diff --git "a/train_roberta.txt" "b/train_roberta.txt"
new file mode 100644--- /dev/null
+++ "b/train_roberta.txt"
@@ -0,0 +1,122 @@
+Found cached dataset squad (/ukp-storage-1/imhof/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)
+
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+Loading cached processed dataset at /ukp-storage-1/imhof/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453/cache-5c72e9294ccc761d.arrow
+
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+Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForQuestionAnswering: ['lm_head.layer_norm.bias', 'lm_head.bias', 'lm_head.layer_norm.weight', 'lm_head.decoder.weight', 'lm_head.dense.weight', '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.bias', 'qa_outputs.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+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
+
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  6%|▌         | 1912/32907 [14:35<3:58:44,  2.16it/s]
  6%|▌         | 1913/32907 [14:36<3:57:51,  2.17it/s]
  6%|▌         | 1914/32907 [14:36<3:57:23,  2.18it/s]
  6%|▌         | 1915/32907 [14:37<3:56:50,  2.18it/s]
  6%|▌         | 1916/32907 [14:37<3:56:27,  2.18it/s]
  6%|▌         | 1917/32907 [14:38<3:56:15,  2.19it/s]
  6%|▌         | 1918/32907 [14:38<3:56:10,  2.19it/s]
  6%|▌         | 1919/32907 [14:39<3:56:06,  2.19it/s]
  6%|▌         | 1920/32907 [14:39<3:56:04,  2.19it/s]
  6%|▌         | 1921/32907 [14:39<3:55:57,  2.19it/s]
  6%|▌         | 1922/32907 [14:40<3:55:56,  2.19it/s]
  6%|▌         | 1923/32907 [14:40<3:55:52,  2.19it/s]
  6%|▌         | 1924/32907 [14:41<3:55:45,  2.19it/s]
  6%|▌         | 1925/32907 [14:41<3:55:40,  2.19it/s]
  6%|▌         | 1926/32907 [14:42<3:55:51,  2.19it/s]
  6%|▌         | 1927/32907 [14:42<3:56:04,  2.19it/s]
  6%|▌         | 1928/32907 [14:43<3:55:58,  2.19it/s]
  6%|▌         | 1929/32907 [14:43<3:55:54,  2.19it/s]
  6%|▌         | 1930/32907 [14:44<3:55:50,  2.19it/s]
  6%|▌         | 1931/32907 [14:44<3:55:56,  2.19it/s]
  6%|▌         | 1932/32907 [14:44<3:55:52,  2.19it/s]
  6%|▌         | 1933/32907 [14:45<3:55:48,  2.19it/s]
  6%|▌         | 1934/32907 [14:45<3:55:40,  2.19it/s]
  6%|▌         | 1935/32907 [14:46<3:55:41,  2.19it/s]
  6%|▌         | 1936/32907 [14:46<3:55:43,  2.19it/s]
  6%|▌         | 1937/32907 [14:47<3:55:42,  2.19it/s]
  6%|▌         | 1938/32907 [14:47<3:55:46,  2.19it/s]
  6%|▌         | 1939/32907 [14:48<3:55:43,  2.19it/s]
  6%|▌         | 1940/32907 [14:48<3:55:34,  2.19it/s]
  6%|▌         | 1941/32907 [14:49<3:55:30,  2.19it/s]
  6%|▌         | 1942/32907 [14:49<3:55:31,  2.19it/s]
  6%|▌         | 1943/32907 [14:50<3:55:36,  2.19it/s]
  6%|▌         | 1944/32907 [14:50<3:55:36,  2.19it/s]
  6%|▌         | 1945/32907 [14:50<3:55:39,  2.19it/s]
  6%|▌         | 1946/32907 [14:51<3:55:35,  2.19it/s]
  6%|▌         | 1947/32907 [14:51<3:55:35,  2.19it/s]
  6%|▌         | 1948/32907 [14:52<3:55:27,  2.19it/s]
  6%|▌         | 1949/32907 [14:52<3:55:27,  2.19it/s]
  6%|▌         | 1950/32907 [14:53<3:55:23,  2.19it/s]
  6%|▌         | 1951/32907 [14:53<3:55:25,  2.19it/s]
  6%|▌         | 1952/32907 [14:54<3:55:28,  2.19it/s]
  6%|▌         | 1953/32907 [14:54<3:55:32,  2.19it/s]
  6%|▌         | 1954/32907 [14:55<3:55:31,  2.19it/s]
  6%|▌         | 1955/32907 [14:55<3:55:32,  2.19it/s]
  6%|▌         | 1956/32907 [14:55<3:55:30,  2.19it/s]
