From c1a6252be173b417e46823d58d51d757344d88ee Mon Sep 17 00:00:00 2001 From: Manuel Romero Date: Wed, 1 Apr 2020 13:14:23 +0200 Subject: [PATCH] Create model card (#3557) Create model card for: distilbert-multi-finetuned-for-xqua-on-tydiqa --- .../README.md | 82 +++++++++++++++++++ 1 file changed, 82 insertions(+) create mode 100644 model_cards/mrm8488/distilbert-multi-finetuned-for-xqua-on-tydiqa/README.md diff --git a/model_cards/mrm8488/distilbert-multi-finetuned-for-xqua-on-tydiqa/README.md b/model_cards/mrm8488/distilbert-multi-finetuned-for-xqua-on-tydiqa/README.md new file mode 100644 index 0000000000..67e0477170 --- /dev/null +++ b/model_cards/mrm8488/distilbert-multi-finetuned-for-xqua-on-tydiqa/README.md @@ -0,0 +1,82 @@ +--- +language: multilingual +thumbnail: +--- + +# DistilBERT multilingual fine-tuned on TydiQA (GoldP task) dataset for multilingual Q&A 😛🌍❓ + + +## Details of the language model + +[distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) + + +## 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. https://ai.google.com/research/tydiqa + + +## 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](https://github.com/google-research-datasets/tydiqa/blob/master/README.md#the-tasks) 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 +python transformers/examples/run_squad.py \ + --model_type distilbert \ + --model_name_or_path distilbert-base-multilingual-cased \ + --do_train \ + --do_eval \ + --train_file /path/to/dataset/train.json \ + --predict_file /path/to/dataset/dev.json \ + --per_gpu_train_batch_size 24 \ + --per_gpu_eval_batch_size 24 \ + --learning_rate 3e-5 \ + --num_train_epochs 5 \ + --max_seq_length 384 \ + --doc_stride 128 \ + --output_dir /content/model_output \ + --overwrite_output_dir \ + --save_steps 1000 \ + --threads 400 + ``` + +## Global Results (dev set) 📝 + +| Metric | # Value | +| --------- | ----------- | +| **EM** | **63.85** | +| **F1** | **75.70** | + +## Specific Results (per language) 🌍📝 + +| Language | # Samples | # EM | # F1 | +| --------- | ----------- |--------| ------ | +| Arabic | 1314 | 66.66 | 80.02 | +| Bengali | 180 | 53.09 | 63.50 | +| English | 654 | 62.42 | 73.12 | +| Finnish | 1031 | 64.57 | 75.15 | +| Indonesian| 773 | 67.89 | 79.70 | +| Korean | 414 | 51.29 | 61.73 | +| Russian | 1079 | 55.42 | 70.08 | +| Swahili | 596 | 74.51 | 81.15 | +| Telegu | 874 | 66.21 | 79.85 | + + +## Similar models + +You can also try [bert-multi-cased-finedtuned-xquad-tydiqa-goldp](https://huggingface.co/mrm8488/bert-multi-cased-finedtuned-xquad-tydiqa-goldp) that achieves **F1 = 82.16** and **EM = 71.06** (And of course better marks per language). + + +> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) + +> Made with ♥ in Spain