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Transformers-Github-Issues

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MDU6SXNzdWU0MTI4MDc5OTc=

Tests error: Issue with python3 compatibility, on zope interface implementation

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Hi, I came across the following error after run **python -m pytest tests/modeling_test.py** ________________________________________________________________________________ ERROR collecting tests/modeling_test.py __________________________________________________________________________________ modeling_test.py:25: in <module> from pytorch_pretrained_bert import (BertConfig, BertModel, BertForMaskedLM, /usr/local/lib/python3.6/site-packages/pytorch_pretrained_bert/__init__.py:7: in <module> from .modeling import (BertConfig, BertModel, BertForPreTraining, /usr/local/lib/python3.6/site-packages/pytorch_pretrained_bert/modeling.py:218: in <module> from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm /usr/local/lib/python3.6/site-packages/apex/__init__.py:18: in <module> from apex.interfaces import (ApexImplementation, /usr/local/lib/python3.6/site-packages/apex/interfaces.py:10: in <module> class ApexImplementation(object): /usr/local/lib/python3.6/site-packages/apex/interfaces.py:14: in ApexImplementation implements(IApex) /usr/local/lib/python3.6/site-packages/zope/interface/declarations.py:483: in implements raise TypeError(_ADVICE_ERROR % 'implementer') E TypeError: Class advice impossible in Python3. Use the @implementer class decorator instead. **My configurations are as follows: python version 3.6.4 CUDA Version 8.0.61 torch==1.0.1.post2 apex==0.9.10.dev0 zope.interface==4.6.0** Thanks

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MDU6SXNzdWU0MTI3NDI0MzU=

It seems the eval speed of transformer-xl is not faster than bert-base-uncased.

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I run `run_classifier.py` with `bert-base-uncased` and `max_seq_length=128` on the MRPC task. The log: ``` Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex. 02/21/2019 12:11:44 - INFO - __main__ - device: cpu n_gpu: 1, distributed training: False, 16-bits training: False 02/21/2019 12:11:45 - INFO - pytorch_pretrained_bert.tokenization - loading vocabulary file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt from cache at /home/tong.guo/.pytorch_pretrained_bert/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084 02/21/2019 12:11:45 - INFO - pytorch_pretrained_bert.modeling - loading archive file ../model_file/bert-base-uncased.tar.gz 02/21/2019 12:11:45 - INFO - pytorch_pretrained_bert.modeling - extracting archive file ../model_file/bert-base-uncased.tar.gz to temp dir /tmp/tmpaho9_3dk 02/21/2019 12:11:50 - INFO - pytorch_pretrained_bert.modeling - Model config { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 2, "vocab_size": 30522 } 02/21/2019 12:11:55 - INFO - pytorch_pretrained_bert.modeling - Weights of BertForSequenceClassification not initialized from pretrained model: ['classifier.weight', 'classifier.bias'] 02/21/2019 12:11:55 - INFO - pytorch_pretrained_bert.modeling - Weights from pretrained model not used in BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias'] 02/21/2019 12:11:55 - INFO - pytorch_pretrained_bert.modeling - loading archive file ../model_file/bert-base-uncased.tar.gz 02/21/2019 12:11:55 - INFO - pytorch_pretrained_bert.modeling - extracting archive file ../model_file/bert-base-uncased.tar.gz to temp dir /tmp/tmpfehb71wu 02/21/2019 12:11:59 - INFO - pytorch_pretrained_bert.modeling - Model config { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 2, "vocab_size": 30522 } 02/21/2019 12:12:03 - INFO - pytorch_pretrained_bert.modeling - Weights of BertForSequenceClassification not initialized from pretrained model: ['classifier.weight', 'classifier.bias'] 02/21/2019 12:12:03 - INFO - pytorch_pretrained_bert.modeling - Weights from pretrained model not used in BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias'] 02/21/2019 12:12:03 - INFO - __main__ - *** Example *** 02/21/2019 12:12:03 - INFO - __main__ - guid: dev-1 02/21/2019 12:12:03 - INFO - __main__ - tokens: [CLS] [UNK] ' s chief operating officer , [UNK] [UNK] , and [UNK] [UNK] , the chief financial officer , will report directly to [UNK] [UNK] . [SEP] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] and [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] will report to [UNK] . [SEP] 02/21/2019 12:12:03 - INFO - __main__ - input_ids: 101 100 1005 1055 2708 4082 2961 1010 100 100 1010 1998 100 100 1010 1996 2708 3361 2961 1010 2097 3189 3495 2000 100 100 1012 102 100 100 100 100 100 100 1998 100 100 100 100 100 100 2097 3189 2000 100 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - label: 1 (id = 1) 02/21/2019 12:12:03 - INFO - __main__ - *** Example *** 02/21/2019 12:12:03 - INFO - __main__ - guid: dev-2 02/21/2019 12:12:03 - INFO - __main__ - tokens: [CLS] [UNK] world ' s two largest auto ##makers said their [UNK] . [UNK] . sales declined more than predicted last month as a late summer sales frenzy caused more of an industry backlash than expected . [SEP] [UNK] sales at both [UNK] and [UNK] . 2 [UNK] [UNK] [UNK] . declined more than predicted as a late summer sales frenzy prompted a larger - than - expected industry backlash . [SEP] 02/21/2019 12:12:03 - INFO - __main__ - input_ids: 101 100 2088 1005 1055 2048 2922 8285 12088 2056 2037 100 1012 100 1012 4341 6430 2062 2084 10173 2197 3204 2004 1037 2397 2621 4341 21517 3303 2062 1997 2019 3068 25748 2084 3517 1012 102 100 4341 2012 2119 100 1998 100 1012 1016 100 100 100 1012 6430 2062 2084 10173 2004 1037 2397 2621 4341 21517 9469 1037 3469 1011 2084 1011 3517 3068 25748 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - label: 1 (id = 1) 02/21/2019 12:12:03 - INFO - __main__ - *** Example *** 02/21/2019 12:12:03 - INFO - __main__ - guid: dev-3 02/21/2019 12:12:03 - INFO - __main__ - tokens: [CLS] [UNK] to the federal [UNK] for [UNK] [UNK] and [UNK] ( news - web sites ) , there were 19 reported cases of me ##as ##les in the [UNK] [UNK] in 2002 . [SEP] [UNK] [UNK] for [UNK] [UNK] and [UNK] said there were 19 reported cases of me ##as ##les in the [UNK] [UNK] in 2002 . [SEP] 02/21/2019 12:12:03 - INFO - __main__ - input_ids: 101 100 2000 1996 2976 100 2005 100 100 1998 100 1006 2739 1011 4773 4573 1007 1010 2045 2020 2539 2988 3572 1997 2033 3022 4244 1999 1996 100 100 1999 2526 1012 102 100 100 2005 100 100 1998 100 2056 2045 2020 2539 2988 3572 1997 2033 3022 4244 1999 1996 100 100 1999 2526 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - label: 1 (id = 1) 02/21/2019 12:12:03 - INFO - __main__ - *** Example *** 02/21/2019 12:12:03 - INFO - __main__ - guid: dev-4 02/21/2019 12:12:03 - INFO - __main__ - tokens: [CLS] [UNK] tropical storm rapidly developed in the [UNK] of [UNK] [UNK] and was expected to hit somewhere along the [UNK] or [UNK] coasts by [UNK] night . [SEP] [UNK] tropical storm rapidly developed in the [UNK] of [UNK] on [UNK] and could have hurricane - force winds when it hits land somewhere along the [UNK] coast [UNK] night . [SEP] 02/21/2019 12:12:03 - INFO - __main__ - input_ids: 101 100 5133 4040 5901 2764 1999 1996 100 1997 100 100 1998 2001 3517 2000 2718 4873 2247 1996 100 2030 100 20266 2011 100 2305 1012 102 100 5133 4040 5901 2764 1999 1996 100 1997 100 2006 100 1998 2071 2031 7064 1011 2486 7266 2043 2009 4978 2455 4873 2247 1996 100 3023 100 2305 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - label: 0 (id = 0) 02/21/2019 12:12:03 - INFO - __main__ - *** Example *** 02/21/2019 12:12:03 - INFO - __main__ - guid: dev-5 02/21/2019 12:12:03 - INFO - __main__ - tokens: [CLS] [UNK] company didn ' t detail the costs of the replacement and repairs . [SEP] [UNK] company officials expect the costs of the replacement work to run into the millions of dollars . [SEP] 02/21/2019 12:12:03 - INFO - __main__ - input_ids: 101 100 2194 2134 1005 1056 6987 1996 5366 1997 1996 6110 1998 10315 1012 102 100 2194 4584 5987 1996 5366 1997 1996 6110 2147 2000 2448 2046 1996 8817 1997 6363 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02/21/2019 12:12:03 - INFO - __main__ - label: 0 (id = 0) 02/21/2019 12:12:04 - INFO - __main__ - ***** Running evaluation ***** 02/21/2019 12:12:04 - INFO - __main__ - Num examples = 1725 02/21/2019 12:12:04 - INFO - __main__ - Batch size = 8 Evaluating: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 216/216 [06:06<00:00, 1.70s/it] 02/21/2019 12:18:11 - INFO - __main__ - ***** Eval results ***** 02/21/2019 12:18:11 - INFO - __main__ - eval_accuracy = 0.33507246376811595 02/21/2019 12:18:11 - INFO - __main__ - eval_loss = 1.002936492777533 02/21/2019 12:18:11 - INFO - __main__ - global_step = 0 02/21/2019 12:18:11 - INFO - __main__ - loss = Non ``` The speed is about 1.7 s/batch ------------------ I run `run_transfo_xl.py` on the `wikitext-103` task. The log: ``` Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex. 02/20/2019 19:49:30 - INFO - __main__ - device: cuda 02/20/2019 19:49:30 - INFO - pytorch_pretrained_bert.tokenization_transfo_xl - loading vocabulary file ../model_file/transfo-xl-wt103-vocab.bin 02/20/2019 19:49:30 - INFO - pytorch_pretrained_bert.tokenization_transfo_xl - loading corpus file ../model_file/transfo-xl-wt103-corpus.bin 02/20/2019 19:49:36 - INFO - pytorch_pretrained_bert.modeling_transfo_xl - loading weights file ../model_file/transfo-xl-wt103-pytorch_model.bin 02/20/2019 19:49:36 - INFO - pytorch_pretrained_bert.modeling_transfo_xl - loading configuration file ../model_file/transfo-xl-wt103-config.json 02/20/2019 19:49:36 - INFO - pytorch_pretrained_bert.modeling_transfo_xl - Model config { "adaptive": true, "attn_type": 0, "clamp_len": 1000, "cutoffs": [ 20000, 40000, 200000 ], "d_embed": 1024, "d_head": 64, "d_inner": 4096, "d_model": 1024, "div_val": 4, "dropatt": 0.0, "dropout": 0.1, "ext_len": 0, "init": "normal", "init_range": 0.01, "init_std": 0.02, "mem_len": 1600, "n_head": 16, "n_layer": 18, "n_token": 267735, "pre_lnorm": false, "proj_init_std": 0.01, "same_length": true, "sample_softmax": -1, "tgt_len": 128, "tie_projs": [ false, true, true, true ], "tie_weight": true, "untie_r": true } 02/20/2019 19:49:51 - INFO - __main__ - Evaluating with bsz 10 tgt_len 128 ext_len 0 mem_len 1600 clamp_len 1000 02/20/2019 19:57:35 - INFO - __main__ - Time : 464.00s, 2416.66ms/segment 02/20/2019 19:57:35 - INFO - __main__ - ==================================================================================================== 02/20/2019 19:57:35 - INFO - __main__ - | test loss 2.90 | test ppl 18.213 02/20/2019 19:57:35 - INFO - __main__ - ==================================================================================================== ``` The speed is about 2.4 s/batch