  6%|▌         | 1957/32907 [14:56<3:55:28,  2.19it/s]
  6%|▌         | 1958/32907 [14:56<3:55:27,  2.19it/s]
  6%|▌         | 1959/32907 [14:57<3:55:25,  2.19it/s]
  6%|▌         | 1960/32907 [14:57<3:55:24,  2.19it/s]
  6%|▌         | 1961/32907 [14:58<3:55:23,  2.19it/s]
  6%|▌         | 1962/32907 [14:58<3:55:31,  2.19it/s]
  6%|▌         | 1963/32907 [14:59<3:55:29,  2.19it/s]
  6%|▌         | 1964/32907 [14:59<3:55:28,  2.19it/s]
  6%|▌         | 1965/32907 [15:00<3:55:26,  2.19it/s]
  6%|▌         | 1966/32907 [15:00<3:55:25,  2.19it/s]
  6%|▌         | 1967/32907 [15:00<3:55:28,  2.19it/s]
  6%|▌         | 1968/32907 [15:01<3:55:31,  2.19it/s]
  6%|▌         | 1969/32907 [15:01<3:55:28,  2.19it/s]
  6%|▌         | 1970/32907 [15:02<3:55:23,  2.19it/s]
  6%|▌         | 1971/32907 [15:02<3:55:17,  2.19it/s]
  6%|▌         | 1972/32907 [15:03<3:55:14,  2.19it/s]
  6%|▌         | 1973/32907 [15:03<3:55:19,  2.19it/s]
  6%|▌         | 1974/32907 [15:04<3:55:16,  2.19it/s]
  6%|▌         | 1975/32907 [15:04<3:55:17,  2.19it/s]
  6%|▌         | 1976/32907 [15:05<3:55:22,  2.19it/s]
  6%|▌         | 1977/32907 [15:05<3:55:27,  2.19it/s]
  6%|▌         | 1978/32907 [15:05<3:55:21,  2.19it/s]
  6%|▌         | 1979/32907 [15:06<3:55:18,  2.19it/s]
  6%|▌         | 1980/32907 [15:06<3:55:19,  2.19it/s]
  6%|▌         | 1981/32907 [15:07<3:55:17,  2.19it/s]
  6%|▌         | 1982/32907 [15:07<3:55:14,  2.19it/s]
  6%|▌         | 1983/32907 [15:08<3:55:16,  2.19it/s]
  6%|▌         | 1984/32907 [15:08<3:55:24,  2.19it/s]
  6%|▌         | 1985/32907 [15:09<3:55:30,  2.19it/s]
  6%|▌         | 1986/32907 [15:09<3:55:30,  2.19it/s]
  6%|▌         | 1987/32907 [15:10<3:55:30,  2.19it/s]
  6%|▌         | 1988/32907 [15:10<3:55:27,  2.19it/s]
  6%|▌         | 1989/32907 [15:11<3:55:19,  2.19it/s]
  6%|▌         | 1990/32907 [15:11<3:55:16,  2.19it/s]
  6%|▌         | 1991/32907 [15:11<3:55:11,  2.19it/s]
  6%|▌         | 1992/32907 [15:12<3:55:14,  2.19it/s]
  6%|▌         | 1993/32907 [15:12<3:55:21,  2.19it/s]
  6%|▌         | 1994/32907 [15:13<3:55:22,  2.19it/s]
  6%|▌         | 1995/32907 [15:13<3:55:19,  2.19it/s]
  6%|▌         | 1996/32907 [15:14<3:55:13,  2.19it/s]
  6%|▌         | 1997/32907 [15:14<3:55:12,  2.19it/s]
  6%|▌         | 1998/32907 [15:15<3:55:08,  2.19it/s]
  6%|▌         | 1999/32907 [15:15<3:55:07,  2.19it/s]
  6%|▌         | 2000/32907 [15:16<3:55:07,  2.19it/s]
                                                      

  6%|▌         | 2000/32907 [15:16<3:55:07,  2.19it/s]
  6%|▌         | 2001/32907 [15:16<3:58:37,  2.16it/s]
  6%|▌         | 2002/32907 [15:16<3:57:32,  2.17it/s]
  6%|▌         | 2003/32907 [15:17<3:56:47,  2.18it/s]
  6%|▌         | 2004/32907 [15:17<3:56:31,  2.18it/s]
  6%|▌         | 2005/32907 [15:18<3:56:12,  2.18it/s]
  6%|▌         | 2006/32907 [15:18<3:55:51,  2.18it/s]
  6%|▌         | 2007/32907 [15:19<3:56:09,  2.18it/s]
  6%|▌         | 2008/32907 [15:19<3:55:51,  2.18it/s]
  6%|▌         | 2009/32907 [15:20<3:55:39,  2.19it/s]
  6%|▌         | 2010/32907 [15:20<3:55:30,  2.19it/s]
  6%|▌         | 2011/32907 [15:21<3:55:24,  2.19it/s]
  6%|▌         | 2012/32907 [15:21<3:55:14,  2.19it/s]
  6%|▌         | 2013/32907 [15:22<3:55:13,  2.19it/s]
  6%|▌         | 2014/32907 [15:22<3:55:08,  2.19it/s]
  6%|▌         | 2015/32907 [15:22<3:55:07,  2.19it/s]
