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DanielPFlorian

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Transformers-Github-Issues

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[ "Please [format your code correctly](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks). Now it is very unreadable.", "Thank you for mentioning this, it's done!", "Something seems to have gone wrong. Can you check? There is a line \"and here is my training script:\" that is in the code block but shouldn't be. Also the last line.", "yeah i corrected it", "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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[ "I am having the same problem when finetuning my own language model with run_lm_finetuning.py on camembert Model. I guess this might be related to the fact that it is reading a big file at once (103M, ~500k lines).\r\n Since the code reads whole data at once, it requires so much memory to handle huge corpus.\r\n\r\nThis pull request [2339](https://github.com/huggingface/transformers/pull/2339) is suggesting:\r\n- read corpus by each lines\r\n- flatten 2-dimension array by itertools.chain, it requies less memory and fast\r\nYou can find the code [here](https://github.com/huggingface/transformers/pull/2339/commits/537a1de53d824b5851bce32cb5eafaef3f9ce5ef#diff-713f433a085810c3d63a417486e56a88)\r\n\r\n", "> I am having the same problem when finetuning my own language model with run_lm_finetuning.py on camembert Model. I guess this might be related to the fact that it is reading a big file at once (103M, ~500k lines).\r\n> Since the code reads whole data at once, it requires so much memory to handle huge corpus.\r\n> \r\n> This pull request [2339](https://github.com/huggingface/transformers/pull/2339) is suggesting:\r\n> \r\n> * read corpus by each lines\r\n> * flatten 2-dimension array by itertools.chain, it requies less memory and fast\r\n> You can find the code [here](https://github.com/huggingface/transformers/pull/2339/commits/537a1de53d824b5851bce32cb5eafaef3f9ce5ef#diff-713f433a085810c3d63a417486e56a88)\r\n\r\n@HendZouari this is interesting.\r\nI thought the same thing, but I'm not sure the reason is the size of data, since I verified that with multlingual BERT, it works. \r\n", "Can you guys try out the recently-merged-to-master `LineByLineTextDataset`, defined at\r\nhttps://github.com/huggingface/transformers/blob/master/examples/run_lm_finetuning.py#L124 ?\r\n\r\nYou can use it by adding the `--line_by_line` flag to your command. (btw: tokenization will soon be _way_ faster everywhere in the library as we are rolling out `tokenizers` integration everywhere)", "For large datasets that don't fit in memory, I use a lazy dataset, modified from [this Github post](https://github.com/pytorch/text/issues/130#issuecomment-333306652).\r\n\r\n```python\r\nclass LazyTextDataset(Dataset):\r\n def __init__(self, fin):\r\n # get absolute path\r\n # convert to str, linecache doesn't accept Path objects\r\n self.fin = str(Path(fin).resolve())\r\n self.num_entries = self._get_n_lines(self.fin)\r\n\r\n @staticmethod\r\n def _get_n_lines(fin):\r\n with open(fin, encoding='utf-8') as fhin:\r\n for line_idx, _ in enumerate(fhin, 1):\r\n pass\r\n\r\n return line_idx\r\n\r\n def __getitem__(self, idx):\r\n # linecache starts counting from one, not zero, +1 the given index\r\n return linecache.getline(self.fin, idx+1)\r\n\r\n def __len__(self):\r\n return self.num_entries\r\n```\r\n\r\nWith a bit of work you can modify this to return the tokenized strings. I would advise you to write a custom collate_fn for the dataloader, which can be parallellized by using the `n_workers` argument.\r\n\r\nSomething like this (untested)\r\n\r\n```python\r\n from torch.utils.data.dataloader import default_collate\r\n\r\n def collate(data):\r\n data = default_collate(data)\r\n return tokenizer.encode(data)\r\n```\r\n\r\nThat being said, it might be easier to just use wait a bit until `tokenizers` is implemented everywhere, as @julien-c mentions.", "@julien-c it's woking now! The flag `--line_by_line` was fundamental for me 🤩.