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MDExOlB1bGxSZXF1ZXN0MjU0ODczOTY0

Update README.md

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MDU6SXNzdWU0MTI3MjAzNTg=

Issue happens while using convert_tf_checkpoint_to_pytorch

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Hi, We are using your brilliant project for working on the Japanese BERT model with Sentence Piece. https://github.com/yoheikikuta/bert-japanese We are trying to use the convert to to convert below TF BERT model to PyTorch. https://drive.google.com/drive/folders/1Zsm9DD40lrUVu6iAnIuTH2ODIkh-WM-O But we see error logs: Traceback (most recent call last): File "/Users/weicheng.zhu/PycharmProjects/pytorch-pretrained-BERT-master/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py", line 66, in <module> args.pytorch_dump_path) File "/Users/weicheng.zhu/PycharmProjects/pytorch-pretrained-BERT-master/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py", line 37, in convert_tf_checkpoint_to_pytorch load_tf_weights_in_bert(model, tf_checkpoint_path) File "/Users/weicheng.zhu/PycharmProjects/pytorch-pretrained-BERT-master/pytorch_pretrained_bert/modeling.py", line 95, in load_tf_weights_in_bert pointer = getattr(pointer, l[0]) File "/usr/local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 535, in __getattr__ type(self).__name__, name)) AttributeError: 'BertForPreTraining' object has no attribute 'global_step' Could you kindly help with how we can avoid this? Thank you so much!

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MDExOlB1bGxSZXF1ZXN0MjU0NzU1NTQw

Update run_openai_gpt.py

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Adding invocation to the top of `run_openai_gpt.py` so that's it's easy to find. Previously, the header said that running the script w/ default values works, but actually you need to set some paths.

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MDU6SXNzdWU0MTI1NjUxMzk=

Can I do a code reference in implementing my code?

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@thomwolf I am trying simply implementing gpt-2 on Pytorch I have trouble in trasfering tensorflow checkpoint to pytorch :( https://github.com/graykode/gpt-2-Pytorch Could I do this code reference in implementing my code? I'll write reference in my code!! Thanks for awesome sharing!

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MDU6SXNzdWU0MTI0Njg5NTM=

Example Code in README fails.

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There is an assertion error in the example code in the README. The text that is input to the model `"[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"` is expected to be tokenized and masked like so `['[CLS]', 'who', 'was', 'jim', 'henson', '?', '[SEP]', 'jim', '[MASK]', 'was', 'a', 'puppet', '##eer', '[SEP]']`. However the `[CLS]` and `[SEP]` tokens are split up and in the class of `[CLS]` it is broken into word pieces. The actual tokenized results looks like this `['[', 'cl', '##s', ']', 'who', 'was', 'jim', 'henson', '[MASK]', '[', 'sep', ']', 'jim', 'henson', 'was', 'a', 'puppet', '##eer', '[', 'sep', ']']`

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MDExOlB1bGxSZXF1ZXN0MjU0NjMxMTQw

typo

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typo in annotation

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MDU6SXNzdWU0MTIyMjIxNTA=

`train_dataset` and `eval_dataset` in run_openai_gpt.py

closed

CONTRIBUTOR

Running `examples/run_openai_gpt.py` w/ the default arguments throws an error: ``` $ python run_openai_gpt.py --output_dir tmp --do_eval Traceback (most recent call last): File "run_openai_gpt.py", line 259, in <module> main() File "run_openai_gpt.py", line 153, in main train_dataset = load_rocstories_dataset(args.train_dataset) File "run_openai_gpt.py", line 49, in load_rocstories_dataset with open(dataset_path, encoding='utf_8') as f: FileNotFoundError: [Errno 2] No such file or directory: '' ``` Looking at the code, it looks like `train_dataset` and `eval_dataset` need to be explicitly set. Any suggestions on what they should be set to? Thanks! cc @thomwolf

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MDU6SXNzdWU0MTIyMjA0Njg=

bert.pooler.dense initialization

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CONTRIBUTOR

Hey! Sorry if this is redundant. I saw 3 other issues asking similar questions but couldn't find these exact layers mentioned. Are bert.pooler.dense.weight & bert.pooler.dense.bias randomly initialized? Thank you so much!

