Update README.md

README.md

@@ -5,4 +5,87 @@ language:
 - nl
 ---
 
-The used dataset raalst/squad_v2_dutch was kindly provided by Henryk 
+The used dataset raalst/squad_v2_dutch was kindly provided by Henryk 
+it contains train and validation. 
+I declared 20% of Train to function as Test
+when using raalst/squad_v2_dutch, be sure to clean up quotes and double quotes in the contexts
+
+def cleanup(mylist):
+    for item in mylist:
+        if '"' in item["context"]:
+            item["context"] = item["context"].replace('"','\\"')
+        if "'" in item["context"]:
+            item["context"] = item["context"].replace("'","\\'")             
+
+The pretrained model was pdelobelle/robbert-v2-dutch-base, a dutch RoBERTa model  
+
+results obtained in training are :
+
+{'exact': 61.75389109958193,
+ 'f1': 66.89717170237417,
+ 'total': 19853,
+ 'HasAns_exact': 48.967182330322814,
+ 'HasAns_f1': 58.09796564493008,
+ 'HasAns_total': 11183,
+ 'NoAns_exact': 78.24682814302192,
+ 'NoAns_f1': 78.24682814302192,
+ 'NoAns_total': 8670,
+ 'best_exact': 61.75389109958193,
+ 'best_exact_thresh': 0.0,
+ 'best_f1': 66.89717170237276,
+ 'best_f1_thresh': 0.0}
+
+settings (until I figured out how to report them properly):
+
+DatasetDict({
+    train: Dataset({
+        features: ['id', 'title', 'context', 'question', 'answers'],
+        num_rows: 79412
+    })
+    test: Dataset({
+        features: ['id', 'title', 'context', 'question', 'answers'],
+        num_rows: 19853
+    })
+    validation: Dataset({
+        features: ['id', 'title', 'context', 'question', 'answers'],
+        num_rows: 9669
+    })
+})
+
+tokenizer = AutoTokenizer.from_pretrained("pdelobelle/robbert-v2-dutch-base")
+
+from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer
+
+model = AutoModelForQuestionAnswering.from_pretrained("pdelobelle/robbert-v2-dutch-base")
+training_args = TrainingArguments(
+    output_dir="./qa_model",
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    push_to_hub=False,
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_squad["train"],
+    eval_dataset=tokenized_squad["validation"],
+    tokenizer=tokenizer,
+    data_collator=data_collator,
+)
+
+trainer.train()
+[15198/15198 2:57:03, Epoch 3/3]
+Epoch 	Training Loss 	Validation Loss
+1 	1.380700 	1.177431
+2 	1.093000 	1.052601
+3 	0.849700 	1.143632
+
+TrainOutput(global_step=15198, training_loss=1.1917077029499668, metrics={'train_runtime': 10623.9565, 
+'train_samples_per_second': 22.886, 'train_steps_per_second': 1.431, 'total_flos': 4.764955396486349e+16, 
+'train_loss': 1.1917077029499668, 'epoch': 3.0})
+
+Trained on Ubuntu with 1080Ti