\r\nThanks also to @HendZouari and @BramVanroy: I guess If I had followed your advices, the program would have worked well in the same way :)", "I am having this issue while using --line_by_line flag\r\n```\r\nTraceback (most recent call last):\r\n File \"run_lm_finetuning.py\", line 785, in <module>\r\n main()\r\n File \"run_lm_finetuning.py\", line 730, in main\r\n train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)\r\n File \"run_lm_finetuning.py\", line 147, in load_and_cache_examples\r\n return LineByLineTextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size)\r\n File \"run_lm_finetuning.py\", line 135, in __init__\r\n self.examples = tokenizer.batch_encode_plus(lines, max_length=block_size)[\"input_ids\"]\r\nAttributeError: 'BertTokenizer' object has no attribute 'batch_encode_plus'\r\n```\r\n\r\nWithout this flag I run into another loss function related error for which I just opened an issue.", "`AttributeError: 'BertTokenizer' object has no attribute 'batch_encode_plus'`\r\n\r\nYou need to update `transformers`. `batch_encode_plus` was only introduced recently.", "Yes. Upgraded and now working. Thank you.", "@paulthemagno Can you close this? Thanks ", "I have reopened the issue for a stange RuntimeError.\r\nIn the middle of the training (after several hours in which it was working with no problem), it crashes with this log:\r\n```\r\nFile \"finetuning.py\", line 801, in <module>51:22, 4.94it/s]\r\n main()\r\n File \"finetuning.py\", line 750, in main\r\n global_step, tr_loss = train(args, train_dataset, model, tokenizer)\r\n File \"finetuning.py\", line 342, in train\r\n inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)\r\n File \"finetuning.py\", line 222, in mask_tokens\r\n inputs[indices_random] = random_words[indices_random]\r\nRuntimeError: expected dtype Float but got dtype Long\r\nEpoch: 55%|█████▍ | 6/11 [20:26:33<17:02:07, 12265.60s/it]\r\nIteration: 69%|██████▊ | 33378/48603 [1:47:45<49:09, 5.16it/s]\r\n```\r\n\r\nIt fails in the _mask_tokens()_ function:\r\n```python\r\n# 10% of the time, we replace masked input tokens with random word\r\n indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced\r\n random_words = torch.randint(len(tokenizer), labels.shape, dtype=torch.long)\r\n inputs[indices_random] = random_words[indices_random]\r\n```\r\n\r\nShould I set `dtype=torch.float`? Why does It work fine for so much time and it suddenly gives this error at the 6th epoch on 11? ", "I have the same issue, but to me it fails immediately.\r\n```\r\n File \"run_lm_finetuning.py\", line 340, in train\r\n inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)\r\n File \"run_lm_finetuning.py\", line 218, in mask_tokens\r\n inputs[indices_random] = random_words[indices_random]\r\nRuntimeError: expected dtype Float but got dtype Long\r\n```\r\nIt is triggered by using `--line_by_line`.", "I have the same issue as @paulthemagno 's. Mine fails while fine-tuning a basic uncased bert model using the new --line_by_line flag about a quarter way through the epoch. \r\n\r\n```Traceback (most recent call last):███████████████████▋ | 2184/8205 [06:29<18:54, 5.31it/s]\r\n\r\n File \"run_lm_finetuning.py\", line 692, in <module>\r\n main()\r\n File \"run_lm_finetuning.py\", line 641, in main\r\n global_step, tr_loss = train(args, train_dataset, model, tokenizer)\r\n File \"run_lm_finetuning.py\", line 320, in train\r\n inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)\r\n File \"run_lm_finetuning.py\", line 193, in mask_tokens\r\n inputs[indices_random] = random_words[indices_random]\r\nRuntimeError: expected dtype Float but got dtype Long\r\nEpoch: 0%| | 0/1 [06:29<?, ?it/s]\r\nIteration: 27%|████████████████████████████████████▋ \r\n\r\n2184/8205 [06:29<17:54, 5.61it/s]```", "I tried to set `dtype=torch.float` but it fails immediately after the launch.\r\nThe only way I found is to restart from the last saved checkpoint rather than from the original language model. If someone knew how to fix it, I'd appreciate it.", "Can you guys open a new issue for this? (w/ PyTorch version + ideally a small reproduction case)", "> Can you guys open a new issue for this? (w/ PyTorch version + ideally a small reproduction case)\r\n\r\nYes, done now #2728" ]

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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. Many people are reporting unstable results or just unexpected results. You can search for issues in this library, and even in other ones (e.g. https://github.com/deepset-ai/FARM/issues/202#issuecomment-577077201). It seems that ALBERT is very sensitive to hyperparameters and even then... For now there seems to be no solution. It is probably best to stick to another model. I'd recommend RoBERTa but it depends on your use-case.", "@BramVanroy I tried with roberta-base as well, the token level similarity is coming out very bad. \r\nSmoking is getting matched with software.", "Can you share a repo to your full code?", "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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[ "Though the core of the library is Python 3.5+, the CLI is actually Python3.6+ as for instance the serving subcommand uses FastAPI which is Py36+ only (cc @mfuntowicz)\r\n\r\nDo we have a way to make that