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DanielPFlorian

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Transformers-Github-Issues

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3413?src=pr&el=h1) Report\n> Merging [#3413](https://codecov.io/gh/huggingface/transformers/pull/3413?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/e392ba6938f50655a195ea7ec8a260b1e9fc6058&el=desc) will **increase** coverage by `0.02%`.\n> The diff coverage is `93.75%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3413?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3413 +/- ##\n==========================================\n+ Coverage 77.56% 77.58% +0.02% \n==========================================\n Files 100 100 \n Lines 16970 16993 +23 \n==========================================\n+ Hits 13162 13184 +22 \n- Misses 3808 3809 +1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3413?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3413/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.71% <ø> (-0.02%)` | :arrow_down: |\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/3413/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `73.05% <93.10%> (+0.52%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3413/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `84.44% <100.00%> (+0.52%)` | :arrow_up: |\n| [src/transformers/tokenization\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/3413/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdDUucHk=) | `95.89% <100.00%> (+0.05%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3413?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3413?src=pr&el=footer). Last update [e392ba6...23778d1](https://codecov.io/gh/huggingface/transformers/pull/3413?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "This should be fixed on master since a8e3336a850e856188350a93e67d77c07c85b8af.\r\n\r\nFeel free to re-open if that's not the case.", "You might want to upgrade your repo.\r\n`pip install --upgrade .`" ]

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[ "> pending my comments\r\n\r\nVery much down to share the summarization code in another PR!", "Code quality test fails because of unpinned isort library (see https://github.com/huggingface/transformers/pull/3449)" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3408?src=pr&el=h1) Report\n> Merging [#3408](https://codecov.io/gh/huggingface/transformers/pull/3408?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/e392ba6938f50655a195ea7ec8a260b1e9fc6058&el=desc) will **decrease** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3408?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3408 +/- ##\n==========================================\n- Coverage 77.56% 77.55% -0.01% \n==========================================\n Files 100 100 \n Lines 16970 16970 \n==========================================\n- Hits 13162 13161 -1 \n- Misses 3808 3809 +1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3408?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3408/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.58% <0.00%> (-0.14%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3408?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3408?src=pr&el=footer). Last update [e392ba6...756792d](https://codecov.io/gh/huggingface/transformers/pull/3408?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "@songsuoyuan , if you notice the line:\r\n p.data.mul_(1 - group['lr'] * group['weight_decay'])\r\nThe multiplication factor is (1 - group['lr'] * group['weight_decay']) .\r\nAll subsequent first and second order moment calculations are not using p.data anymore.\r\nThis means we would get the same result if we had skipped that multiplication and introduce an addition operation at the end if weight decay > 0 with p.data._mul(-group['lr'] * group['weight_decay']) and this what has been done in the hugginFace implementation as well.\r\nSo essentially both are same.\r\n\r\nAlso in the paper, in-fact the weight decay term is introduced at end ( line-12 :Algorithm-2). Decay term in line-6 corresponds to L2 regularization which is not used here.\r\nTherefore it looks to me both the implementation are the same and reflect what {ilya,fh}@ proposed in the paper.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "I find this question too. Two codes are obviously different. \r\nBecause the `p.data` in huggingface has changed through `p.data.addcdiv_(-step_size, exp_avg, denom)`\r\nBut I can't understand why.", "*bump*", "Update: they are indeed the same. PyTorch's implementation is just too confusing to understand.", "They are not equivalent. This should be reported as a bug, but I see the huggingface AdamW has been deprecated.", "bump again.\r\nI see old code from researcher on github use AdamW with huggingface scheduler\r\n\r\n```\r\nfrom pytorch_transformers import AdamW, WarmupLinearSchedule\r\n```\r\n\r\nShould I replace AdamW of huggingface to AdamW of pytorch ?\r\n\r\n```\r\nfrom torch.optim import AdamW\r\nfrom pytorch_transformers import WarmupLinearSchedule\r\n```\r\n\r\nAny advise ?\r\n" ]

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[ "hm my local isort passes even on a clean env\r\n```\r\n(transformers) shoarora@sho-5:~/transformers ‹glue-test-processors›\r\n$ make style\r\nblack --line-length 119 --target-version py35 examples templates tests src utils\r\nAll done! ✨ 🍰 ✨\r\n243 files left unchanged.\r\nisort --recursive examples templates tests src utils\r\n(transformers) shoarora@sho-5:~/transformers ‹glue-test-processors›\r\n$ which isort\r\n/home/shoarora/miniconda3/envs/transformers/bin/isort\r\n```\r\n\r\nUltimately, I ran it in the `circleci/python:3.6` docker image to get the correct formatting. This disagrees with what happens when I style locally in a clean env. ", "Hi @shoarora, this is a good addition but in the meantime we updated the run_glue script (and associated utilities) quite a bit in #3800.\r\n\r\nWould you like to take a stab at updating this (probably opening a new PR)? The `Trainer`'s predict method accepts non-labelled datasets now so it should be pretty straightforward to hook it. Let us know, otherwise we'll do it down the line.", "Would love to see this updated and merged :)", "@ZhaofengWu You can check out https://github.com/huggingface/transformers/pull/4463 which we are going to take a look at soon", "Thanks!", "Closed by #4463" ]

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[ "As a non-blocking question, I do note that a lot of the examples use argparse to parse comparatively long lists of arguments. I've maintained the extant style in this PR to avoid causing noise and confusion Would it be acceptable if I broke with this style to use a JSON file to store all the arguments for an experiment?", "Hi @mgoldey, sorry for only responding now. Thanks a lot for adding a seq2seq example :-) I will take a look early next week and maybe we can have a quick chat how to merge this PR and https://github.com/huggingface/transformers/pull/3383. \r\n\r\n", "That sounds good. I'm still tweaking things on my end for accuracy and improved logic as I get more familiar with the code base here. I'll see if I can rebase of #3383 by then, depending on my other workload. Feel free to reach out via google hangouts if you're comfortable.", "Sorry, to answer only now! I'll will soon add a Encoder-Decoder google colab that shows how to use seq2seq ", "Thanks - fine to close. We've moved forward without using seq2seq due to poor overall accuracy with the scale of data in place." ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3400?src=pr&el=h1) Report\n> Merging [#3400](https://codecov.io/gh/huggingface/transformers/pull/3400?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f7dcf8fcea4d486544f221032625a97ad7dc5405&el=desc) will **not change** coverage by `%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3400?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3400 +/- ##\n=======================================\n Coverage 77.55% 77.55% \n=======================================\n Files 100 100 \n Lines 16970 16970 \n=======================================\n Hits 13161 13161 \n Misses 3809 3809 \n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3400?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3400?src=pr&el=footer). Last update [f7dcf8f...9cfd3da](https://codecov.io/gh/huggingface/transformers/pull/3400?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "This [blogpost](https://huggingface.co/blog/how-to-train) might be interesting, have you seen it?", "Yes, saddly the part in which I am interested in, namely, instantiating and training/testing from scratch with my own data are not almost not or not at all described.\r\n\r\nDo you know if it is possible to feed individual tensors to the model ? And if so, how should the dimensions (batch, sequence etc..) be ordered ? I would like to write my own training function for more flexibility.", "To answer my own question, everything can be found in the code, by reading the docstrings : https://github.com/huggingface/transformers/blob/v2.5.1/src/transformers/modeling_gpt2.py#L99\r\n", "@johncwok Didu succeed in training the GPT2 model on your own dataset from scratch? ", "I did, but it didn't produce very good results. Either my data is not good enough or I need more layers, but I have reach the max. of what my computer capacity can allow me ", "Hi @johncwok, I plan to train gpt2 on my data. Do you mind to share your training script, along with the raw data and the code to preprocess it?" ]