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MDU6SXNzdWU0MTIxOTc4NTk=

Tests failure

closed

NONE

Steps to reproduce: 1. Clone the repo. 2. Set up a plain virtual environment `venv` for the repo with Python 3.6. 3. Run `pip install .` (using the `[--editable]` didn't work and there was some error, so I just removed it) 4. Run `pip install spacy ftfy==4.4.3` and `python -m spacy download en` -- SUCCESSFUL. 5. Run `pip install pytest` -- SUCCESSFUL. 6. Run `python -m pytest -sv tests/` -- FAILURE with error below. ``` tests/modeling_gpt2_test.py::GPT2ModelTest::test_config_to_json_string PASSED tests/modeling_gpt2_test.py::GPT2ModelTest::test_default dyld: lazy symbol binding failed: Symbol not found: _PySlice_Unpack Referenced from: /Users/XYZ/PycharmProjects/pytorch-pretrained-BERT/venv/lib/python3.6/site-packages/torch/lib/libtorch_python.dylib Expected in: flat namespace dyld: Symbol not found: _PySlice_Unpack Referenced from: /Users/XYZ/PycharmProjects/pytorch-pretrained-BERT/venv/lib/python3.6/site-packages/torch/lib/libtorch_python.dylib Expected in: flat namespace Abort trap: 6 ``` In case it helps, these are the packages in my `venv` after I perform the first 5 steps. ``` atomicwrites | 1.3.0 | 1.3.0 attrs | 18.2.0 | 18.2.0 boto3 | 1.9.98 | 1.9.98 botocore | 1.12.98 | 1.12.98 certifi | 2018.11.29 | 2018.11.29 chardet | 3.0.4 | 3.0.4 cymem | 2.0.2 | 2.0.2 cytoolz | 0.9.0.1 | 0.9.0.1 dill | 0.2.9 | 0.2.9 docutils | 0.14 | 0.14 en-core-web-sm | 2.0.0 |   ftfy | 4.4.3 | 5.5.1 html5lib | 1.0.1 | 1.0.1 idna | 2.8 | 2.8 jmespath | 0.9.3 | 0.9.3 more-itertools | 6.0.0 | 6.0.0 msgpack | 0.5.6 | 0.6.1 msgpack-numpy | 0.4.3.2 | 0.4.4.2 murmurhash | 1.0.2 | 1.0.2 numpy | 1.16.1 | 1.16.1 pip | 19.0.2 | 19.0.2 plac | 0.9.6 | 1.0.0 pluggy | 0.8.1 | 0.8.1 preshed | 2.0.1 | 2.0.1 py | 1.7.0 | 1.7.0 pytest | 4.3.0 | 4.3.0 python-dateutil | 2.8.0 | 2.8.0 pytorch-pretrained-bert | 0.6.1 | 0.6.1 regex | 2018.1.10 | 2019.02.20 requests | 2.21.0 | 2.21.0 s3transfer | 0.2.0 | 0.2.0 setuptools | 39.1.0 | 40.8.0 six | 1.12.0 | 1.12.0 spacy | 2.0.18 | 2.0.18 thinc | 6.12.1 | 7.0.1 toolz | 0.9.0 | 0.9.0 torch | 1.0.1.post2 | 1.0.1.post2 tqdm | 4.31.1 | 4.31.1 ujson | 1.35 | 1.35 urllib3 | 1.24.1 | 1.24.1 wcwidth | 0.1.7 | 0.1.7 webencodings | 0.5.1 | 0.5.1 wrapt | 1.10.11 | 1.11.1 ```

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MDU6SXNzdWU0MTIxNjE0NDA=

Transformer-XL: Convert lm1b model to PyTorch

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Hi, I wanted to convert the TensorFlow checkpoint for the ` lm1b` model to PyTorch with the `convert_transfo_xl_checkpoint_to_pytorch.py` script. I downloaded the checkpoint with the [download.sh](https://github.com/kimiyoung/transformer-xl/blob/master/tf/sota/download.sh) script. Then I called the convert script with: ```bash $ python3 convert_transfo_xl_checkpoint_to_pytorch.py --pytorch_dump_folder_path converted --tf_checkpoint_path /mnt/transformer-xl/tf/sota/pretrained_xl/tf_lm1b/model/checkpoint ``` Then the following error message is returned: ```bash 2019-02-19 22:46:54.693060: W tensorflow/core/util/tensor_slice_reader.cc:95] Could not open /mnt/transformer-xl/tf/sota/pretrained_xl/tf_lm1b/model/checkpoint: Data loss: not an sstable (bad magic number): perhaps your file is in a different file format and you need to use a different restore operator? Traceback (most recent call last): File "convert_transfo_xl_checkpoint_to_pytorch.py", line 116, in <module> args.transfo_xl_dataset_file) File "convert_transfo_xl_checkpoint_to_pytorch.py", line 81, in convert_transfo_xl_checkpoint_to_pytorch model = load_tf_weights_in_transfo_xl(model, config, tf_path) File "/usr/local/lib/python3.6/dist-packages/pytorch_pretrained_bert/modeling_transfo_xl.py", line 141, in load_tf_weights_in_transfo_xl init_vars = tf.train.list_variables(tf_path) File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/checkpoint_utils.py", line 95, in list_variables reader = load_checkpoint(ckpt_dir_or_file) File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/checkpoint_utils.py", line 64, in load_checkpoint return pywrap_tensorflow.NewCheckpointReader(filename) File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 382, in NewCheckpointReader return CheckpointReader(compat.as_bytes(filepattern), status) File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/errors_impl.py", line 548, in __exit__ c_api.TF_GetCode(self.status.status)) tensorflow.python.framework.errors_impl.DataLossError: Unable to open table file /mnt/transformer-xl/tf/sota/pretrained_xl/tf_lm1b/model/checkpoint: Data loss: not an sstable (bad magic number): perhaps your file is in a different file format and you need to use a different restore operator? ``` I'm using the *0.6.1* version of `pytorch-pretrained-BERT` and the latest `tf-nightly-gpu` package that ships TensorFlow 1.13dev.

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MDU6SXNzdWU0MTIxNDc4NjE=

Sudden catastrophic classification output during NER training

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CONTRIBUTOR

Hi, I am fine-tuning BERT model (based on `BertForTokenClassification`) to a NER task with 9 labels ("O" + BILU tags for 2 classes) and sometimes during training I run into this odd behavior: a network with 99% accuracy that is showing a converging trend suddenly shifts all of its predictions to a single class. This happens during the interval of a single epoch. Below are the confusion matrices and some other metrics one epoch before the event and after the event: ``` Epoch 7/10: 150.57s/it, val_acc=99.718% (53391/53542), val_acc_bilu=87.568% (162/185), val_rec=98.780%, val_prec=55.862%, val_f1=71.366% Confusion matrix: [[53229 2 66 25 2 25 8] [ 0 7 0 7 0 0 0] [ 0 0 14 0 0 0 0] [ 0 0 0 67 0 0 1] [ 1 0 0 3 11 0 1] [ 1 0 1 0 0 14 0] [ 0 0 0 7 1 0 49]] Epoch 8/10: 150.64s/it, val_acc=0.030% (16/53542), val_acc_bilu=8.649% (16/185), val_rec=100.000%, val_prec=0.030%, val_f1=0.060% Confusion matrix: [[ 0 0 0 0 53357 0 0] [ 0 0 0 0 14 0 0] [ 0 0 0 0 14 0 0] [ 0 0 0 0 68 0 0] [ 0 0 0 0 16 0 0] [ 0 0 0 0 16 0 0] [ 0 0 0 0 57 0 0]] ``` I am using the default configs for `bert-base-multilingual-cased` and standard `CrossEntropyLoss`. The optimizer is `BertAdam` untouched with learning rate 1e-5. The dataset is highly unbalanced (very few named entities, so >99% of the tokens are "O" tags), so I use a weight of 0.01 to the "O" tag in CE. Has anyone faced a similar issue? Thanks in advance

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MDU6SXNzdWU0MTIwNjMxMDI=

How to change config parameters when loading the model with `from_pretrained`

closed

CONTRIBUTOR

I have created a model by extending `PreTrainedBertModel`: ```python class BertForMultiLabelClassification(PreTrainedBertModel): def __init__(self, config, num_labels=2): super(BertForMultiLabelClassification, self).__init__(config) self.num_labels = num_labels self.bert = BertModel(config) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.classifier = nn.Linear(config.hidden_size, num_labels) self.apply(self.init_bert_weights) # some code here ... ``` I am creating an instance of this model: ```python model = BertForMultiLabelClassification.from_pretrained(args.bert_model, cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format( args.local_rank), num_labels=num_labels) ``` what is an effective way to modify parameters of the default config, when creating an instance of `BertForMultiLabelClassification`? (say, setting a different value for `config.hidden_dropout_prob`). Any thoughts on what is an effective way to do this?

