diff --git "a/train_shortcut_token.txt" "b/train_shortcut_token.txt"
new file mode 100644--- /dev/null
+++ "b/train_shortcut_token.txt"
@@ -0,0 +1,140 @@
+Found cached dataset squad_v2 (/ukp-storage-1/imhof/.cache/huggingface/datasets/squad_v2/squad_v2/2.0.0/09187c73c1b837c95d9a249cd97c2c3f1cebada06efe667b4427714b27639b1d)
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+Found cached dataset squad (/ukp-storage-1/imhof/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)
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+Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForQuestionAnswering: ['lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.bias']
+- This IS expected if you are initializing RobertaForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
+- This IS NOT expected if you are initializing RobertaForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
+Some weights of RobertaForQuestionAnswering were not initialized from the model checkpoint at roberta-base and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+Loading cached split indices for dataset at /ukp-storage-1/imhof/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453/cache-bace650cd89bf3e6.arrow and /ukp-storage-1/imhof/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453/cache-64dbe49c4d157f27.arrow
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+Loading cached shuffled indices for dataset at /ukp-storage-1/imhof/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453/cache-096bb50a673d81bc.arrow
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+
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+Using cuda_amp half precision backend
+/storage/ukp/work/imhof/miniconda3/envs/thesis/lib/python3.10/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
+  warnings.warn(
+***** Running training *****
+  Num examples = 87745
+  Num Epochs = 3
+  Instantaneous batch size per device = 8
+  Total train batch size (w. parallel, distributed & accumulation) = 8
+  Gradient Accumulation steps = 1
+  Total optimization steps = 32907
+  Number of trainable parameters = 124056578
+
  0%|          | 0/32907 [00:00<?, ?it/s]You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+
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  6%|▌         | 1860/32907 [14:07<3:55:02,  2.20it/s]
  6%|▌         | 1861/32907 [14:07<3:54:56,  2.20it/s]
  6%|▌         | 1862/32907 [14:08<3:54:58,  2.20it/s]
  6%|▌         | 1863/32907 [14:08<3:54:57,  2.20it/s]
  6%|▌         | 1864/32907 [14:09<3:55:02,  2.20it/s]
  6%|▌         | 1865/32907 [14:09<3:54:57,  2.20it/s]
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  6%|▌         | 1867/32907 [14:10<3:55:05,  2.20it/s]
  6%|▌         | 1868/32907 [14:10<3:55:15,  2.20it/s]
  6%|▌         | 1869/32907 [14:11<3:55:09,  2.20it/s]
  6%|▌         | 1870/32907 [14:11<3:55:17,  2.20it/s]
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  6%|▌         | 1876/32907 [14:14<3:54:52,  2.20it/s]
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  6%|▌         | 1880/32907 [14:16<3:54:50,  2.20it/s]
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  6%|▌         | 1884/32907 [14:18<3:54:59,  2.20it/s]
  6%|▌         | 1885/32907 [14:18<3:55:12,  2.20it/s]
  6%|▌         | 1886/32907 [14:19<3:55:11,  2.20it/s]
  6%|▌         | 1887/32907 [14:19<3:55:02,  2.20it/s]
  6%|▌         | 1888/32907 [14:20<3:55:10,  2.20it/s]
  6%|▌         | 1889/32907 [14:20<3:55:11,  2.20it/s]
  6%|▌         | 1890/32907 [14:20<3:54:59,  2.20it/s]
  6%|▌         | 1891/32907 [14:21<3:54:49,  2.20it/s]
  6%|▌         | 1892/32907 [14:21<3:54:48,  2.20it/s]
  6%|▌         | 1893/32907 [14:22<3:55:52,  2.19it/s]
  6%|▌         | 1894/32907 [14:22<3:56:39,  2.18it/s]
  6%|▌         | 1895/32907 [14:23<3:56:43,  2.18it/s]
  6%|▌         | 1896/32907 [14:23<3:56:22,  2.19it/s]
  6%|▌         | 1897/32907 [14:24<3:56:02,  2.19it/s]
  6%|▌         | 1898/32907 [14:24<3:55:43,  2.19it/s]
  6%|▌         | 1899/32907 [14:25<3:55:29,  2.19it/s]
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  6%|▌         | 1902/32907 [14:26<3:55:04,  2.20it/s]
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  6%|▌         | 1908/32907 [14:29<3:58:09,  2.17it/s]
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  6%|▌         | 1910/32907 [14:30<3:56:44,  2.18it/s]
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  6%|▌         | 1912/32907 [14:30<3:55:43,  2.19it/s]