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[ "Merging with deep suspicion that circleci failure is spurious." ]

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[ "Hi Alex, language support is described in the metadata in the models' [model cards](https://github.com/huggingface/transformers/tree/master/model_cards) and then rendered in a searchable way on the https://huggingface.co/models website.\r\n\r\nThe mapping is not exhaustive right now because a lot of the canonical/historical models do not have a model card yet. Feel free to create them for the models you're researching. cc @thomwolf @LysandreJik @clmnt ", "Thanks, that's a good place to start. I am more interested in the canonical/historical models as you say. I see that some README.md in model cards have a \r\n```\r\n---\r\nlanguage:\r\n- bulgarian\r\n- czech\r\n- polish\r\n- russian\r\n- ...\r\n---\r\n```\r\nAre you OK for me to do a pull request to add these?\r\n\r\nI would also like to standardize languages using the ISO 693-1 two-letter code (https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). Do you agree on this standard? ", "Sounds good to me!", "Super, I have forked the repo and created a branch. Expect a pull request in the new few weeks :) so we can close this.", "FYI @MobiusLooper", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "In order to import the tensorflow models, you need to have TF2+ installed. Please update your environment info if you *do* have TF2 installed in the environment in which you're running your script.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "As for guidelines about making MMBT work, here is an example on the mm-imdb dataset: https://github.com/huggingface/transformers/blob/master/examples/mm-imdb/run_mmimdb.py.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Hi seems like above examples folder has been removed, is it because the multi modal experiment is in intermediate stage.", "I believe it's available here: https://github.com/huggingface/transformers/tree/master/examples/contrib/mm-imdb", "Hey is there any progress with it soon? \r\nI find only the mm-imdb example: https://github.com/huggingface/transformers/tree/master/examples/contrib/mm-imdb\r\n\r\nYour LXMERT model receives only text features from what I see (\"visual_feats - These are currently not provided by the transformers library.\")\r\n\r\nThanks :) ", "The example for mmbt on mm-imdb is also an invalid link now. ", "Here is a correct link for now https://github.com/huggingface/transformers/tree/master/examples/research_projects/mm-imdb", "> As for guidelines about making MMBT work, here is an example on the mm-imdb dataset: https://github.com/huggingface/transformers/blob/master/examples/mm-imdb/run_mmimdb.py.\r\n\r\nThe link is broken!", "> The link is broken!\r\n\r\nSee the reply above you :) That seems to work" ]

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[ "Thanks for sharing! Any way you could format the training results as a Markdown table? Might be more readable.", "I'll merge for now" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3391?src=pr&el=h1) Report\n> Merging [#3391](https://codecov.io/gh/huggingface/transformers/pull/3391?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/cf72479bf11bf7fbc499a518896dfd3cafdd0b21&el=desc) will **increase** coverage by `0.01%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3391?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3391 +/- ##\n==========================================\n+ Coverage 77.55% 77.56% +0.01% \n==========================================\n Files 100 100 \n Lines 16970 16970 \n==========================================\n+ Hits 13161 13163 +2 \n+ Misses 3809 3807 -2 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3391?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3391/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.72% <0.00%> (+0.13%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3391/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `84.09% <0.00%> (+0.17%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3391?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3391?src=pr&el=footer). Last update [cf72479...c941e45](https://codecov.io/gh/huggingface/transformers/pull/3391?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Hi all\r\n\r\nI am using colab , 1 GPU , Tesla P100-PCIE-16GB\r\n\r\ncode below ran OK \r\n\r\n!python /content/transformers/examples/run_language_modeling.py \\\r\n --output_dir=/content/outputs \\\r\n --model_type=bert \\\r\n --model_name_or_path=bert-base-cased \\\r\n --num_train_epochs 1\\\r\n --do_train \\\r\n --do_eval \\\r\n --per_gpu_train_batch_size 152\\\r\n --train_data_file=/content/input_data/trn.txt \\\r\n --eval_data_file=/content/input_data/val.txt \\\r\n --evaluate_during_training \\\r\n --learning_rate 1e-4\\\r\n --overwrite_output_dir\\\r\n --tokenizer_name /content/token/ \\\r\n --block_size 64\\\r\n --mlm\r\n\r\n\r\n\r\n\r\n\r\n(and batch_size 152 was max num i was able to run without cuda out of memory )\r\nthen installing apex by \r\n\r\n\r\n\r\n\r\n%%writefile setup.sh\r\n\r\nexport CUDA_HOME=/usr/local/cuda-10.1\r\ngit clone https://github.com/NVIDIA/apex\r\npip install -v --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" ./apex\r\n\r\n!sh setup.sh\r\n\r\n\r\n\r\nthen adding \" --fp16\\\" to code but i was not able to increase batch size , even abit\r\n\r\ndo you know that ?\r\n@thomwolf , @VictorSanh , @aaugustin , @BramVanroy , @julien-c , @LysandreJik", "is it also the case with GTX 1080 , any one tried ?\r\n", "and one more thing , \r\ndoes any function in those scripts , concatenate the short lines to each other ?\r\nin order not to be enforced to pad each line so much ", "Please don't mass-tag people — thanks.", "solve :\r\nthat was because i was using p100" ]

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[ "Hey @jia-zhuang,\r\n\r\nAs you can see [here](https://github.com/huggingface/transformers/blob/master/examples/ner/run_tf_ner.py#L525) I update the classification layer to add a softmax as activation, then the `from_logits=True` is not necessary.", "@jplu Thanks for your reply! I learn a lot from your code." ]