  6%|▌         | 2016/32907 [15:23<3:55:06,  2.19it/s]
  6%|▌         | 2017/32907 [15:23<3:55:00,  2.19it/s]
  6%|▌         | 2018/32907 [15:24<3:55:01,  2.19it/s]
  6%|▌         | 2019/32907 [15:24<3:55:02,  2.19it/s]
  6%|▌         | 2020/32907 [15:25<3:54:59,  2.19it/s]
  6%|▌         | 2021/32907 [15:25<3:55:02,  2.19it/s]
  6%|▌         | 2022/32907 [15:26<3:55:02,  2.19it/s]
  6%|▌         | 2023/32907 [15:26<3:55:05,  2.19it/s]
  6%|▌         | 2024/32907 [15:27<3:55:12,  2.19it/s]
  6%|▌         | 2025/32907 [15:27<3:55:10,  2.19it/s]
  6%|▌         | 2026/32907 [15:27<3:55:01,  2.19it/s]
  6%|▌         | 2027/32907 [15:28<3:55:01,  2.19it/s]
  6%|▌         | 2028/32907 [15:28<3:55:00,  2.19it/s]
  6%|▌         | 2029/32907 [15:29<3:55:02,  2.19it/s]
  6%|▌         | 2030/32907 [15:29<3:55:01,  2.19it/s]
  6%|▌         | 2031/32907 [15:30<3:54:57,  2.19it/s]
  6%|▌         | 2032/32907 [15:30<3:54:59,  2.19it/s]
  6%|▌         | 2033/32907 [15:31<3:54:53,  2.19it/s]
  6%|▌         | 2034/32907 [15:31<3:54:50,  2.19it/s]
  6%|▌         | 2035/32907 [15:32<3:54:52,  2.19it/s]
  6%|▌         | 2036/32907 [15:32<3:54:51,  2.19it/s]
  6%|▌         | 2037/32907 [15:32<3:54:54,  2.19it/s]
  6%|▌         | 2038/32907 [15:33<3:54:50,  2.19it/s]
  6%|▌         | 2039/32907 [15:33<3:54:53,  2.19it/s]
  6%|▌         | 2040/32907 [15:34<3:54:49,  2.19it/s]
  6%|▌         | 2041/32907 [15:34<3:54:47,  2.19it/s]
  6%|▌         | 2042/32907 [15:35<3:54:51,  2.19it/s]
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  6%|▌         | 2049/32907 [15:38<3:54:59,  2.19it/s]
  6%|▌         | 2050/32907 [15:38<3:54:56,  2.19it/s]
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  6%|▋         | 2057/32907 [15:42<3:57:05,  2.17it/s]
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  6%|▋         | 2059/32907 [15:43<3:56:27,  2.17it/s]
  6%|▋         | 2060/32907 [15:43<3:56:00,  2.18it/s]
  6%|▋         | 2061/32907 [15:43<3:55:37,  2.18it/s]
  6%|▋         | 2062/32907 [15:44<3:55:19,  2.18it/s]
  6%|▋         | 2063/32907 [15:44<3:55:11,  2.19it/s]
  6%|▋         | 2064/32907 [15:45<3:55:07,  2.19it/s]
  6%|▋         | 2065/32907 [15:45<3:55:00,  2.19it/s]
  6%|▋         | 2066/32907 [15:46<3:54:55,  2.19it/s]
  6%|▋         | 2067/32907 [15:46<3:54:50,  2.19it/s]
  6%|▋         | 2068/32907 [15:47<3:54:47,  2.19it/s]
  6%|▋         | 2069/32907 [15:47<3:54:45,  2.19it/s]
  6%|▋         | 2070/32907 [15:48<3:54:40,  2.19it/s]
  6%|▋         | 2071/32907 [15:48<3:54:39,  2.19it/s]
  6%|▋         | 2072/32907 [15:48<3:54:36,  2.19it/s]
  6%|▋         | 2073/32907 [15:49<3:54:35,  2.19it/s]
  6%|▋         | 2074/32907 [15:49<3:54:41,  2.19it/s]
  6%|▋         | 2075/32907 [15:50<3:54:43,  2.19it/s]
  6%|▋         | 2076/32907 [15:50<3:54:40,  2.19it/s]
  6%|▋         | 2077/32907 [15:51<3:54:39,  2.19it/s]
  6%|▋         | 2078/32907 [15:51<3:54:33,  2.19it/s]
  6%|▋         | 2079/32907 [15:52<3:54:34,  2.19it/s]
  6%|▋         | 2080/32907 [15:52<3:54:33,  2.19it/s]
  6%|▋         | 2081/32907 [15:53<3:54:39,  2.19it/s]
  6%|▋         | 2082/32907 [15:53<3:54:35,  2.19it/s]
  6%|▋         | 2083/32907 [15:53<3:54:32,  2.19it/s]
  6%|▋         | 2084/32907 [15:54<3:54:30,  2.19it/s]
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  6%|▋         | 2087/32907 [15:55<3:54:27,  2.19it/s]
  6%|▋         | 2088/32907 [15:56<3:55:03,  2.19it/s]