clear in the doc @LysandreJik?", "Personal opinion: supporting 3.6+ only seems realistic and may make maintenance easier. [AllenNLP](https://github.com/allenai/allennlp) also requires 3.6.1+. Of course I don't know how large the 3.5 user-base is for `transformers` so it might be worth maintaining. That being said, if there are plans to stop support for 3.5, it might be a good idea to announce this in a release (\"last supported release for 3.5\"). ", "@BramVanroy According to the PyPI stats at https://pypistats.org/packages/transformers (look for the `Daily Download Proportions of transformers package - Python Minor` graph) around 1% of _pip installs_ are on Python 3.5.", "> @BramVanroy According to the PyPI stats at https://pypistats.org/packages/transformers (look for the `Daily Download Proportions of transformers package - Python Minor` graph) around 1% of _pip installs_ are on Python 3.5.\r\n\r\nAh, I didn't know this website - thanks! I'm not sure if 1% is worth the effort, then again I don't know how much additional effort (and resources for CI) are needed to maintain for 3.5 anyway. (But 3.6 has f-strings and ordered dicts (officially in 3.7), PathLike, better `typing`, sooo... :D)\r\n\r\nPS: I wonder what happened on December 24 or thereabouts, with the spike in 3.5 installations. ", "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 the lib is now officially Python 3.6+" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2607?src=pr&el=h1) Report\n> Merging [#2607](https://codecov.io/gh/huggingface/transformers/pull/2607?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/1a8e87be4e2a1b551175bd6f0f749f3d2289010f?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2607?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2607 +/- ##\n=======================================\n Coverage 74.53% 74.53% \n=======================================\n Files 87 87 \n Lines 14819 14819 \n=======================================\n Hits 11046 11046 \n Misses 3773 3773\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/2607?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/2607/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `81.09% <ø> (ø)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2607?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/2607?src=pr&el=footer). Last update [1a8e87b...980d1f8](https://codecov.io/gh/huggingface/transformers/pull/2607?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Hi! Indeed, we changed all the Cross Entropy ignored indices to be -100 to respect the PyTorch default. The docstrings would need to be changed, instead of the index. We could even remove the argument:\r\n\r\n```py\r\nloss_fct = CrossEntropyLoss()\r\n```\r\n\r\nto keep in sync with pytorch.", "@LysandreJik, that makes perfect sense. Thanks for the background!\r\n\r\nI've updated this PR to change the doc string instead. \r\n\r\nRemoving the `ignore_index` argument would mean that the behavior of the method depends on which PyTorch version a user has installed, which could be counter-intuitive. Similarly, to ensure the doc string is accurate we'd have to add a unit test for it and run against all versions of PyTorch that a user might reasonably install.\r\n\r\nFor simplicity / consistency, I'd suggest continuing to explicitly pass the argument.\r\n\r\nI'm happy to add a commit removing it though, if you feel otherwise.", "Thanks for updating, we can keep the `ignore_index` argument.\r\n\r\nThanks @nalourie-ai2 !" ]

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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", "This was completed but forgot to close the issue." ]

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[ "Hello! Indeed, you can't access the dictionary by using `example.label`. Are you passing your `tf.data.Dataset` as the `examples` argument to the `glue_convert_examples_to_features`?", "yes, train_data is the examples. and the spec is: \r\n\r\n{'idx': TensorSpec(shape=(), dtype=tf.string, name=None),\r\n'sentence1': TensorSpec(shape=(), dtype=tf.string, name=None),\r\n'sentence2': TensorSpec(shape=(), dtype=tf.string, name=None),\r\n'label': TensorSpec(shape=(), dtype=tf.int32, name=None)}\r\n\r\nshouldn't it look like this? \r\n", "I think the code needs to change to example[\"label\"]. ", "Here's the format of my data that is passed to glue_convert_examples_to_features():\r\n\r\n{'idx': <tf.Tensor: shape=(), dtype=string, numpy=b'TEXT'>, 'sentence1': <tf.Tensor: shape=(), dtype=string, numpy=b\"TEXT TEXT TEXT\">, 'sentence2': <tf.Tensor: shape=(), dtype=string, numpy=b'text'>, 'label': <tf.Tensor: shape=(), dtype=int32, numpy=1>}\r\n", "Do you mind letting me know on which version of transformers you're running your code?