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[ "Can anyone advise on the failed tests? Seems to failing in parts of the code-base I haven't touched.", "@GCHQResearcher92457 Yes, the failures are unrelated.", "Most recent failures are unrelated.", "Quick question in passing because I am working on something close, did you run some benchmark to see how this behaves speedwise?", "> Quick question in passing because I am working on something close, did you run some benchmark to see how this behaves speedwise?\r\n\r\nI've found in practice so far that training iterations are the same speed using this method as the previous methods, i.e. the bottleneck seems to be later on. Just did some crude tests on a file of 100 lines to test only the data loading performance.\r\n\r\nInstantiating lazy dataset: 39.1 µs ± 408 ns\r\nInstantiating cached dataset: 17.4 ms ± 41.6 µs \r\nRandom access to single item (lazy): 3.34 µs ± 171 \r\nRandom access to single item (cached): 3.18 µs ± 86.3 ns\r\nCreating a single tokenized batch (lazy): 1.33 ms ± 3.5 µs\r\nCreating a single tokenized batch (cached): 81.5 µs ± 5.15 µs", "I tested out the LazyLineByLineTextDataset and quickly ran out of memory.\r\n\r\nIt looks like linecache isn't capable of efficiently indexing into large files. My ~6GB training data causes linecache to stall & use up 7+ GB of RAM. \r\n\r\nSaw a similar issue [here](https://stackoverflow.com/questions/620367/how-to-jump-to-a-particular-line-in-a-huge-text-file/2727585). Might be better to use a system similar to the second answer in that post where you create a map of line breaks in the file and seek to them. \r\n\r\n```\r\nclass LineSeekableFile:\r\n def __init__(self, seekable):\r\n self.fin = seekable\r\n self.line_map = list() # Map from line index -> file position.\r\n self.line_map.append(0)\r\n while seekable.readline():\r\n self.line_map.append(seekable.tell())\r\n\r\n def __getitem__(self, index):\r\n # NOTE: This assumes that you're not reading the file sequentially. \r\n # For that, just use 'for line in file'.\r\n self.fin.seek(self.line_map[Index])\r\n return self.fin.readline()\r\n```", "> I tested out the LazyLineByLineTextDataset and quickly ran out of memory.\r\n> \r\n> It looks like linecache isn't capable of efficiently indexing into large files. My ~6GB training data causes linecache to stall & use up 7+ GB of RAM.\r\n> \r\n> Saw a similar issue [here](https://stackoverflow.com/questions/620367/how-to-jump-to-a-particular-line-in-a-huge-text-file/2727585). Might be better to use a system similar to the second answer in that post where you create a map of line breaks in the file and seek to them.\r\n> \r\n> ```\r\n> class LineSeekableFile:\r\n> def __init__(self, seekable):\r\n> self.fin = seekable\r\n> self.line_map = list() # Map from line index -> file position.\r\n> self.line_map.append(0)\r\n> while seekable.readline():\r\n> self.line_map.append(seekable.tell())\r\n> \r\n> def __getitem__(self, index):\r\n> # NOTE: This assumes that you're not reading the file sequentially. \r\n> # For that, just use 'for line in file'.\r\n> self.fin.seek(self.line_map[Index])\r\n> return self.fin.readline()\r\n> ```\r\n\r\nDid you run into out-of-memory issues, or did the process simply use a lot of memory? It is likely to be the latter, and that is exactly what line_cache_ is supposed to do: it reads as much of the file into memory as it can for quick access as much as possible (considering the available memory), and then does its work.\r\n\r\nLineSeekableFile can be an alternative but definitely not a good replacement imo (it'll be slower, and expects a file handle to always be open which you often would not want).", "> Did you run into out-of-memory issues, or did the process simply use a lot of memory? It is likely to be the latter, and that is exactly what line_cache_ is supposed to do: it reads as much of the file into memory as it can for quick access as much as possible (considering the available memory), and then does its work.\r\n> \r\n> LineSeekableFile can be an alternative but definitely not a good replacement imo (it'll be slower, and expects a file handle to always be open which you often would not want).\r\n\r\nI ran the code on a GCP VM instance with 13 GB of RAM. My RAM quickly went to 0 and I was kicked out of SSH. I was forced to restart the instance in order to regain access. \r\n\r\nFrom what I'm seeing, it seems like linecache is primarily designed to be used on Python source files, not large text files. From what I can tell, the [source code](https://github.com/python/cpython/blob/10dabbf8d2c1c929f6ac395e19c64b361bd58fdd/Lib/linecache.py#L82) reads all the lines in the file into memory, without any consideration for available memory. ", "@ceremonious I tested this locally with a 50+GB file on my 32GB RAM system and it works as expected. Memory usage goes up to around 95% and stays there. Reproducible code:\r\n\r\n```python\r\nimport linecache\r\nimport random\r\n\r\n\r\ndef get_n_lines(fin, size=65536):\r\n # borrowed from https://stackoverflow.com/a/9631635/1150683\r\n def blocks(files):\r\n while True:\r\n b = files.read(size)\r\n if not b:\r\n break\r\n yield b\r\n\r\n with open(fin, encoding=\"utf-8\") as fhin:\r\n n_lines = sum(bl.count(\"\\n\") for bl in blocks(fhin))\r\n return n_lines\r\n\r\ndef main(fin):\r\n n_lines = get_n_lines(fin)\r\n while True:\r\n idx = random.randint(1, n_lines+1)\r\n line = linecache.getline(fin, idx)\r\n print(line)\r\n\r\n\r\nif __name__ == '__main__':\r\n f = r'path/to/huge/file.txt'\r\n main(f)\r\n```\r\n\r\nI haven't dug into the source code (though I do see a MemoryError check in it), but I have used this for many projects on our own servers and I can tell you that it works (it will utilise as much RAM as it can but won't throw OOM errors). It is good to know that this won't work well with GCP, though! A note should be included in the class's docstring.", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3388?src=pr&el=h1) Report\n> Merging [#3388](https://codecov.io/gh/huggingface/transformers/pull/3388?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d22894dfd40d5c858e8398e2783545103d191b47&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3388?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3388 +/- ##\n=======================================\n Coverage 78.26% 78.26% \n=======================================\n Files 106 106 \n Lines 17964 17964 \n=======================================\n Hits 14060 14060 \n Misses 3904 3904 \n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3388?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3388?src=pr&el=footer). Last update [d22894d...1ead846](https://codecov.io/gh/huggingface/transformers/pull/3388?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "The merge conflicts are a bit of a mess because of datasets and collation being moved outside the main script in `master`. I've opened a new PR where the code used here has been slotted-in to the the more modular format of this script. Please see PR #4009.", "Any updates on this PR? Lazy loading sounds like an important functionality for massive datasets.", "@misrasaurabh1 This PR is closed. See https://github.com/huggingface/transformers/pull/4009 for the continuation." ]

