updated dutch squad model card (#3736)

* added model_cards for polish squad models

* corrected mistake in polish design cards

* updated model_cards for squad2_dutch model

* added links to benchmark models
Co-authored-by: Henryk Borzymowski <henryk.borzymowski@pwc.com>

model_cards/henryk/bert-base-multilingual-cased-finetuned-dutch-squad2/README.md

@@ -6,14 +6,14 @@ language: dutch
 
 This model is the multilingual model provided by the Google research team with a fine-tuned dutch Q&A downstream task.
 
-## Details of the language model(bert-base-multilingual-cased)
+## Details of the language model
 
 Language model ([**bert-base-multilingual-cased**](https://github.com/google-research/bert/blob/master/multilingual.md)):
 12-layer, 768-hidden, 12-heads, 110M parameters.
 Trained on cased text in the top 104 languages with the largest Wikipedias.
 
-## Details of the downstream task - Dataset
-Using the `mtranslate` Python module, [**SQuAD2.0**](https://rajpurkar.github.io/SQuAD-explorer/) was machine-translated. In order to find the start tokens the direct translations of the answers were searched in the corresponding paragraphs. Since the answer could not always be found in the text, due to the different translations depending on the context (missing context in the pure answer), a loss of question-answer examples occurred. This is a potential problem where errors can occur in the data set (but in the end it was a quick and dirty solution that worked well enough for my task).
+## Details of the downstream task
+Using the `mtranslate` Python module, [**SQuAD2.0**](https://rajpurkar.github.io/SQuAD-explorer/) was machine-translated. In order to find the start tokens, the direct translations of the answers were searched in the corresponding paragraphs. Due to the different translations depending on the context (missing context in the pure answer), the answer could not always be found in the text, and thus a loss of question-answer examples occurred. This is a potential problem where errors can occur in the data set.
 
 | Dataset                | # Q&A |
 | ---------------------- | ----- |
@@ -22,29 +22,73 @@ Using the `mtranslate` Python module, [**SQuAD2.0**](https://rajpurkar.github.io
 | SQuAD2.0 Dev           | 12  K |
 | Dutch SQuAD2.0 Dev     | 10  K |
 
+
+## Model benchmark
+
+
+| Model                | EM/F1 |HasAns (EM/F1) | NoAns |
+| ---------------------- | ----- | ----- | ----- |
+| [robBERT](https://huggingface.co/pdelobelle/robBERT-base)   | 58.04/60.95  | 33.08/40.64 | 73.67 |
+| [dutchBERT](https://huggingface.co/wietsedv/bert-base-dutch-cased)   | 64.25/68.45 | 45.59/56.49  | 75.94 |
+| [multiBERT](https://huggingface.co/bert-base-multilingual-cased) | **67.38**/**71.36**  | 47.42/57.76 | 79.88 |
+
 ## Model training
 
-The model was trained on a Tesla V100 GPU with the following command:
+The model was trained on a **Tesla V100** GPU with the following command:
 
 ```python
 export SQUAD_DIR=path/to/nl_squad
 
-python run_squad.py \
+python run_squad.py 
   --model_type bert \
   --model_name_or_path bert-base-multilingual-cased \
-  --version_2_with_negative \
   --do_train \
   --do_eval \
-  --train_file $SQUAD_DIR/train_nl-v2.0.json \
-  --predict_file $SQUAD_DIR/dev_nl-v2.0.json \
-  --per_gpu_train_batch_size 12 \
-  --learning_rate 3e-5 \
-  --num_train_epochs 2.0 \
+  --train_file $SQUAD_DIR/nl_squadv2_train_clean.json \
+  --predict_file $SQUAD_DIR/nl_squadv2_dev_clean.json \
+  --num_train_epochs 2 \
   --max_seq_length 384 \
   --doc_stride 128 \
-  --output_dir /tmp/output_dir/
+  --save_steps=8000 \
+  --output_dir ../../output \
+  --overwrite_cache \
+  --overwrite_output_dir
 ```
 
 **Results**:
 
-{'exact': **67.38**, 'f1': **71.36**} 
+{'exact': 67.38028751680629, 'f1': 71.362297054268, 'total': 9669, 'HasAns_exact': 47.422126745435015, 'HasAns_f1': 57.761023151910734, 'HasAns_total': 3724, 'NoAns_exact': 79.88225399495374, 'NoAns_f1': 79.88225399495374, 'NoAns_total': 5945, 'best_exact': 67.53542248422795, 'best_exact_thresh': 0.0, 'best_f1': 71.36229705426837, 'best_f1_thresh': 0.0}
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+
+## Model in action
+
+Fast usage with **pipelines**:
+
+```python
+from transformers import pipeline
+
+qa_pipeline = pipeline(
+    "question-answering",
+    model="henryk/bert-base-multilingual-cased-finetuned-dutch-squad2",
+    tokenizer="henryk/bert-base-multilingual-cased-finetuned-dutch-squad2"
+)
+
+qa_pipeline({
+    'context': "Amsterdam is de hoofdstad en de dichtstbevolkte stad van Nederland.",
+    'question': "Wat is de hoofdstad van Nederland?"})
+
+```
+
+# Output:
+
+```json
+{
+  "score": 0.83,
+  "start": 0, 
+  "end": 9,
+  "answer": "Amsterdam"
+}
+```
+
+## Contact
+
+Please do not hesitate to contact me via [LinkedIn](https://www.linkedin.com/in/henryk-borzymowski-0755a2167/) if you want to discuss or get access to the Dutch version of SQuAD.
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