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  6%|▌         | 1915/32907 [14:32<3:55:09,  2.20it/s]
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  6%|▌         | 1921/32907 [14:35<3:54:48,  2.20it/s]
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  6%|▌         | 1926/32907 [14:37<3:55:06,  2.20it/s]
  6%|▌         | 1927/32907 [14:37<3:54:56,  2.20it/s]
  6%|▌         | 1928/32907 [14:38<3:55:24,  2.19it/s]
  6%|▌         | 1929/32907 [14:38<3:55:09,  2.20it/s]
  6%|▌         | 1930/32907 [14:39<3:56:38,  2.18it/s]
  6%|▌         | 1931/32907 [14:39<3:55:55,  2.19it/s]
  6%|▌         | 1932/32907 [14:40<3:55:32,  2.19it/s]
  6%|▌         | 1933/32907 [14:40<3:55:24,  2.19it/s]
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  6%|▌         | 1943/32907 [14:45<3:54:57,  2.20it/s]
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  6%|▌         | 1948/32907 [14:47<3:54:32,  2.20it/s]
  6%|▌         | 1949/32907 [14:47<3:54:38,  2.20it/s]
  6%|▌         | 1950/32907 [14:48<3:54:35,  2.20it/s]
  6%|▌         | 1951/32907 [14:48<3:54:42,  2.20it/s]
  6%|▌         | 1952/32907 [14:49<3:54:28,  2.20it/s]
  6%|▌         | 1953/32907 [14:49<3:54:29,  2.20it/s]
  6%|▌         | 1954/32907 [14:50<3:54:41,  2.20it/s]
  6%|▌         | 1955/32907 [14:50<3:54:39,  2.20it/s]
  6%|▌         | 1956/32907 [14:50<3:54:34,  2.20it/s]
  6%|▌         | 1957/32907 [14:51<3:54:27,  2.20it/s]
  6%|▌         | 1958/32907 [14:51<3:54:27,  2.20it/s]
  6%|▌         | 1959/32907 [14:52<3:57:10,  2.17it/s]
  6%|▌         | 1960/32907 [14:52<3:58:20,  2.16it/s]
  6%|▌         | 1961/32907 [14:53<3:57:54,  2.17it/s]
  6%|▌         | 1962/32907 [14:53<3:57:10,  2.17it/s]
  6%|▌         | 1963/32907 [14:54<3:56:26,  2.18it/s]
  6%|▌         | 1964/32907 [14:54<3:56:12,  2.18it/s]
  6%|▌         | 1965/32907 [14:55<3:55:47,  2.19it/s]
  6%|▌         | 1966/32907 [14:55<3:55:22,  2.19it/s]
  6%|▌         | 1967/32907 [14:56<3:55:18,  2.19it/s]
  6%|▌         | 1968/32907 [14:56<3:55:39,  2.19it/s]
  6%|▌         | 1969/32907 [14:56<3:55:23,  2.19it/s]
  6%|▌         | 1970/32907 [14:57<3:54:56,  2.19it/s]
  6%|▌         | 1971/32907 [14:57<3:54:40,  2.20it/s]
  6%|▌         | 1972/32907 [14:58<3:54:50,  2.20it/s]
  6%|▌         | 1973/32907 [14:58<3:54:32,  2.20it/s]
  6%|▌         | 1974/32907 [14:59<3:54:38,  2.20it/s]
  6%|▌         | 1975/32907 [14:59<3:54:47,  2.20it/s]
  6%|▌         | 1976/32907 [15:00<3:54:35,  2.20it/s]
  6%|▌         | 1977/32907 [15:00<3:54:24,  2.20it/s]
  6%|▌         | 1978/32907 [15:01<3:54:29,  2.20it/s]
  6%|▌         | 1979/32907 [15:01<3:54:14,  2.20it/s]
  6%|▌         | 1980/32907 [15:01<3:54:22,  2.20it/s]
  6%|▌         | 1981/32907 [15:02<3:54:18,  2.20it/s]
  6%|▌         | 1982/32907 [15:02<3:54:11,  2.20it/s]
  6%|▌         | 1983/32907 [15:03<3:54:09,  2.20it/s]
  6%|▌         | 1984/32907 [15:03<3:54:21,  2.20it/s]
  6%|▌         | 1985/32907 [15:04<3:54:25,  2.20it/s]
  6%|▌         | 1986/32907 [15:04<3:54:16,  2.20it/s]
  6%|▌         | 1987/32907 [15:05<3:54:32,  2.20it/s]
  6%|▌         | 1988/32907 [15:05<3:54:32,  2.20it/s]
  6%|▌         | 1989/32907 [15:06<3:54:29,  2.20it/s]
  6%|▌         | 1990/32907 [15:06<3:54:18,  2.20it/s]
  6%|▌         | 1991/32907 [15:06<3:54:22,  2.20it/s]
  6%|▌         | 1992/32907 [15:07<3:54:14,  2.20it/s]
  6%|▌         | 1993/32907 [15:07<3:54:16,  2.20it/s]
  6%|▌         | 1994/32907 [15:08<3:54:24,  2.20it/s]
  6%|▌         | 1995/32907 [15:08<3:54:29,  2.20it/s]
  6%|▌         | 1996/32907 [15:09<3:57:55,  2.17it/s]
  6%|▌         | 1997/32907 [15:09<3:56:51,  2.18it/s]
  6%|▌         | 1998/32907 [15:10<3:56:02,  2.18it/s]
  6%|▌         | 1999/32907 [15:10<3:55:22,  2.19it/s]
  6%|▌         | 2000/32907 [15:11<3:55:03,  2.19it/s]
                                                      

  6%|▌         | 2000/32907 [15:11<3:55:03,  2.19it/s]
  6%|▌         | 2001/32907 [15:11<3:57:48,  2.17it/s]
  6%|▌         | 2002/32907 [15:11<3:56:48,  2.18it/s]
  6%|▌         | 2003/32907 [15:12<3:55:55,  2.18it/s]
  6%|▌         | 2004/32907 [15:12<3:55:43,  2.19it/s]
  6%|▌         | 2005/32907 [15:13<3:55:21,  2.19it/s]
  6%|▌         | 2006/32907 [15:13<3:55:04,  2.19it/s]
  6%|▌         | 2007/32907 [15:14<3:54:44,  2.19it/s]
  6%|▌         | 2008/32907 [15:14<3:54:44,  2.19it/s]
  6%|▌         | 2009/32907 [15:15<3:54:29,  2.20it/s]
  6%|▌         | 2010/32907 [15:15<3:54:33,  2.20it/s]