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[ "**Update:**\r\n\r\nWhen I pre-process a token not applied lower case, its EM from the initial weights was almost 76.85. (I couldn't remember exact score.)\r\n\r\nIn the TensorFlow version, its initial weights outputs 76.30 EM.\r\nHowever, their pre-processing has been applied to lower case for a question, document, and answer.\r\n\r\nI got a EM 74.02 with the lower case at the initial step.\r\nAnd, I used an answer as a target inputs and outputs instead of using the answer from a spanning.\r\nIf an answer is in a extracted document, it will be an example in the training step.\r\n\r\nBut serious problem is to decrease the validation performance after a few training steps.\r\nWhen I validated my trained model at 100 steps, its score went down almost 62.XX.\r\n\r\nI think that a problem is one of them for the batch size or wrong pre-processing or bugs in the T5 model.\r\nFor the optimizer, I tested for AdaFactor and Adam optimizer but both results are same.\r\n\r\nI didn't understand one thing when implementing it.\r\nThe thing is that loaded pre-trained weights don't have a weight for the 'lm_head' layer.\r\nI guess that it is for an user who wants to implement own vocabulary.\r\nBut I think this is a reason for that validation accuracy is lower than the TensorFlow version at the initial step. (lm_head layer should be randomly initialized.)\r\n\r\n\r\nWhen I applied a mask for the outputs and inputs_embeds for the encoder and decoder, the validation accuracy goes up. [Value * (mask == 1).float().unsqueeze(2), features of (PAD) should be zero.]\r\nBut I have to train the T5 model more for proving whether correct or not.\r\nAnd, low learning rate is better than a learning rate from the original paper. (Original paper: 1e-3, but I used 5e-5 mentioned in the BERT.)\r\n\r\n\r\nLast, in the previous comment I forgot to write something for my inputs.\r\ninput_ids = ['(QUESTION)', Q_W1, Q_W2, ..., '(CONTEXT)', C_W1, C_W2, (EOS), '(PAD)', ...]\r\nattention_masks = [1, 1, 1, ..., 1, 1, 1, 1, ..., 0, ...]\r\ndecoder_input_ids = ['(PAD)', W1, W2, ..., '(PAD)', '(PAD)', ...]\r\ndecoder_attention_masks = [1, 1, 1, ..., 0, 0, ...]\r\nlm_labels = [W1, W2, ..., '(EOS)', '(PAD)', ...]\r\n\r\nEOS token should be added in a context.\r\nAnd, tokens of the input_ids are [question : Q_W1, Q_W2, ..., context : 'C_W1, C_W2, (EOS), (PAD)', ...]\r\n\r\nI hope it will be helpful for someone who is implementing it.\r\nAnd, I will write more about it when I finish to train my model.", "Hi @h19920918, \r\n\r\nThanks for the in-detail report. Could you quickly post your environment information here as well? \r\nYou can simply run `python transformers-cli env` in the root folder of your cloned transformers repo and copy paste it below. \r\nAnd do you use T5 with Tensorflow or PyTorch? \r\nAlso it would be great if you could copy paste your code for the above experiment here :-) ", "@patrickvonplaten Thank you for your answer.\r\n\r\nUnfortunately, I didn't use all codes in yours.\r\nI partly used your code to implement it.\r\n\r\nFirst, my environment is below:\r\nPython == 3.6.4\r\nPytorch == 1.4.0+cu92\r\nCUDA == 9.2\r\nCuDNN == 6 or 7? (I don't know exactly.)\r\nTransformer == 2.5.1\r\n\r\nActually, I solved the problem.\r\n\r\nPaper: \r\n T5-Small: EM: 79.10 || F1: 87.24\r\nOwn:\r\n T5-Small: EM: 79.03 || F1: 87.35\r\n\r\nI suspected four things:\r\n1. Batch size\r\n Original paper used 128 batch size to train the model, but I trained with small number of batch size due to the insufficient resources. In my training process, I trained my model with 72 batch size.\r\n\r\n2. Learning rate\r\n I adjust a learning rate from 1e-3 to 1e-4 with the AdaFactor optimizer.\r\n\r\n3. Masking for the 'inputs_embeds', 'encoder_outputs', and 'decoder_outputs'\r\n I masked for three things with [Value * (mask == 1).float().unsqueeze(2)].\r\n\r\n4. Loss scale\r\n Originally, a loss is calculated by dividing the number of tokens.\r\n But I changed this to diving the number of batch size.\r\n\r\nAdditionally, I changed pre-processing part to usage an example if an answer is in an extracted document.\r\nHowever, it can be a problem since some of the documents have an answer but it is not reasonable answer.\r\n\r\nA reason to do this, some of spanned answers are a little bit different with original answer. (e.g. answer, != answer)\r\nAnd, some of spanned answers are converted into (UNK) tokens. (I'm not sure it is fixed right thing after changing my pre-process code.)\r\n\r\nI will upload my code on the GitHub as soon as possible.", "Great, happy that you solved it :-) \r\n\r\nI think this will be very useful for others. If you could link your uploaded GitHub code to this issue this would be very helpful :-) ", "I upload my Github, you can see the code in https://github.com/h19920918/T5_SQuAD.\r\n\r\nBut it is quite dirty code..\r\nSo, I recommend which part you watch.\r\nAlmost implementation came from your code.\r\n\r\n1. Mask\r\nhttps://github.com/h19920918/T5_SQuAD/blob/c75d44544c3f18b87a4d8d09ed320742f9aaab36/models/modeling_t5.py#L556\r\nhttps://github.com/h19920918/T5_SQuAD/blob/c75d44544c3f18b87a4d8d09ed320742f9aaab36/models/modeling_t5.py#L659\r\n\r\n2. Loss\r\nhttps://github.com/h19920918/T5_SQuAD/blob/c75d44544c3f18b87a4d8d09ed320742f9aaab36/models/modeling_t5.py#L941\r\nhttps://github.com/h19920918/T5_SQuAD/blob/c75d44544c3f18b87a4d8d09ed320742f9aaab36/models/modeling_t5.py#L943\r\n\r\n3. Pre-processing\r\nhttps://github.com/h19920918/T5_SQuAD/blob/c75d44544c3f18b87a4d8d09ed320742f9aaab36/datasets/squad.py#L101\r\n\r\nAbove links are the modification by me.\r\nWith these modification, my model could be trained. \r\n\r\n\r\nI'm sorry not to provide clean code since I'm working on something in this code..\r\nI hope it will be helpful for someone.\r\n\r\np.s. I have to do ablation study for which part is the real problem.\r\n\r\n@patrickvonplaten I have a question.\r\nAre the T5 checkpoints pre-trained by TensorFlow version or yours?\r\nIt means I want to know whether the checkpoints are converted from somewhere or not.\r\n\r\nI forgot to write something.\r\nThe results from the initial checkpoint are same.\r\nHowever, I don't understand since the 'lm_head' layer should be initialized randomly. (I used different seed for each result.)", "Thanks for linking your code! I think especially the pre-processing code can be very useful for others!\r\n\r\nThe T5 checkpoints are the official Google checkpoints pre-trained by the T5 team: https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints . \r\n\r\nThese checkpoints were attained by pretraining on both the unsupervised C4 dataset (using a denoising objective) and the mixed multi-task supervised dataset (see [paper](https://arxiv.org/abs/1910.10683)). The PyTorch weights were retrieved by conversion from these weights but correspond 1-to-1 to the same values as the original TF weights. \r\n\r\nDoes that make sense? ", "Thank you for your detail answer.\r\n\r\nStill, I don't understand the pre-trained weights output the same results with different seeds.\r\nAs I know, the 'lm_head' layer should be used in the inference process for generating tokens.\r\nHowever, the layer is initialized randomly since it is not in the pre-trained weights.\r\n\r\nI guess one thing about it, where the pre-trained weights dominate all features, therefore, the outputs are same regardless of the 'lm_head' layer.\r\n\r\nIs my inference correct?", "The `lm_head` layer corresponds to the \"inverse\" token embeddings. It is tied to the input embeddings. It should not be randomly initialized when loading weights from the pretrained models.", "Thank you for your answer.\r\n\r\nSorry, it is my mistake." ]

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[ "Proposed a fix that didn't work out-of-the-box with official ALBERT models. Still looking into it, will keep you posted.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Closing since it seems solved in #4076. Thank you!" ]