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MDExOlB1bGxSZXF1ZXN0MjU0MjM2MDA0

fix broken link in readme

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MDU6SXNzdWU0MTE4NTU0NTk=

Extract Features for GPT2 and Transformer-XL

closed

NONE

Hi everyone, I'm interested in extracting token-level embeddings from the pre-trained GPT2 and Transformer-XL models and noticed that extract_features.py seems to be specific to BERT. Can you let us know if you have any plans to provide a similar implementation for models other than BERT? Alternatively, could you possibly provide some hints for us to extract the token-level embeddings the code you already made available with the models? Many thanks, great work!

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MDExOlB1bGxSZXF1ZXN0MjU0MDM0MTM1

Minor README typos corrected

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MDExOlB1bGxSZXF1ZXN0MjUzOTk1MTQw

Fix typo in `GPT2Model` code sample

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CONTRIBUTOR

Typo prevented code from running

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MDU6SXNzdWU0MTE1NTg4OTE=

Too much info @ stdout

closed

NONE

As a library, it is preferred to have no unnecessary `print`s in the repo. Using the `pytorch-pretrained-BERT` makes it impossible to use `stdout` as main output mechanism for my code. For example: it prints directly to `stdout` "Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex.". I guess printing this kind of messages to `stderr` will be a better idea.

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MDExOlB1bGxSZXF1ZXN0MjUzODgxMTA3

Typo/formatting fixes in README

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HugginFace or HuggingFace?

closed

CONTRIBUTOR

Thought to flag, also given the terrific work on this repo (and others), that the company name in the code here seems to be systematically spelt wrong (?) https://github.com/huggingface/pytorch-pretrained-BERT/search?q=hugginface&unscoped_q=hugginface

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MDExOlB1bGxSZXF1ZXN0MjUzODcwNTEz

forgot to add regex to requirements.txt :(

closed

MEMBER

Updating requirements to add `regex` for GPT-2 tokenizer. A test on OpenAI GPT-2 tokenizer module would have caught that. But (byte-level) BPE tokenization tests are such a pain to make properly. Let's add one in the next release, after the ACL deadline.

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MDExOlB1bGxSZXF1ZXN0MjUzODU1MzI5

Gpt2

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MEMBER

Adding GPT-2...

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MDExOlB1bGxSZXF1ZXN0MjUzNjY1NjMx

Update activation function docstring

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Anyone tried this model to write a next sentence?

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MDU6SXNzdWU0MTA3ODI1OTg=

Error in Apex's FusedLayerNorm

closed

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After installing `apex` with the cuda extensions and running BERT, I get the following error in `FusedLayerNormAffineFunction`, [apex/normalization/fused_layer_norm.py](https://github.com/NVIDIA/apex/blob/master/apex/normalization/fused_layer_norm.py#L16) (line 21). ``` RuntimeError: a Tensor with 2482176 elements cannot be converted to Scalar (item at /pytorch/aten/src/ATen/native/Scalar.cpp:9) ``` Here are the shapes of my tensors: ``` input_ - [32, 101, 768] (this is the embeddings tensor in BertEmbeddings) bias_ - [768] weight_ - [768] self.normalized_shape - [768] ``` I'm not sure if it's a problem with `pytorch-pretrained-BERT` or `apex`. Any idea? Full stacktrace below. ``` File "/home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/pytorch_pretrained_bert/modeling.py", line 710, in forward embedding_output = self.embeddings(input_ids, token_type_ids) File "/home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "/home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/pytorch_pretrained_bert/modeling.py", line 261, in forward embeddings = self.LayerNorm(embeddings) File "/home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "/home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/apex-0.1-py3.6-linux-x86_64.egg/apex/normalization/fused_layer_norm.py", line 149, in forward input, self.weight, self.bias) File "/home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/apex-0.1-py3.6-linux-x86_64.egg/apex/normalization/fused_layer_norm.py", line 21, in forward input_, self.normalized_shape, weight_, bias_, self.eps) RuntimeError: a Tensor with 2482176 elements cannot be converted to Scalar (item at /pytorch/aten/src/ATen/native/Scalar.cpp:9) frame #0: std::function<std::string ()>::operator()() const + 0x11 (0x7f1aa5da3021 in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/lib/libc10.so) frame #1: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x2a (0x7f1aa5da28ea in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/lib/libc10.so) frame #2: at::native::item(at::Tensor const&) + 0x12c3 (0x7f1aa690d5b3 in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/lib/libcaffe2.so) frame #3: at::TypeDefault::item(at::Tensor const&) const + 0x55 (0x7f1aa6b1c905 in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/lib/libcaffe2.so) frame #4: torch::autograd::VariableType::eye_out(at::Tensor&, long, long) const + 0x184 (0x7f1aa4faeec4 in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/lib/libtorch.so.1) frame #5: <unknown function> + 0x89ca (0x7f1a82e739ca in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/apex-0.1-py3.6-linux-x86_64.egg/fused_layer_norm_cuda.cpython-36m-x86_64-linux-gnu.so) frame #6: layer_norm_affine(at::Tensor, c10::ArrayRef<long>, at::Tensor, at::Tensor, double) + 0x185 (0x7f1a82e762a5 in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/apex-0.1-py3.6-linux-x86_64.egg/fused_layer_norm_cuda.cpython-36m-x86_64-linux-gnu.so) frame #7: <unknown function> + 0x18d44 (0x7f1a82e83d44 in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/apex-0.1-py3.6-linux-x86_64.egg/fused_layer_norm_cuda.cpython-36m-x86_64-linux-gnu.so) frame #8: <unknown function> + 0x16495 (0x7f1a82e81495 in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/apex-0.1-py3.6-linux-x86_64.egg/fused_layer_norm_cuda.cpython-36m-x86_64-linux-gnu.so) frame #9: _PyCFunction_FastCallDict + 0x154 (0x55a8f9925744 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #10: <unknown function> + 0x198610 (0x55a8f99ac610 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #11: _PyEval_EvalFrameDefault + 0x30a (0x55a8f99d138a in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #12: <unknown function> + 0x71e1 (0x7f1af51ee1e1 in /home/hyper/.PyCharm2018.1/system/cythonExtensions/_pydevd_frame_eval_ext/pydevd_frame_evaluator.cpython-36m-x86_64-linux-gnu.so) frame #13: _PyFunction_FastCallDict + 0x11b (0x55a8f99a6bab in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #14: _PyObject_FastCallDict + 0x26f (0x55a8f9925b0f in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #15: _PyObject_Call_Prepend + 0x63 (0x55a8f992a6a3 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #16: PyObject_Call + 0x3e (0x55a8f992554e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #17: THPFunction_do_forward(THPFunction*, _object*) + 0x15c (0x7f1ae02e21ec in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/lib/libtorch_python.so) frame #18: PyCFunction_Call + 0x5f (0x55a8f992863f in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #19: PyObject_Call + 0x3e (0x55a8f992554e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #20: <unknown function> + 0x16ba91 (0x55a8f997fa91 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #21: _PyObject_FastCallDict + 0x8b (0x55a8f992592b in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #22: <unknown function> + 0x19857e (0x55a8f99ac57e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #23: _PyEval_EvalFrameDefault + 0x30a (0x55a8f99d138a in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #24: <unknown function> + 0x71e1 (0x7f1af51ee1e1 in /home/hyper/.PyCharm2018.1/system/cythonExtensions/_pydevd_frame_eval_ext/pydevd_frame_evaluator.cpython-36m-x86_64-linux-gnu.so) frame #25: _PyFunction_FastCallDict + 0x11b (0x55a8f99a6bab in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #26: _PyObject_FastCallDict + 0x26f (0x55a8f9925b0f in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #27: _PyObject_Call_Prepend + 0x63 (0x55a8f992a6a3 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #28: PyObject_Call + 0x3e (0x55a8f992554e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #29: _PyEval_EvalFrameDefault + 0x19ec (0x55a8f99d2a6c in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #30: <unknown function> + 0x71e1 (0x7f1af51ee1e1 in /home/hyper/.PyCharm2018.1/system/cythonExtensions/_pydevd_frame_eval_ext/pydevd_frame_evaluator.cpython-36m-x86_64-linux-gnu.so) frame #31: <unknown function> + 0x1918e4 (0x55a8f99a58e4 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #32: _PyFunction_FastCallDict + 0x1bc (0x55a8f99a6c4c in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #33: _PyObject_FastCallDict + 0x26f (0x55a8f9925b0f in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #34: _PyObject_Call_Prepend + 0x63 (0x55a8f992a6a3 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #35: PyObject_Call + 0x3e (0x55a8f992554e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #36: <unknown function> + 0x16ba91 (0x55a8f997fa91 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #37: _PyObject_FastCallDict + 0x8b (0x55a8f992592b in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #38: <unknown function> + 0x19857e (0x55a8f99ac57e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #39: _PyEval_EvalFrameDefault + 0x30a (0x55a8f99d138a in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #40: <unknown function> + 0x71e1 (0x7f1af51ee1e1 in /home/hyper/.PyCharm2018.1/system/cythonExtensions/_pydevd_frame_eval_ext/pydevd_frame_evaluator.cpython-36m-x86_64-linux-gnu.so) frame #41: <unknown function> + 0x1918e4 (0x55a8f99a58e4 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #42: _PyFunction_FastCallDict + 0x3da (0x55a8f99a6e6a in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #43: _PyObject_FastCallDict + 0x26f (0x55a8f9925b0f in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #44: _PyObject_Call_Prepend + 0x63 (0x55a8f992a6a3 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #45: PyObject_Call + 0x3e (0x55a8f992554e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #46: _PyEval_EvalFrameDefault + 0x19ec (0x55a8f99d2a6c in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #47: <unknown function> + 0x71e1 (0x7f1af51ee1e1 in /home/hyper/.PyCharm2018.1/system/cythonExtensions/_pydevd_frame_eval_ext/pydevd_frame_evaluator.cpython-36m-x86_64-linux-gnu.so) frame #48: <unknown function> + 0x1918e4 (0x55a8f99a58e4 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #49: _PyFunction_FastCallDict + 0x1bc (0x55a8f99a6c4c in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #50: _PyObject_FastCallDict + 0x26f (0x55a8f9925b0f in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #51: _PyObject_Call_Prepend + 0x63 (0x55a8f992a6a3 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #52: PyObject_Call + 0x3e (0x55a8f992554e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #53: <unknown function> + 0x16ba91 (0x55a8f997fa91 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #54: _PyObject_FastCallDict + 0x8b (0x55a8f992592b in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #55: <unknown function> + 0x19857e (0x55a8f99ac57e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #56: _PyEval_EvalFrameDefault + 0x30a (0x55a8f99d138a in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #57: <unknown function> + 0x71e1 (0x7f1af51ee1e1 in /home/hyper/.PyCharm2018.1/system/cythonExtensions/_pydevd_frame_eval_ext/pydevd_frame_evaluator.cpython-36m-x86_64-linux-gnu.so) frame #58: <unknown function> + 0x1918e4 (0x55a8f99a58e4 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #59: _PyFunction_FastCallDict + 0x3da (0x55a8f99a6e6a in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #60: _PyObject_FastCallDict + 0x26f (0x55a8f9925b0f in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #61: _PyObject_Call_Prepend + 0x63 (0x55a8f992a6a3 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #62: PyObject_Call + 0x3e (0x55a8f992554e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) frame #63: _PyEval_EvalFrameDefault + 0x19ec (0x55a8f99d2a6c in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python) ```

