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Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/mrm8488/distilbert-multi-finetuned-for-xqua-on-tydiqa/README.md

README.md

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+---
+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/question-answering/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 <span style="color: #e25555;">&hearts;</span> in Spain