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[ "Indeed, those notebooks are not up-to-date and should be deprecated.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hi @yagelardan ,\r\n\r\nIn order to fine-tune gpt2 you should be able to use this example [script](https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py)\r\n\r\nAlso, you can refer to this issue https://github.com/huggingface/transformers/issues/1407 where people already trained gpt2 on different languages. \r\nAnd this issue https://github.com/huggingface/transformers/issues/2008 could help you out :-) " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3383?src=pr&el=h1) Report\n> Merging [#3383](https://codecov.io/gh/huggingface/transformers/pull/3383?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/857ccdb259b7e46c60cf86c58b7ab038c63e4d4e&el=desc) will **increase** coverage by `0.29%`.\n> The diff coverage is `88.46%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3383?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3383 +/- ##\n==========================================\n+ Coverage 78.61% 78.90% +0.29% \n==========================================\n Files 106 105 -1 \n Lines 17953 17973 +20 \n==========================================\n+ Hits 14114 14182 +68 \n+ Misses 3839 3791 -48 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3383?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/3383/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `89.01% <75.00%> (+0.60%)` | :arrow_up: |\n| [src/transformers/modeling\\_encoder\\_decoder.py](https://codecov.io/gh/huggingface/transformers/pull/3383/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19lbmNvZGVyX2RlY29kZXIucHk=) | `84.41% <92.30%> (+63.36%)` | :arrow_up: |\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/3383/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `98.98% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3383/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.06% <0.00%> (+0.12%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3383/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `92.76% <0.00%> (+0.16%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3383?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3383?src=pr&el=footer). Last update [857ccdb...83f3d10](https://codecov.io/gh/huggingface/transformers/pull/3383?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "UPDATE:\r\n\r\nI really liked the idea of adding a encoder_decoder config (@thomwolf ) and having for both the encoder_decoder config and model 2 `from_pretrained` fn:\r\n1. The standard one which is used thanks to inheritence to `PretrainedConfig` and `PretrainedModel` \r\n2. a `from_encoder_decoder_pretrained` fn (@sshleifer)\r\n\r\nTo understand how to use the encoder decoder class please confer to the added tests.", "Code is cleaned: added type hints, cleaned the docstring and added a encoder-decoder model page.\r\nJust need to resolve the issue with importing Bert's model tester. @sshleifer found a solution. If everybody is fine with it - I'll go for it :-) ", "LGTM!", "Ok Good to merge for me! If @sshleifer it's ok for you I will use your PR #4027 for the new test proposition.", "Yes!" ]

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[ "We would need a bit more information to understand the issue. A reproducible code example would be even better.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Could you provide all the information related to your environment as the bug template recommends?\r\n\r\nCan you try installing from master? In the latest pypi version, we only handled the `save_pretrained` method, not the `save`/`save_weights` methods. This should have been changed with #3103.", "The changes in #3103 only address serialization of `TF*MainLayer` classes, used within a general Functional/Sequential API Keras model (which was my use case). Looking at [the Network docstring](https://github.com/tensorflow/tensorflow/blob/5c4931bbf69e0f006f210c6382a234e83dd4dc8e/tensorflow/python/keras/engine/network.py#L89-L97) it seems like the `TF*Model` classes, being “subclass models”, need more work in order to support Keras serialization.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Agree and I follow you. ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hi, this is probably because you're using an old version of `transformers`. Version 2.8 doesn't exist, the latest is 2.5.1 ...", "Sorry, I accidently put 2.8. The version I have is: 2.5.1\r\nI am going to edit the post. ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Started getting the same issue today. Is there a known solution?" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3376?src=pr&el=h1) Report\n> Merging [#3376](https://codecov.io/gh/huggingface/transformers/pull/3376?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/cf72479bf11bf7fbc499a518896dfd3cafdd0b21&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3376?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3376 +/- ##\n=======================================\n Coverage 77.55% 77.56% \n=======================================\n Files 100 100 \n Lines 16970 16970 \n=======================================\n+ Hits 13161 13162 +1 \n+ Misses 3809 3808 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3376?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3376/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `84.09% <0.00%> (+0.17%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3376?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3376?src=pr&el=footer). Last update [cf72479...0c19c77](https://codecov.io/gh/huggingface/transformers/pull/3376?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3375?src=pr&el=h1) Report\n> Merging [#3375](https://codecov.io/gh/huggingface/transformers/pull/3375?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/8becb732931bbab5dd75cca5f5e7c75b2516d10b&el=desc) will **increase** coverage by `0.06%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3375?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3375 +/- ##\n==========================================\n+ Coverage 77.64% 77.71% +0.06% \n==========================================\n Files 100 100 \n Lines 16979 16979 \n==========================================\n+ Hits 13184 13195 +11 \n+ Misses 3795 3784 -11 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3375?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3375/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.50% <0.00%> (+0.13%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3375/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.52% <0.00%> (+1.73%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3375?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3375?src=pr&el=footer). Last update [8becb73...74e09c3](https://codecov.io/gh/huggingface/transformers/pull/3375?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Also https://github.com/huggingface/transformers/pull/2249/", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "We converted them from Google's checkpoints." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3371?src=pr&el=h1) Report\n> Merging [#3371](https://codecov.io/gh/huggingface/transformers/pull/3371?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/cf72479bf11bf7fbc499a518896dfd3cafdd0b21&el=desc) will **decrease** coverage by `0.02%`.\n> The diff coverage is `93.75%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3371?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3371 +/- ##\n==========================================\n- Coverage 77.55% 77.52% -0.03% \n==========================================\n Files 100 100 \n Lines 16970 16957 -13 \n==========================================\n- Hits 13161 13146 -15 \n- Misses 3809 3811 +2 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3371?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/3371/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `97.59% <93.75%> (-0.50%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3371?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3371?src=pr&el=footer). Last update [cf72479...8db65c1](https://codecov.io/gh/huggingface/transformers/pull/3371?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Like the change a lot! \r\nOne question I asked myself: With this change the `encoder_outputs` which are the same point to the same memory address -> could that lead to problems? Probably not because the `encoder_outputs` are never changed, right? \r\n\r\nI'd just propose some renaming." ]

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[ "Any update on this?", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3367?src=pr&el=h1) Report\n> Merging [#3367](https://codecov.io/gh/huggingface/transformers/pull/3367?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/ae6834e028ecdf7fdbe886c1f86d0e02d5fef6f0&el=desc) will **increase** coverage by `0.06%`.\n> The diff coverage is `91.30%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3367?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3367 +/- ##\n==========================================\n+ Coverage 77.80% 77.87% +0.06% \n==========================================\n Files 100 100 \n Lines 17064 17127 +63 \n==========================================\n+ Hits 13277 13338 +61 \n- Misses 3787 3789 +2 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3367?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3367/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.98% <87.50%> (+0.17%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3367/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.63% <94.44%> (+0.47%)` | :arrow_up: |\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3367/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `96.99% <100.00%> (+0.02%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3367?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3367?src=pr&el=footer). Last update [ae6834e...19d6acd](https://codecov.io/gh/huggingface/transformers/pull/3367?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Sadly, TF tensorflow test seems flaky see: https://github.com/huggingface/transformers/commit/b38d552a92a0a201c005afae0e1b861ae6de9ce0\r\n\r\nMight need to revert the commit. " ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Not sure why we missed this one. Thanks!" ]

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[ "What do you mean exactly be all possible sentences? \r\n\r\nThe space of possible sentence that could be generated grows exponentially with the length of the sentences. Having **V** words in your vocabulary, there are **V^N** possible sentences of length **N** that could be sampled. If **V ~ 50,000** and **N = 10**, you are already at > 10^40 possibilities which is intractable.", "Thanks for the reply. By all possible sentences I meant all possible sentences under some certain sampling technique. For example, if I want all possible sentences with top-k=1, there would be just 1 in the space. I can control the desired number of possible sentences by choosing sampling techniques at a certain level of strictness. However, I don't want to do a sampling, I want to find all of them by some BFS or DFS. What I plan to do is find some desired sampling technique so that there are say 1 million unique sentences in the space, then find out all of them. The provided util in the package only does sampling which could generate duplicate sentences. Does that make sense?" ]