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MDU6SXNzdWU0MTA3MjM0Mzk=

unicode

closed

NONE

The general run_squad.py doesn't appear to work properly for python 2.7 because of the json dumping string vs unicode issues during the eval. python2.7 run_squad.py \ --bert_model bert-base-uncased \ --do_train \ --do_predict \ --do_lower_case \ --train_file $SQUAD_DIR/train-v1.1.json \ --predict_file $SQUAD_DIR/dev-v1.1.json \ --train_batch_size 12 \ --learning_rate 3e-5 \ --num_train_epochs 2.0 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /pytorch-pretrained-BERT/tmp/debug_squad/ 02/15/2019 04:55:17 - INFO - __main__ - Writing predictions to: /pytorch-pretrained-BERT/tmp/debug_squad/predictions.json 02/15/2019 04:55:17 - INFO - __main__ - Writing nbest to: /pytorch-pretrained-BERT/tmp/debug_squad/nbest_predictions.json Traceback (most recent call last): File "run_squad.py", line 1077, in <module> main() File "run_squad.py", line 1073, in main args.version_2_with_negative, args.null_score_diff_threshold) File "run_squad.py", line 619, in write_predictions writer.write(json.dumps(all_predictions, indent=4) + "\n") TypeError: write() argument 1 must be unicode, not str

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MDExOlB1bGxSZXF1ZXN0MjUzMzM5NDI4

Fix some bug about SQuAD code

closed

CONTRIBUTOR

Fix issue in #207  This error occurs when 'nbest' only contain 1 item, but none 'text'. So the code to add empty will not work. I add another condition to solve it. https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_squad.py#L570-L590

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MDU6SXNzdWU0MTA2NDYxMDg=

Conversion of gpt-2 small model

closed

NONE

Hey! This seems like something a lot of folks will want. I'd like to be able to load GPT-2 117M and fine-tune it. What's necessary to convert it? I looked at the tensorflow code a little and it looks vaguely related to transformer xl, but I haven't looked at the paper yet or etc.

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MDU6SXNzdWU0MTA1OTEzMTA=

Have you eval the inference speed of transformer-xl?

closed

CONTRIBUTOR

Thank you very much!

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MDU6SXNzdWU0MTAxNDMwNjY=

DataParallel imbalanced memory usage

closed

NONE

Simialr to this issue: https://discuss.pytorch.org/t/dataparallel-imbalanced-memory-usage/22551/12, when I run run_lm_finetuning.py using 4 GPUs on Microsoft Azure, the first GPU will have 4000MB Memory usage while the other 3 are at 700MB. The Volatile Util for the first GPU also is at 100% while the rest are at 0%. It seems that the solution might be something to do with incorporating the loss calculation in the forward pass but I do not know how to solve it.

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MDU6SXNzdWU0MTAwNzQ5Nzc=

PAD symbols change the output

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NONE

Adding `[PAD]` symbols to an input sentence changes the output of the model. I put together a small example here: https://gist.github.com/juditacs/8be068d5f9063ad68e3098a473b497bd I also noticed that the seed state affects the output as well. Resetting it in every run ensures that the output is always the same. Is this because of layernorm?

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MDU6SXNzdWU0MDk4NzA1NDM=

80min training time to fine-tune BERT-base on the SQuAD dataset instead of 24min?

closed

NONE

I just fine-tuned BERT-base on the SQuAD dataset with an AWS EC2 `p3.2xlarge` Deep Learning AMI with a single Tesla V100 16GB: I used the config in your README: ``` export SQUAD_DIR=/path/to/SQUAD python run_squad.py \ --bert_model bert-base-uncased \ --do_train \ --do_predict \ --do_lower_case \ --train_file $SQUAD_DIR/train-v1.1.json \ --predict_file $SQUAD_DIR/dev-v1.1.json \ --train_batch_size 12 \ --learning_rate 3e-5 \ --num_train_epochs 2.0 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /tmp/debug_squad/ ``` It took 80min. According to your README: > This example code fine-tunes BERT on the SQuAD dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large) on a single tesla V100 16GB. How to explain this difference? Is there any way to accelerate the training to 24min as well? Thanks

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MDU6SXNzdWU0MDk4NjExMjI=

Argument do_lower_case is repeated in run_lm_finetuning.py

closed

NONE

Hi, I am trying to finetune LM and am facing the following issue. **argparse.ArgumentError: argument --do_lower_case: conflicting option string: --do_lower_case**

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MDExOlB1bGxSZXF1ZXN0MjUyNzE1NDQ3

--do_lower_case is duplicated in parser args

closed

CONTRIBUTOR

I'm therefore deleting one repetition (please review!)