  6%|▌         | 2011/32907 [15:16<3:54:46,  2.19it/s]
  6%|▌         | 2012/32907 [15:16<3:54:28,  2.20it/s]
  6%|▌         | 2013/32907 [15:16<3:54:17,  2.20it/s]
  6%|▌         | 2014/32907 [15:17<3:54:09,  2.20it/s]
  6%|▌         | 2015/32907 [15:17<3:54:08,  2.20it/s]
  6%|▌         | 2016/32907 [15:18<3:54:00,  2.20it/s]
  6%|▌         | 2017/32907 [15:18<3:54:00,  2.20it/s]
  6%|▌         | 2018/32907 [15:19<3:54:36,  2.19it/s]
  6%|▌         | 2019/32907 [15:19<3:54:32,  2.19it/s]
  6%|▌         | 2020/32907 [15:20<3:54:20,  2.20it/s]
  6%|▌         | 2021/32907 [15:20<3:54:08,  2.20it/s]
  6%|▌         | 2022/32907 [15:21<3:54:08,  2.20it/s]
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  6%|▌         | 2030/32907 [15:24<3:56:09,  2.18it/s]
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+Configuration saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/checkpoint-10969/config.json
+Model weights saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/checkpoint-10969/pytorch_model.bin
+tokenizer config file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/checkpoint-10969/tokenizer_config.json
+Special tokens file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/checkpoint-10969/special_tokens_map.json
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+Configuration saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/checkpoint-21938/config.json
+Model weights saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/checkpoint-21938/pytorch_model.bin
+tokenizer config file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/checkpoint-21938/tokenizer_config.json
+Special tokens file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/checkpoint-21938/special_tokens_map.json
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100%|██████████| 32907/32907 [4:10:05<00:00,  2.85it/s]Saving model checkpoint to /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/checkpoint-32907
+Configuration saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/checkpoint-32907/config.json
+Model weights saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/checkpoint-32907/pytorch_model.bin
+tokenizer config file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/checkpoint-32907/tokenizer_config.json
+Special tokens file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/checkpoint-32907/special_tokens_map.json
+
+
+Training completed. Do not forget to share your model on huggingface.co/models =)
+
+
+
                                                       

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+The following columns in the test set don't have a corresponding argument in `RobertaForQuestionAnswering.forward` and have been ignored: offset_mapping, example_id. If offset_mapping, example_id are not expected by `RobertaForQuestionAnswering.forward`,  you can safely ignore this message.
+***** Running Prediction *****
+  Num examples = 10619
+  Batch size = 8
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+{'train_runtime': 15010.9305, 'train_samples_per_second': 17.536, 'train_steps_per_second': 2.192, 'train_loss': 0.7912965875911304, 'epoch': 3.0}
+
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+The following columns in the test set don't have a corresponding argument in `RobertaForQuestionAnswering.forward` and have been ignored: offset_mapping, example_id. If offset_mapping, example_id are not expected by `RobertaForQuestionAnswering.forward`,  you can safely ignore this message.
+***** Running Prediction *****
+  Num examples = 10619
+  Batch size = 8
+{'text': 'weight, jet thrust, bicycle', 'logit_score': 14.66}
+Results with the shortcut evaluation set: {'exact_match': 28.637653736991485, 'f1': 74.70141448647325}
+
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+Saving model checkpoint to /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start
+Configuration saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/config.json
+Model weights saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/pytorch_model.bin
+tokenizer config file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/tokenizer_config.json
+Special tokens file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad-shortcut-token-before-answer-start/special_tokens_map.json
+{'text': 'kip', 'logit_score': 12.06}
+Results with the normal evaluation set: {'exact_match': 84.94796594134343, 'f1': 91.56003393447934}