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[ "I feel its not necessary" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "@bourrel \r\nWhy do you use `jplu/tf-flaubert-base-cased`? (https://huggingface.co/jplu/tf-flaubert-base-cased)\r\nAny particular reason not to use `flaubert/flaubert_base_cased`? (https://huggingface.co/flaubert/flaubert_base_cased)", "It was 2 years ago, I don't remember sorry 😅 " ]

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[ "@mariamabarham didn't you have a similar issue? ", "Yes I encountered the same issue. I solved it by adding --fp16(need to install apex first). You can also reduce the block_size to 512. Both worked out for me.", "You should probably set the `per_gpu_train_batch_size` to 1. That is the default behavior for `gpt-2-simple` to prevent OOM. (I am not a fan of the default batch_size of 4 in `run_language_modeling.py`)", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "You can also try using gradient accumulation steps. \r\nBasically, if you want a batch_size of 32, but your GPU can only fit 16.\r\n\r\nSo you make two passes of 16 batches each, accumulate your gradients, and then do the backward pass after 2 batches.\r\n" ]

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[ "- Item `1) a) i.` is fixed (list of model ids below for reference)\r\n- For models which don't have a tokenizer, or an auto-detected model type, we'll add a notice on their model page (and remove the code sample which is misleading as it lists AutoModel and AutoConfig)\r\n\r\n```\r\nalbert-base\r\nalbert-large\r\nalbert-xlarge\r\nalbert-xxlarge\r\nbert-base-multilingual-cased-finetuned-conll03-dutch\r\nbert-base-multilingual-cased-finetuned-conll03-spanish\r\nmlm-100-1280\r\nmlm-17-1280\r\nbertabs-finetuned-cnndm-extractive-abstractive-summarization\r\nbertabs-finetuned-extractive-abstractive-summarization\r\nbertabs-finetuned-xsum-extractive-abstractive-summarization\r\n```", "## UPDATE: \r\n\r\n### Stats\r\n\r\n1. **61** can't load either their config (n)or their tokenizer:\r\n\r\n - a) **23** models can't load their config file. The reasons for this are as follows:There is an unrecognized `model_type` in the config.json, `e.g.` \r\n> \"Error: Message: Unrecognized model in hfl/rbtl3. Should have a `model_type` key in its config.json, or contain one of the following strings in its name: t5, distilbert, albert, camembert, xlm-roberta, bart, roberta, flaubert, bert, openai-gpt, gpt2, transfo-xl, xlnet, xlm, ctrl\r\n> \"\r\n\r\n - b) **38** models can load their config, but cannot load their tokenizers. The error message is always the same:\r\n\r\n> TOK ERROR: clue/roberta_chinese_base tokenizer can not be loaded\r\n> Message: Model name 'clue/roberta_chinese_base' was not found in tokenizers model name list (roberta-base, roberta-large, roberta-large-mnli, distilroberta-base, roberta-base-openai-detector, roberta-large-openai-detector). We assumed 'clue/roberta_chinese_base' was a path, a model identifier, or url to a directory containing vocabulary files named ['vocab.json', 'merges.txt'] but couldn't find such vocabulary files at this path or url.\r\n\r\n - Here: the model has neither of: \r\n - `vocab_file`\r\n - `added_tokens_file`\r\n - `special_tokens_map_file`\r\n - `tokenizer_config_file`\r\n\r\n2. For **254** models everything is fine! \r\n\r\nHere the full analysis log [here](https://github.com/patrickvonplaten/files_to_link_to/blob/master/results.txt)\r\nHere the code that created this log (simple comparison of loaded tokenizer and config with default config): [here](https://github.com/patrickvonplaten/files_to_link_to/blob/master/test_all_community_models.py)\r\n\r\n## NEXT STEPS\r\n\r\n1a) and 1b) cannot really be fixed by us because for 1a) we don't know which `model_type` is used and for 1b) if the tokenizer does not work or does not exist it should be fixed or uploaded by the author. These **61** models can probably still be used if the correct model class is used instead of `AutoModel.from_pretrained(...)`\r\n\r\nWe could contact the authors or add a `warning` sign to the model page. ", "the problem of denpa92/bert-base-cantonese is not solved.\r\n", "hey @liuchenbaidu , I'd recommend contacting the author of the model in this case.", "When I use ernie model pretained by BaiDu, I had the same problem.\r\nMy solution is to add \"model_type\":\"bert\" to the configuration file, It worked, but I don't know if it's reasonable.", "> When I use ernie model pretained by BaiDu, I had the same problem.\r\n> My solution is to add \"model_type\":\"bert\" to the configuration file, It worked, but I don't know if it's reasonable.\r\n\r\nHi, @XiangQinYu. I'm a bit of a newbie with Huggingface. Can you say more about how you did this? I guess you mean adding \"model_type\":\"bert\" to a file like [this](https://huggingface.co/adamlin/ClinicalBert_all_notes/blob/main/config.json). But how did you edit the file? Did you download the whole model repository, and edit and run it locally?\r\n\r\nEDIT: Nevermind, figured it out with help of a commenter on [a question I asked on SO](https://stackoverflow.com/questions/68682786/is-it-possible-to-use-the-allennlp-semantic-role-labeler-with-bert-large-instead?noredirect=1#comment121759210_68682786)." ]

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[ "@julien-c ", "This was a bogus model file, rm'ed it." ]

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[ "Good question. As far as I can tell (I don't think there's a definitive a.k.a legally tested answer to that question) a model is not considered as a derivative work from the dataset(s) it was trained on, so the person who trained the model can choose whatever licensing option they want.\r\n\r\nFor instance, the original [BERT weights](https://github.com/google-research/bert) mention: \r\n> We will not be able to release the pre-processed datasets used in the paper.\r\n> [...]\r\n> These models are all released under the same license as the source code (Apache 2.0).\r\n\r\nPlease share your findings if you conduct more extensive research.", "Thanks. That's what I assumed as well. Based on the linked you shared, it seems that all BERT models are under the same license. If that's the case for other model architectures, the investigation should be simple. I will look at the ~15 architectures supported and share my findings this week.\r\n\r\nI have a separate question on language support by model, but I will submit it as a separate issue.\r\n\r\nHave a great day,\r\n\r\nAlex", "Hi @julien-c,\r\n\r\nI really liked your suggestion on #3397 to add it to model cards.\r\n\r\nCould I add license information in the same way, using tags on the model card?\r\n\r\nCheers,\r\n\r\nAlex", "Yes @alexcombessie feel free to do some research and open a PR. You can add a `license: x` tag to the metadata, where `x` is an identifier found in https://help.github.com/en/github/creating-cloning-and-archiving-repositories/licensing-a-repository\r\n\r\nA few additional data points:\r\n- Camembert: MIT (source: https://camembert-model.fr/), trained on Oscar (https://traces1.inria.fr/oscar/) whose license is `cc0`\r\n- same for all models from Fairseq: https://github.com/pytorch/fairseq#license", "FYI @MobiusLooper", "The Distil* models trained at Hugging Face are released under Apache 2.0." ]