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MDU6SXNzdWU0MDk3MTU5NTA=

Help: how to get index/symbol from last_hidden, on text8?

closed

NONE

I am trying on text8 dataset. I want to print next token. The model in source code forward() output is loss, but I want to get logits and softmax result, and finally get next token in vocab. how to get index/symbol from last_hidden, on text8? Thanks

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https://github.com/huggingface/transformers/pull/273

MDExOlB1bGxSZXF1ZXN0MjUyNjE0NzI4

Update to fifth release

closed

MEMBER

Mostly a bug fix update for loading the `TransfoXLModel` from s3: - this fixes a bug in the loading of the pretrained `TransfoXLModel` from the s3 dump (which is a converted `TransfoXLLMHeadModel`) and the weights were not loaded. - I also added a fallback of `OpenAIGPTTokenizer` on BERT's `BasicTokenizer` when SpaCy and ftfy are not installed. Using BERT's `BasicTokenizer` instead of SpaCy should be fine in most cases as long as you have a relatively clean input (SpaCy+ftfy were included to exactly reproduce the paper's pre-processing steps on the Toronto Book Corpus) and this also let us use the `never_split` option to avoid splitting special tokens like `[CLS], [SEP]...` which is easier than adding the tokens after tokenization. - I also updated the README on the tokenizers options and methods which was lagging behind a bit.

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MDU6SXNzdWU0MDk1OTg4NjU=

Facing issue in Run Fine tune LM

closed

NONE

So my LM sample.txt is such that each doc has only one line So in BERTDataSet len is giving negative I tried changing it to self.num_docs - 1 def __len__(self): print(self.corpus_lines ,self.num_docs) return self.corpus_lines - self.num_docs - 1 I am also getting errors at multiple steps, Is the code written with the assumption that each document will have multiple lines in it?

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MDU6SXNzdWU0MDk1ODU5NzQ=

Transformer-XL: wrong encoding in the vocab

closed

NONE

Seems that something odd happened during vocab serialization as many symbols with non-latin symbols are broken. E.g.: ``` In [1]: import pytorch_pretrained_bert In [2]: tokenizer = pytorch_pretrained_bert.TransfoXLTokenizer.from_pretrained('transfo-xl-wt103') In [4]: print(tokenizer.idx2sym[224178]) 'Enquêtes ``` The correct token should be "'Enquêtes". And there are around 10k tokens like this. Could it be 'encoding="latin1"' here? https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/convert_transfo_xl_checkpoint_to_pytorch.py#L54

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MDU6SXNzdWU0MDk1MTY1MzA=

Transformer-XL: hidden states are nan

closed

COLLABORATOR

Hi, I followed the code in the Transformer-XL section: ```python import torch from pytorch_pretrained_bert import TransfoXLTokenizer, TransfoXLModel, TransfoXLLMHeadModel # Load pre-trained model tokenizer (vocabulary from wikitext 103) tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103') # Tokenized input text_1 = "Who was Jim Henson ?" text_2 = "Jim Henson was a puppeteer" tokenized_text_1 = tokenizer.tokenize(text_1) tokenized_text_2 = tokenizer.tokenize(text_2) # Convert token to vocabulary indices indexed_tokens_1 = tokenizer.convert_tokens_to_ids(tokenized_text_1) indexed_tokens_2 = tokenizer.convert_tokens_to_ids(tokenized_text_2) # Convert inputs to PyTorch tensors tokens_tensor_1 = torch.tensor([indexed_tokens_1]) tokens_tensor_2 = torch.tensor([indexed_tokens_2]) model = TransfoXLModel.from_pretrained('transfo-xl-wt103') model.eval() # If you have a GPU, put everything on cuda tokens_tensor_1 = tokens_tensor_1 tokens_tensor_2 = tokens_tensor_2 with torch.no_grad(): # Predict hidden states features for each layer hidden_states_1, mems_1 = model(tokens_tensor_1) # We can re-use the memory cells in a subsequent call to attend a longer context hidden_states_2, mems_2 = model(tokens_tensor_2, mems=mems_1) print(hidden_states_1) print(hidden_states_2) ``` (One modification: I'm running this on CPU). The hidden states of both sentences are: ```bash tensor([[[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]]) ``` Is this expected? I wanted to get the embeddings of the two sentences 🤔 Tested with PyTorch 1.0.1 and Python 3.7.

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MDU6SXNzdWU0MDkzODU2MjY=

Get hidden states from all layers of Transformer-XL?

closed

NONE

Hi, Thank you for supporting the pretrained Transformer-XL model! I was wondering if it makes sense to get hidden states from all layers of Transformer-XL as the output, just as what can be done for BERT. It seems this is not supported currently. Practically I found this strategy worked well for BERT and gave better results. Not sure if it is a good idea for Transformer-XL. Thank you!

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https://github.com/huggingface/transformers/pull/268

MDExOlB1bGxSZXF1ZXN0MjUyMjg5MzEy

fixed a minor bug in README.md

closed

CONTRIBUTOR

Assertion failed if one followed the instructions in README.md->Usage->BERT. https://github.com/huggingface/pytorch-pretrained-BERT/issues/266#issuecomment-462730151

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MDU6SXNzdWU0MDkxOTQxODk=

Missing files for Transformer-XL examples

closed

COLLABORATOR

Hi, thanks so much for the new *0.5.0* release. I wanted to train a `TransfoXLModel` model, as described in the `README` [here](https://github.com/huggingface/pytorch-pretrained-BERT/blame/master/README.md#L132). Unfortunately, the files `transfo_xl_train.py` and `transfo_xl_eval.py` are not located in the `examples` directory. Could you please add them to repository? Thanks :heart:

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MDU6SXNzdWU0MDkxNDE0NTM=

Tokenization Incorrect

closed

NONE

The tokenizer is not working correctly for me for e.g. [CLS] is gettig broken '[' , 'cl ', '##s', ']' In [1]: import torch ...: from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMas ...: kedLM ...: ...: # Load pre-trained model tokenizer (vocabulary) ...: tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex. In [2]: text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP ...: ]" ...: tokenized_text = tokenizer.tokenize(text) In [3]: tokenized_text Out[3]: ['[', 'cl', '##s', ']', 'who', 'was', 'jim', 'henson', '?', '[', 'sep', ']', 'jim', 'henson', 'was', 'a', 'puppet', '##eer', '[', 'sep', ']']

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MDU6SXNzdWU0MDg3NzEwODc=

Variance Sources

closed

NONE

Hi, when I change the `--seed` argument, I get a high variance between different runs on my dataset. So I was wondering where the sources of variance might come from. I see that the seed is set (e.g. in `run_squad.py`) via: `random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed)` But how can I find out where randomization is actually used? I found the `RandomSampler` and replaced it with a `SequentialSampler`, but the variance remains high. I know that `modeling.py` randomly initializes the weights, but these are overwritten by the fixed weights when loading a pre-trained BERT model, e.g. `bert-base-uncased`, correct? Can anyone point me in any other direction where my source of variance might come from? Thanks!

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MDU6SXNzdWU0MDg3Mjk5MTY=

RuntimeError: cuda runtime error while running run_classifier.py with 'bert-large-uncased' bert model

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MDU6SXNzdWU0MDgzNDM3MzA=

potential bug in extract_features.py

closed

NONE

Hi, `token_type_ids` is not set for this line: `all_encoder_layers, _ = model(input_ids, token_type_ids=None, attention_mask=input_mask)` https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/extract_features.py#L267, this does not affect single sequence feature extraction, but for a pair of sequence, the model will process the pair as a single sequence and add `A` embedding to the two sequences, which should add `A`, `B` respectively. Seems like a bug. Best, Jie

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MDU6SXNzdWU0MDc4NDk2Mjg=

speed becomes slow

closed

NONE

Hi, I'm trying to fine-tune Bert-base-uncased model for Squad v1.1 on microsoft azure. And I'm experiencing slow speed as the training continues. [logs from first epoch] Iteration: 3%|▎ | 452/14774 [01:46<57:06, 4.18it/s] Iteration: 3%|▎ | 453/14774 [01:46<57:07, 4.18it/s] Iteration: 3%|▎ | 454/14774 [01:47<57:02, 4.18it/s] Iteration: 3%|▎ | 455/14774 [01:47<57:14, 4.17it/s] Iteration: 3%|▎ | 456/14774 [01:47<57:12, 4.17it/s] Iteration: 3%|▎ | 457/14774 [01:47<57:23, 4.16it/s] Iteration: 3%|▎ | 458/14774 [01:48<57:26, 4.15it/s] [logs from 2nd epoch] Iteration: 29%|██▉ | 4313/14774 [3:51:45<10:33:14, 3.63s/it] Iteration: 29%|██▉ | 4314/14774 [3:51:49<10:31:50, 3.62s/it] Iteration: 29%|██▉ | 4315/14774 [3:51:52<10:31:40, 3.62s/it] Iteration: 29%|██▉ | 4316/14774 [3:51:56<10:28:11, 3.60s/it] Iteration: 29%|██▉ | 4317/14774 [3:51:59<10:29:19, 3.61s/it] Iteration: 29%|██▉ | 4318/14774 [3:52:03<10:27:00, 3.60s/it] I have seen you were also using Microsoft Azure, and I wonder if you could help me to figure out what was wrong with my setting. [Azure Cluster configuration] VM size : STANDARD_NC6S_V3 (single Tesla V100) Operating system : Canonical UbuntuServer 16.04-LTS (latest) Auto scale : true Target number of nodes : 1 (Min: 0, Max: 50) [json file used to submit the job] "containerSettings": { "imageSourceRegistry": { "image": "pytorch/pytorch:latest" } }, "jobPreparation": { "commandLine": "conda install python==3.7 && pip install requests boto3 tqdm" }, And I have used the same setting in the repo except tran_batch_size=6. Thanks!