  6%|▋         | 2089/32907 [15:56<3:54:55,  2.19it/s]
  6%|▋         | 2090/32907 [15:57<3:54:45,  2.19it/s]
  6%|▋         | 2091/32907 [15:57<3:54:44,  2.19it/s]
  6%|▋         | 2092/32907 [15:58<3:54:38,  2.19it/s]
  6%|▋         | 2093/32907 [15:58<3:54:34,  2.19it/s]
  6%|▋         | 2094/32907 [15:59<3:54:32,  2.19it/s]
  6%|▋         | 2095/32907 [15:59<3:54:32,  2.19it/s]
  6%|▋         | 2096/32907 [15:59<3:54:41,  2.19it/s]
  6%|▋         | 2097/32907 [16:00<3:54:31,  2.19it/s]
  6%|▋         | 2098/32907 [16:00<3:54:31,  2.19it/s]
  6%|▋         | 2099/32907 [16:01<3:54:34,  2.19it/s]
  6%|▋         | 2100/32907 [16:01<3:54:32,  2.19it/s]
  6%|▋         | 2101/32907 [16:02<3:54:28,  2.19it/s]
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  6%|▋         | 2103/32907 [16:03<3:54:22,  2.19it/s]
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  6%|▋         | 2110/32907 [16:06<3:54:21,  2.19it/s]
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  7%|▋         | 2139/32907 [16:19<3:54:22,  2.19it/s]
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+Configuration saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/checkpoint-10969/config.json
+Model weights saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/checkpoint-10969/pytorch_model.bin
+tokenizer config file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/checkpoint-10969/tokenizer_config.json
+Special tokens file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/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/checkpoint-21938/config.json
+Model weights saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/checkpoint-21938/pytorch_model.bin
+tokenizer config file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/checkpoint-21938/tokenizer_config.json
+Special tokens file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/checkpoint-21938/special_tokens_map.json
+
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100%|██████████| 32907/32907 [4:11:01<00:00,  2.84it/s]Saving model checkpoint to /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/checkpoint-32907
+Configuration saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/checkpoint-32907/config.json
+Model weights saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/checkpoint-32907/pytorch_model.bin
+tokenizer config file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/checkpoint-32907/tokenizer_config.json
+Special tokens file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/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': 15064.7363, 'train_samples_per_second': 17.474, 'train_steps_per_second': 2.184, 'train_loss': 0.7512069995666943, 'epoch': 3.0}
+
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+Saving model checkpoint to /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad
+Configuration saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/config.json
+Model weights saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/pytorch_model.bin
+tokenizer config file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/tokenizer_config.json
+Special tokens file saved in /storage/ukp/work/imhof/Towards-Faithful-and-Explainable-NLP/Models/roberta-finetuned-squad/special_tokens_map.json
+{'text': 'kip', 'logit_score': 1.113}
+Results
+{'exact_match': 85.87511825922422, 'f1': 92.12966408818717}