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[ "For some reference, check out https://github.com/pytorch/pytorch/issues/12042 and https://github.com/facebookresearch/maskrcnn-benchmark/pull/40. These address the same issue.\r\n\r\nAlso, one of the checks that failed, `check_code_quality`, would fail for the existing version of the script as well. There's a check for a line length of 119, and there are already many lines exceeding that.", "I did think about the other scripts. Are those already setup with `DistributedDataParallel` cause one would theorize that those tasks aren't that heavy and wouldn't benefit much from running across multiple GPUs. \r\n\r\nAlso, I have one or 2 more fixes along the lines of this one for distributed training. I was wondering if I should rename this PR and add those in, or create a new one for each of those fixes. One of them is about loading checkpoints (of the optimizer and scheduler) while resuming training.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Closing this as `run_language_modeling.py` is now based on the trainer. Thank you for your contribution!!" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Do you solve the problem?" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3354?src=pr&el=h1) Report\n> Merging [#3354](https://codecov.io/gh/huggingface/transformers/pull/3354?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/3bedfd334763cb5676c2fe92705390ac57d8de5f&el=desc) will **increase** coverage by `0.07%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3354?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3354 +/- ##\n==========================================\n+ Coverage 77.61% 77.68% +0.07% \n==========================================\n Files 100 100 \n Lines 16938 16938 \n==========================================\n+ Hits 13146 13159 +13 \n+ Misses 3792 3779 -13 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3354?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/3354/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `98.92% <ø> (ø)` | |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3354/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.55% <0.00%> (+2.32%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3354?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3354?src=pr&el=footer). Last update [3bedfd3...77a2a4c](https://codecov.io/gh/huggingface/transformers/pull/3354?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "I didn't know that worked, thanks @BramVanroy. @aaugustin what do you think?", "I'm not familiar with the syntax, but if it works, go for it. I really hope we have a release of sort and we can remove this soon.", "Works on my machine so I'll merge :)\r\n\r\nThanks @BramVanroy, this will simplify @LysandreJik and @thomwolf's lives a lot!" ]

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[ "Looks good! [**Model page**](https://huggingface.co/huseinzol05/bert-base-bahasa-cased)\r\n\r\nI've also added a filter for the Malay language here:\r\n<img width=\"792\" alt=\"Screenshot 2020-03-19 at 15 28 42\" src=\"https://user-images.githubusercontent.com/326577/77106980-58beec00-69f6-11ea-8145-4d273d605693.png\">\r\n" ]