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https://github.com/huggingface/transformers/pull/261

MDExOlB1bGxSZXF1ZXN0MjUxMDM3Mjc3

removing unused argument eval_batch_size from LM finetuning #256

closed

CONTRIBUTOR

Removing unused eval_batch_size argument for simplification. As requested in #256.

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MDU6SXNzdWU0MDc0OTkxODk=

pretrained model(s) in onnx format

closed

CONTRIBUTOR

hi, would you assist/help in order to export/convert at least 1 model into the onnx format ? https://onnx.ai kind

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MDU6SXNzdWU0MDc0NzUxOTU=

please add option to load fine-tuned file to CPU if trained on GPU

closed

NONE

I fine-tuned the pytorch_model.bin on a GPU machine (google cloud) but need to use it on my home computer (no GPU). When I tried to open it using `model = BertForMaskedLM.from_pretrained(bert_version)` I got the following error: RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location='cpu' to map your storages to the CPU. Perhaps you can add an option into `from_pretrained()` such as `cpu=True` which will then call `torch.load(weights_path, map_location=lambda storage, location: 'cpu')`

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MDExOlB1bGxSZXF1ZXN0MjUwODYzMTI1

Fix the undefined variable in squad example

closed

CONTRIBUTOR

`train_dataset` is undefined

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MDU6SXNzdWU0MDcyMTgxMTA=

Minor redundancy in model defintion?

closed

NONE

this is a _major_ nitpick but it was a bit confusing at first: https://github.com/huggingface/pytorch-pretrained-BERT/blob/822915142b2f201c0b01acd7cffe1b05994d2d82/pytorch_pretrained_bert/modeling.py#L206-L212 L212 can simply be replaced by `self.all_head_size = config.hidden_size` as you already error out if the result of division isn't a whole number.

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MDU6SXNzdWU0MDcwNTE5NzI=

does run_lm_finetuning.py actually use --eval_batch_size?

closed

NONE

I'm looking through this code (thanks so much for writing it, btw) and I'm not seeing whether it actually uses eval_batch_size at all. If it doesn't, is it still performing an evaluation step to assess goodness of fit?

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Error while using Apex

closed

NONE

Hi, I am trying to do mixed precision training, but I have countered a problem that seems to be related to the LayerNorm implementation of Apex. I have the following error msg while running the example (same error for my other code). ` Traceback (most recent call last): File "run_lm_finetuning.py", line 648, in <module> main() File "run_lm_finetuning.py", line 529, in main model = BertForPreTraining.from_pretrained(args.bert_model) File "/home/chenyang/anaconda3/envs/pytorch10/lib/python3.6/site-packages/pytorch_pretrained_bert/modeling.py", line 506, in from_pretrained model = cls(config, *inputs, **kwargs) File "/home/chenyang/anaconda3/envs/pytorch10/lib/python3.6/site-packages/pytorch_pretrained_bert/modeling.py", line 689, in __init__ self.bert = BertModel(config) File "/home/chenyang/anaconda3/envs/pytorch10/lib/python3.6/site-packages/pytorch_pretrained_bert/modeling.py", line 600, in __init__ self.embeddings = BertEmbeddings(config) File "/home/chenyang/anaconda3/envs/pytorch10/lib/python3.6/site-packages/pytorch_pretrained_bert/modeling.py", line 183, in __init__ self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) File "/home/chenyang/anaconda3/envs/pytorch10/lib/python3.6/site-packages/apex-0.1-py3.6.egg/apex/normalization/fused_layer_norm.py", line 126, in __init__ File "/home/chenyang/anaconda3/envs/pytorch10/lib/python3.6/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 994, in _gcd_import File "<frozen importlib._bootstrap>", line 971, in _find_and_load File "<frozen importlib._bootstrap>", line 953, in _find_and_load_unlocked ModuleNotFoundError: No module named 'fused_layer_norm_cuda' ` I am wondering if it is related to the version of Apex, so may I know which Apex checkpoint you used.

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MDExOlB1bGxSZXF1ZXN0MjUwNTg0MTgx

Python 2

closed

MEMBER

Make the package compatible with python 2.7+

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https://github.com/huggingface/transformers/pull/253

MDExOlB1bGxSZXF1ZXN0MjUwNTU2Njk0

Merge pull request #1 from huggingface/master

closed

NONE

updating the repo

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MDU6SXNzdWU0MDY5MTk5Mzk=

BERT tuning all parameters?

closed

CONTRIBUTOR

Just a clarification question: when tuning bert parameters (say, for SQUAD), does it tune the parameters of the final parameter or the whole BERT model?

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https://github.com/huggingface/transformers/pull/251

MDExOlB1bGxSZXF1ZXN0MjUwMTIyMDA2

Only keep the active part mof the loss for token classification

closed

CONTRIBUTOR

If attention mask is not none, then we want to restrict our loss to the items which are not padding (hereby assumed those that have attention_mask = 1). This is important if doing e.g NER.

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MDExOlB1bGxSZXF1ZXN0MjQ5ODc2Mjk2

Fix squad answer start and end position

closed

CONTRIBUTOR

Previous version might miss some overlong answer start and end indices (which should be 0), and sometimes the start/end positions would be outside the model inputs. Doc_start and doc_end are based on tokenized subword sequences while example.start_position and example.end_position are indices in original word-level, which are usually shorter than the subword sequences. Why not use tok_start_position and tok_end_position for comparison?

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MDU6SXNzdWU0MDYwNTcxNTg=

Fine tuning Bert for Question answering

closed

NONE

I just wanted to ask if the weights of the Bert base model are getting updated while fine tuning Bert for Question answering. I see that the Bert for QA is a model with A linear layer on top of Bert pre-trained model. I am trying to reproduce the same model in keras. Could any one tell me if i should freeze the layers in the Bert base model or not?

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MDExOlB1bGxSZXF1ZXN0MjQ5Njk0NjIx

fix prediction on run-squad.py example

closed

CONTRIBUTOR

run_squad.py exits with an error when running do_predict without training. The error is due to the model_state_dict not existing when --do_predict is selected. Traceback (most recent call last): File "run_squad.py", line 980, in <module> main() File "run_squad.py", line 923, in main model_state_dict = torch.load(output_model_file) File "/home/joe/pytorchnlp/lib/python3.5/site-packages/torch/serialization.py", line 365, in load f = open(f, 'rb') FileNotFoundError: [Errno 2] No such file or directory: '../../BERT_work/Squad/pytorch_model.bin'

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MDU6SXNzdWU0MDU3NTc2NTQ=

Multilabel classification and diverging loss

closed

NONE

Hi, I'm not sure I'm posting this at the right spot but I am trying to use your excellent implementation to do some multi label classification on some text. I basically adapted the run_classifier.py code to a Jupyter Notebook and change a little bit the BERT Sequence Classifier model so it can handle multilabel classification. However, my loss tends to diverge and my outputs are either all ones or all zeros. The labels distribution in my train dataset is : `array([ 65, 564, 108, 17, 40, 26, 306, 195, 25, 345, 54, 80, 214]) ` i.e. the label 1 is used 65 times, the label 2 is used 564 times etc... Each sample has between 1 and 4 labels. I am using the Adam Optimizer on the BCEWithLogitsLoss and I am unable to figure out where the problem comes from? Should I add some weights in my loss function? Do I use it in a right way? Is my model wrong somewhere? I attach to this post a Notebook of my test. Maybe someone encountered the same problem before and could help me? [NOTEBOOK](https://nbviewer.jupyter.org/github/nicolas-mingione/nlp/blob/master/test_bert.ipynb) Thanks !

