqa-goldp
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Contributed by

mrm8488 Manuel Romero
96 models

How to use this model directly from the 🤗/transformers library:

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from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mrm8488/bert-multi-cased-finedtuned-xquad-tydiqa-goldp") model = AutoModelForQuestionAnswering.from_pretrained("mrm8488/bert-multi-cased-finedtuned-xquad-tydiqa-goldp")

A fine-tuned model on GoldP task from Tydi QA dataset

This model uses bert-multi-cased-finetuned-xquadv1 and fine-tuned on Tydi QA dataset for Gold Passage task (GoldP)

Details of the language model

The base language model (bert-multi-cased-finetuned-xquadv1) is a fine-tuned version of bert-base-multilingual-cased for the Q&A downstream task

Details of the Tydi QA dataset

TyDi QA contains 200k human-annotated question-answer pairs in 11 Typologically Diverse languages, written without seeing the answer and without the use of translation, and is designed for the training and evaluation of automatic question answering systems. This repository provides evaluation code and a baseline system for the dataset.

Details of the downstream task (Gold Passage or GoldP aka the secondary task)

Given a passage that is guaranteed to contain the answer, predict the single contiguous span of characters that answers the question. the gold passage task differs from the primary task in several ways:

  • only the gold answer passage is provided rather than the entire Wikipedia article;
  • unanswerable questions have been discarded, similar to MLQA and XQuAD;
  • we evaluate with the SQuAD 1.1 metrics like XQuAD; and
  • Thai and Japanese are removed since the lack of whitespace breaks some tools.

Model training

The model was fine-tuned on a Tesla P100 GPU and 25GB of RAM. The script is the following:

python \
  --model_type bert \
  --model_name_or_path mrm8488/bert-multi-cased-finetuned-xquadv1 \
  --do_train \
  --do_eval \
  --train_file /content/dataset/train.json \
  --predict_file /content/dataset/dev.json \
  --per_gpu_train_batch_size 24 \
  --per_gpu_eval_batch_size 24 \
  --learning_rate 3e-5 \
  --num_train_epochs 2.5 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir /content/model_output \
  --overwrite_output_dir \
  --save_steps 5000 \
  --threads 40

Global Results (dev set):

Metric # Value
Exact 71.06
F1 82.16

Specific Results (per language):

Language # Samples # Exact # F1
Arabic 1314 73.29 84.72
Bengali 180 64.60 77.84
English 654 72.12 82.24
Finnish 1031 70.14 80.36
Indonesian 773 77.25 86.36
Korean 414 68.92 70.95
Russian 1079 62.65 78.55
Swahili 596 80.11 86.18
Telegu 874 71.00 84.24

Created by Manuel Romero/@mrm8488

Made with in Spain