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[ "Memory complexity ReformerLayer\r\n vs BertLayer: \r\n\r\n\r\n\r\n", "Time complexity ReformerLayer vs. BertLayer:\r\n\r\n\r\n", "## Experiment\r\n\r\nI tested training the Reformer model on 0.5M tokens per sample on the novel \"Crime and Punishment\" using conventional LM training. I essentially translated the official trax notebook: https://colab.research.google.com/github/google/trax/blob/master/trax/models/reformer/text_generation.ipynb into hugging face code: https://colab.research.google.com/drive/1jR6hA2CQXDbucJXdiDXhmxmyoQmM2Pws\r\n\r\nThe only differences to the official notebook are:\r\n\r\n- The gradient is accumulated over 8 samples and then updated whereas in the official notebook 8 TPUs are used and the gradient is calculated in parallel and then averaged together.\r\n\r\n- The learning rate is 0.005 instead of 0.01 (because already at 0.005, the gradient seems to become too big).\r\n\r\n## Results\r\n\r\nMy training starts similarly around **6.2** and goes down smoothly in the beginning.\r\nAt some point though the gradient seem to explode and the loss goes up again and that even at a learning rate of \"only\" 0.05.\r\n\r\nThe attached plots are here:\r\n\r\n### Loss\r\n\r\n\r\n### Accuracy\r\n\r\n\r\n### Learning rate (cosine scheduler)\r\n\r\n\r\nWhen lowering the learning rate more, to **0.0005** e.g. the loss keeps going down but only reaches something around 2.3 in the end. \r\n\r\n**Comparison**\r\n\r\nThe training in the official trax notebook is very smooth. \r\nLoss starts at **6.2** something and goes down smoothly to **0.8** while the accuracy reaches **>80%** in the end for a learning rate of **0.01**.\r\n\r\n## Analysis\r\n\r\n- It is confirmed that the forward pass is identical with the trax implementation thanks to integration tests. Things that are not fully tested for the backward pass are:\r\n\r\n - **Dropout**: the dropout used in the official trax library does not seem to correspond to the \"usual\" `nn.Dropout` used in PyTorch but sometimes drop specific dimensions only or whole matrices. It is tested though that the dropout used here is deterministic for both the \"normal\" forward pass and the forward pass used in the backward pass to recalculate the activations, by means of setting the random seed used for the first forward pass. Nevertheless, there could still be small bugs.\r\n\r\n - **Reversible Layers**: Because Reformer uses reversible layers, I had to fiddle with a customized backward function here. This is IMO quite prone to errors. I checked multiple times that from a logical point of view everything is correct and compared my code with: https://github.com/RobinBruegger/RevTorch and https://github.com/lucidrains/reformer-pytorch which do similar / the same architecture. IMO, it quite hard to test this for correctness. One could also write the whole code without having reversible layers and then see whether the gradient is the same (Seems actually not like a bad idea to me). \r\n\r\n - **Attention mask**: The official trax code does not seem to use a user-specific attention mask for the LSH Attn Layer, but only for the Local Attn Layer. I tested that the attn mask is correct for the local attention task by integration tests and checked that the attn mask for the LSH layer works correctly (input with mask gives the same result as input without mask), but maybe the LSH Attn mask has to be removed. But don't really see a reason why ?! \r\n\r\n - **Initialization**: The initialization scheme used in the trax library is different from what is normally done in `transformers`, so there are small changes in my code. But I doubt that this is an issue, especially since the training looks very similar in the beginning.\r\n\r\n - **Training parameters**: It might also be simply due to different training / optimization parameters. Maybe there are some under-the-hood training parameters that I didn't notice (special gradient clipping, ...)", "> https://colab.research.google.com/drive/1jR6hA2CQXDbucJXdiDXhmxmyoQmM2Pws\r\n\r\nTried to train model over longer time, but getting [error](http://prntscr.com/s79e04)\r\n\r\n> Forward got unexcepted keyword \"lm_labels\" after calling trainer.train()\r\n\r\nP: Fixed the typo. I will change the model into half-precision soon so that the memory will be sufficient :-) ", "I get some good results with the following parameters: https://gist.github.com/flozi00/b491b41a9865733e5f8bb4032c313540\r\n\r\nthe best eval loss is about 1.654, but is increasing now again the same as yours\r\nwill have a look in a few hours again\r\n\r\n\r\n\r\n", "> I get some good results with the following parameters: https://gist.github.com/flozi00/b491b41a9865733e5f8bb4032c313540\r\n> \r\n> the best eval loss is about 1.654, but is increasing now again the same as yours\r\n> will have a look in a few hours again\r\n> \r\n> \r\n\r\nAwesome that's already much better than what I got! If you manage to get it under 1 (loss) / >75% (accuracy) that would be great. Also feel free to change the hyper-parameters as you wish! Especially the adam betas and co.\r\n\r\nI also added support for fp16 - so the notebook now only needs 8GB of RAM.\r\n\r\n(You might have to reset the environment and re-install the github branch though)", "Sounds very great.\nTrying to decrease sequence length, cause while increasing number of hashes or heads getting memory error.\nTraining on 24GB GPU\n\nRead that 4 hashes are good and 8 brings the best quality.\n\nTrained on some configurations now and everytime the loss goes to ~1 but then increases to 4 very fast and keeps on there for minimum 1000 steps.\nAny idea about it ?", "> Sounds very great.\r\n> Trying to decrease sequence length, cause while increasing number of hashes or heads getting memory error.\r\n> Training on 24GB GPU\r\n> \r\n> Read that 4 hashes are good and 8 brings the best quality.\r\n> \r\n> Trained on some configurations now and everytime the loss goes to ~1 but then increases to 4 very fast and keeps on there for minimum 1000 steps.\r\n> Any idea about it ?\r\n\r\nMy guess is that since it's such a small dataset (0.5M tokens is tiny) the model needs very well-calibrated hyperparameter tuning. When the learning rate is low enough, this actually does not happen anymore but also the loss only gets to about ~2. But I ran very few experiments and didn't do any hyperparameter search.\r\nAlso, I use slightly different dropouts, then were used in the official code so maybe using weight decay instead of dropout could work better. \r\n\r\nWill check that the gradients are correct in the next days and then hopefully be ready soon. ", "@patrickvonplaten I'm excited to see a lot of progress here!\r\n\r\nThe loss curves above could be due to poor hyperparameter choice, but they're also very similar to what you see when the reverse pass of the network doesn't match the forward pass. For example, failing to cache hash bucket assignments (for exact re-use in the backward pass) leads to a failure mode with loss rebounds very similar to the figures you posted above. I also once had a bug where the wrong random seed was used for dropout in the backward pass, which IIRC manifested itself in the same way.", "> @patrickvonplaten I'm excited to see a lot of progress here!\r\n> \r\n> The loss curves above could be due to poor hyperparameter choice, but they're also very similar to what you see when the reverse pass of the network doesn't match the forward pass. For example, failing to cache hash bucket assignments (for exact re-use in the backward pass) leads to a failure mode with loss rebounds very similar to the figures you posted above. I also once had a bug where the wrong random seed was used for dropout in the backward pass, which IIRC manifested itself in the same way.\r\n\r\nThanks for taking a look @nkitaev. I just found a bug in the `backward()`. I now have 1-to-1 the same gradients as your trax code. Will retrain tonight and should get better results :-) ", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3351?src=pr&el=h1) Report\n> Merging [#3351](https://codecov.io/gh/huggingface/transformers/pull/3351?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/7b5bec373ca2b442a7ac8ac46f8eac6e8003e2ae&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3351?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3351 +/- ##\n=======================================\n Coverage 79.13% 79.13% \n=======================================\n Files 117 117 \n Lines 19517 19517 \n=======================================\n Hits 15444 15444 \n Misses 4073 4073 \n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3351?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3351?src=pr&el=footer). Last update [7b5bec3...7b5bec3](https://codecov.io/gh/huggingface/transformers/pull/3351?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Training looks good now on Crime and Punishment. To verify that training works, the model was trained on over 1200 steps and with little regularization.\r\n\r\n### Eval loss \r\n\r\n\r\n\r\n### Eval accuracy\r\n\r\n\r\n", "https://gist.github.com/flozi00/b491b41a9865733e5f8bb4032c313540\r\n\r\nThis gist contains two notebooks, one of them with trainings batch = 2 --> error\r\nin the other I tried to train model with pre-configured parameters, sequence length 4096 --> error\r\n\r\nIs it mistake by me ?", "> https://gist.github.com/flozi00/b491b41a9865733e5f8bb4032c313540\r\n> \r\n> This gist contains two notebooks, one of them with trainings batch = 2 --> error\r\n> in the other I tried to train model with pre-configured parameters, sequence length 4096 --> error\r\n> \r\n> Is it mistake by me ?\r\n\r\nhow did you get past \r\n```\r\n# get a pretrained tokenizer\r\ntokenizer = ReformerTokenizer.from_pretrained(\"patrickvonplaten/reformer-crime-and-punish\")\r\n```", "@lapolonio\r\nI just used the notebook posted by patrickvonplaten four days ago.\r\n\r\nhttps://colab.research.google.com/drive/1jR6hA2CQXDbucJXdiDXhmxmyoQmM2Pws", "The notebook is still under construction, so I would not waste too much time playing around with it at the moment @lapolonio @flozi00. \r\n\r\nThanks a lot for your good comments and remarks @lapolonio and @flozi00 :-) ", "This is looking awesome, thanks! is there plans to add an encoder-decoder version? ", "> This is looking awesome, thanks! is there plans to add an encoder-decoder version?\r\n\r\nYes, this should soon be possible with the encoder-decoder framework", "@patrickvonplaten awesome! is there an issue or a PR I can follow for that? ", "> @patrickvonplaten awesome! is there an issue or a PR I can follow for that?\r\n\r\nNot yet, this will probably still need 1,2 weeks :-) ", "Is it possible or are there any plans to implement reformer for question answering too ?\r\nseq2seq and QA could be very great tasks for it", "> Is it possible or are there any plans to implement reformer for question answering too ?\r\n> seq2seq and QA could be very great tasks for it\r\n\r\nYeah, I will add a cross attention layer in another PR and then the Reformer can be used as a seq-2-seq model with our Encoder-Decoder framework: https://huggingface.co/transformers/model_doc/encoderdecoder.html", "I'm not familiar with the Encoder-Decoder framework after the cross attention layer is added can the decoder be BertForSequenceClassification? Where do I ask questions like this?\r\n", "@patrickvonplaten Based on your merge, it seems like the input size for each batch is fixed in order to match the product of axial position embedding size? I am correct?", "> @patrickvonplaten Based on your merge, it seems like the input size for each batch is fixed in order to match the product of axial position embedding size? I am correct?\r\n\r\nFor training, yes that's correct. For inference the input_size can also be smaller. Also check out: https://huggingface.co/transformers/model_doc/reformer.html", "@patrickvonplaten , I wanted to train a language model for reformers on a custom dataset.\r\nWhat are the steps, and any sample notebooks available for the same", "Hi @prajwal-PHAI, there are a lot of [community notebooks covering T5 finetuning](https://github.com/huggingface/transformers/tree/master/notebooks#community-notebooks).", "Thanks @LysandreJik \r\nI was running into error loading other datasets, which were not there in the nlp library.", "hey. thanks for your amazing work!\r\nI'm running into error while trying the colab example:\r\nhttps://colab.research.google.com/drive/1jR6hA2CQXDbucJXdiDXhmxmyoQmM2Pws#scrollTo=WskGtnXsnWdu\r\n\r\nthe problem is that it doesn't recognize the apex package:\r\n\r\nImportError Traceback (most recent call last)\r\n<ipython-input-29-30584d4c4987> in <module>()\r\n 11 \r\n 12 # train\r\n---> 13 trainer.train()\r\n\r\n/usr/local/lib/python3.6/dist-packages/transformers/trainer.py in train(self, model_path)\r\n 384 if self.args.fp16:\r\n 385 if not is_apex_available():\r\n--> 386 raise ImportError(\"Please install apex from https://www.github.com/nvidia/apex to use fp16 training.\")\r\n 387 model, optimizer = amp.initialize(model, optimizer, opt_level=self.args.fp16_opt_level)\r\n 388 \r\n\r\nImportError: Please install apex from https://www.github.com/nvidia/apex to use fp16 training.\r\n\r\nthough I installed it...anyone know what to do?\r\n", "Linking a related git issue #16972. cc @patrickvonplaten " ]

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