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MDExOlB1bGxSZXF1ZXN0MjQ5NTA5Njg5

Accurate SQuAD answer start and end position

closed

CONTRIBUTOR

Previous version might miss some overlong answer start and end indices (which should be 0), and sometimes the start/end positions would be outside the model inputs. Doc_start and doc_end are based on tokenized subword sequences while example.start_position and example.end_position are indices in original word-level, which are usually shorter than the subword sequences.

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MDU6SXNzdWU0MDUzOTY2MTk=

can you do a new release + pypi

closed

CONTRIBUTOR

we've been getting some requests to incorporate newer features into allennlp that are only on master (e.g. `never_split`). thanks!

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MDExOlB1bGxSZXF1ZXN0MjQ5MTk4NTU1

Avoid confusion of inplace LM masking

closed

NONE

Fix confusion of LM masking that happens inplace. Discussed in https://github.com/huggingface/pytorch-pretrained-BERT/issues/243 and https://github.com/huggingface/pytorch-pretrained-BERT/issues/226

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MDU6SXNzdWU0MDUxNTY2NTg=

seems there is a bug in fine tuning language model

closed

NONE

For masked language model, the input should be tokens masked. But in examples/run_lm_finetuning.py, input is not masked. In method convert_example_to_features, is it supposed to use masked output as token input? tokens = [] segment_ids = [] tokens.append("[CLS]") segment_ids.append(0) for token in tokens_a:# is tokens_a supposed to be t1_random? tokens.append(token) segment_ids.append(0) tokens.append("[SEP]") segment_ids.append(0) assert len(tokens_b) > 0 for token in tokens_b: tokens.append(token) segment_ids.append(1) tokens.append("[SEP]") segment_ids.append(1) input_ids = tokenizer.convert_tokens_to_ids(tokens)

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https://github.com/huggingface/transformers/pull/242

MDExOlB1bGxSZXF1ZXN0MjQ5MDEyODU5

Fix argparse type error

closed

CONTRIBUTOR

Resolved the following error on executing `run_squad2.py --help` ```TypeError: %o format: an integer is required, not dict```

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MDU6SXNzdWU0MDQ5MDU4NDI=

Tokenization doesn't seem to match BERT paper

closed

CONTRIBUTOR

In the [BERT paper](https://arxiv.org/abs/1810.04805) section 4.3 ("Named Entity Recognition") there is an example of some tokenized text: ```python ['Jim', 'Hen', '##son', 'was', 'a', 'puppet', '##eer'] ``` However, when I take that sentence and try to tokenize it myself with `BertTokenizer` from this repo ```python from pytorch_pretrained_bert import BertTokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-cased', do_lower_case=False) text = "Jim Henson was a puppeteer" tokenizer.tokenize(text) == ['Jim', 'He', '##nson', 'was', 'a', 'puppet', '##eer'] ``` same thing happens if I pre-tokenize and just use `BertTokenizer.wordpiece_tokenizer.tokenize()` ```python from itertools import chain from pytorch_pretrained_bert import BertTokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-cased', do_lower_case=False) text = "Jim Henson was a puppeteer".split() list(chain.from_iterable([tokenizer.wordpiece_tokenizer.tokenize(token) for token in text])) == ['Jim', 'He', '##nson', 'was', 'a', 'puppet', '##eer'] ``` Is there something I am misunderstanding / doing wrong or is this an actual bug? The BERT paper and this repo tokenize `"Henson"` as `['Hen', '##son']` and `['He', '##nson']` respectively.

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MDExOlB1bGxSZXF1ZXN0MjQ4OTc5MjIw

Minor update in README

closed

CONTRIBUTOR

Updated links to classes in `modeling.py`

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MDU6SXNzdWU0MDQ4NTAzMjk=

cannot load BERTAdam when restoring from BioBert

closed

NONE

I am trying to convert the recently released BioBert checkpoint: https://github.com/naver/biobert-pretrained The conversion script loads the checkpoint, but appears to balk at BERTAdam when building the Pytorch model. ``` ... Building PyTorch model from configuration: { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 2, "vocab_size": 28996 } Initialize PyTorch weight ['bert', 'embeddings', 'LayerNorm', 'beta'] Traceback (most recent call last): File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) File "/usr/lib/python3.6/runpy.py", line 85, in _run_code exec(code, run_globals) File "/venvs/dev3.6/lib/python3.6/site-packages/pytorch_pretrained_bert/__main__.py", line 19, in <module> convert_tf_checkpoint_to_pytorch(TF_CHECKPOINT, TF_CONFIG, PYTORCH_DUMP_OUTPUT) File "/venvs/dev3.6/lib/python3.6/site-packages/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py", line 69, in convert_tf_checkpoint_to_pytorch pointer = getattr(pointer, l[0]) AttributeError: 'Parameter' object has no attribute 'BERTAdam' ```

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MDU6SXNzdWU0MDQ2MjQ5NjI=

padded positions are ignored when embedding position ids

closed

NONE

When embedding position ids, all positions are considered. ``` seq_length = input_ids.size(1) position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) ``` This is different from most transformer implementations. Should it be ``` position_ids = np.array([ [pos_i+1 if w_i != PAD else 0 for pos_i, w_i in enumerate(seq)] for seq in batch_seq]) ```

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MDU6SXNzdWU0MDQ2MDQ2MTM=

How can I change vocab size for pretrained model?

closed

NONE

Is there way to change (expand) vocab size for pretrained model? When I input the new token id to model, it returns: /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse) 1108 with torch.no_grad(): 1109 torch.embedding_renorm_(weight, input, max_norm, norm_type) -> 1110 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) 1111 1112 RuntimeError: index out of range at /pytorch/aten/src/TH/generic/THTensorMath.cpp:352

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MDU6SXNzdWU0MDQzNjAwODc=

Preprocessing necessary for lengthier text

closed

NONE

Hi, I tried to train squad model on a different dataset where I have lengthier questions/contexts. It gave memory error CUDA out of memory. Tried to allocate 4.50 MiB (GPU 5; 11.78 GiB total capacity; This error seems to happen in pytorch when there are lengthier data points ( pytorch tells how much it tried to allocate as opposed to normal CUDA out of memory error). tensorflow code for bert doesn't give this error as it trims the question/text lengths. I think you should include in this package as well.

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MDU6SXNzdWU0MDQyOTg4NDU=

Training BERT behind a proxy server

closed

NONE

When I try to run BERT training, I get the following error during the vocabulary download: requests.exceptions.ConnectionError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /models.huggingface.co/bert/bert-large-uncased-vocab.txt (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x7fbb55377828>: Failed to establish a new connection: [Errno 110] Connection timed out')) I am running the script behind a proxy server which I suspect is the cause of this error. Is there any way to remedy this?

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MDU6SXNzdWU0MDQyOTQ0ODE=

Fine tuning for evaluation

closed

NONE

Hi! 1) Help me please figure out, what would be optimal batch size for evaluating nextSentencePrediction model? For performance. Is it same as used during pre-training (128)? 2) If i building high performance evaluating backend on CUDA, would it be a good idea to use several threads with bert model in each, or its better to use one thread with proper batching?

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MDU6SXNzdWU0MDM4MTAwNzk=

What is get_lr() meaning in the optimizer.py

closed

NONE

I use a Model based on BertModel, and when I use the BertAdam the learning rate isn't changed. And when I use `get_lr()`, the return result is `[0]`. And I see the length of state isn't 0, but why I get that?

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MDU6SXNzdWU0MDM1NzQxMjM=

Why is the output bias computed separately?

closed

NONE

Hi ! Sorry if this is a dumb question, but I don't understand why is the bias [added separately](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L379) to the decoder weights instead of using `self.decoder = nn.Linear(num_features, num_tokens, bias=True)`? Isn't it equivalent?

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MDU6SXNzdWU0MDM0OTQ0ODc=

Cleaning `~/.pytorch_pretrained_bert`

closed

CONTRIBUTOR

What is inside `~/.pytorch_pretrained_bert`? Is it just the downloaded pre-trained model weights? Is it safe to remove this directory?