New model, new model cards

model_cards/twmkn9/albert-base-v2-squad2/README.md

@@ -4,7 +4,9 @@ This model is ALBERT base v2 trained on SQuAD v2 as:
 python run_squad.py 
 --model_type albert 
 --model_name_or_path albert-base-v2 
---do_train --do_eval 
+--do_train 
+--do_eval 
+--overwrite_cache 
 --do_lower_case 
 --version_2_with_negative 
 --train_file $SQUAD_DIR/train-v2.0.json 

model_cards/twmkn9/bert-base-uncased-squad2/README.md

+This model is BERT base uncased trained on SQuAD v2 as:
+
+```
+python run_squad.py 
+--model_type bert 
+--model_name_or_path bert-base-uncased
+--do_train 
+--do_eval 
+--overwrite_cache 
+--do_lower_case 
+--version_2_with_negative 
+--train_file $SQUAD_DIR/train-v2.0.json 
+--predict_file $SQUAD_DIR/dev-v2.0.json 
+--per_gpu_train_batch_size 8 
+--num_train_epochs 3 
+--learning_rate 3e-5 
+--max_seq_length 384 
+--doc_stride 128 
+--output_dir ./tmp/bert_base_fine/
+```
+
+Performance on a dev subset is close to the original paper:
+
+```
+Results: 
+{
+    'exact': 72.35932872655479, 
+    'f1': 75.75355132564763, 
+    'total': 6078, 
+    'HasAns_exact': 74.29553264604812, 
+    'HasAns_f1': 81.38490892002987, 
+    'HasAns_total': 2910, 
+    'NoAns_exact': 70.58080808080808, 
+    'NoAns_f1': 70.58080808080808, 
+    'NoAns_total': 3168, 
+    'best_exact': 72.35932872655479, 
+    'best_exact_thresh': 0.0, 
+    'best_f1': 75.75355132564766, 
+    'best_f1_thresh': 0.0
+}
+```
+
+We are hopeful this might save you time, energy, and compute. Cheers!
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