\r\n\r\nFor a couple of versions now we handle `tf.data.Dataset` using our `get_example_from_tensor_dict` method, as can be seen [here](https://github.com/huggingface/transformers/blob/master/src/transformers/data/processors/glue.py#L85). This transforms your dictionary into an `InputExample`. \r\n\r\nIt should have been able to convert your dataset as well.", "yes, I use the get_example_from_tensor_dict(). the problem is, even though I pass my label_list=[numpy.int64(1), numpy.int64(0)], and my data type is int32: 'label': <tf.Tensor: shape=(), dtype=int32, numpy=1>, \r\n\r\nwhen glue.py tries to run label = label_map[example.label], the type of example.label is <class: str>! which should not be, and should be int! \r\n\r\nWhat I did, was to clone your repo on my local device, changed that line to label = label_map[int(example.label)], and it works fine now! \r\n\r\nI am guessing when you create the label_map dictionary, the keys are str, but needs to be int. \r\n", "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 Ben,\r\n\r\nUnless I misunderstand what you're trying to do, this is not really what `save_pretrained()` and `from_pretrained()` are made for.\r\n\r\n`save_pretrained()` lets you save a tokenizer or a model _locally_, inside a local folder. (you can then upload those files to your own s3 bucket, or use the `transformers-cli` to upload to our bucket). \r\n\r\n`from_pretrained()` lets you re-spawn a model or tokenizer from either a local folder, a model shortcut (hardcoded in the library's code), or a community model identifier (which resolves to files on our S3 bucket)\r\n\r\nLet me know if things are clearer\r\n", "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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[ "Indeed, this is an error ! Thanks for letting us know, it was patched with 088fa7b!", "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, please check the [contribution guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests) for the code quality tests to pass.\r\n\r\nWhy did you edit this, does this solve a bug or add new functionality?", "Hi i edited this because i read a comment like..\r\n“ # Should find a better way to do this “\r\nNot because of bug or new features", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2602?src=pr&el=h1) Report\n> Merging [#2602](https://codecov.io/gh/huggingface/transformers/pull/2602?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/23c6998bf46e43092fc59543ea7795074a720f08?src=pr&el=desc) will **increase** coverage by `<.01%`.\n> The diff coverage is `100%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2602?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2602 +/- ##\n==========================================\n+ Coverage 74.61% 74.61% +<.01% \n==========================================\n Files 87 87 \n Lines 14802 14804 +2 \n==========================================\n+ Hits 11044 11046 +2 \n Misses 3758 3758\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/2602?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/2602/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hbGJlcnQucHk=) | `79.03% <100%> (+0.11%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2602?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/2602?src=pr&el=footer). Last update [23c6998...a96e39e](https://codecov.io/gh/huggingface/transformers/pull/2602?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\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", "I am facing the same issue. Probably `PreTrainedEncoderDecoder` supports only `Bert` to `Bert` models.", "I'm getting this error too\r\n```\r\nConverting vae...\r\nConverting text encoder...\r\nDownloading100% 1.71G/1.71G [00:24<00:00, 69.1MB/s]\r\nDownloading100% 4.55k/4.55k [00:00<00:00, 746kB/s]\r\nDownloading100% 1.22G/1.22G [00:18<00:00, 66.3MB/s]\r\nDownloading100% 342/342 [00:00<00:00, 57.0kB/s]\r\nSaving diffusion model...\r\nRestored system models.\r\nCheckpoint successfully extracted to /content/gdrive/MyDrive/sd/stable-diffusion-webui/models/dreambooth/TTFM/working\r\nReturning ['default', True, True, 1, '', '', 0, 0, 1, True, True, 50, False, False, 5e-06, 1e-06, 0.0001, '', 5e-05, 1.0, 1.0, 1, 0.5, 1.0, 0.5, 'constant_with_warmup', 0, 75, 'fp16', 100, True, '', 1.0, 512, 1, '', 420420, True, False, True, 25, True, False, True, 5, False, False, False, False, 1, False, 1.0, True, False, True, False, False, '', 7.5, 60, '', '', '', '', '', '', 1, 0, 0, -1, 7.5, 60, '', '', '', '', 7.5, 60, '', '', '', '', '', '', 1, 0, 0, -1, 7.5, 60, '', '', '', '', 7.5, 60, '', '', '', '', '', '', 1, 0, 0, -1, 7.5, 60, '', '', '', 'Loaded config.']\r\nSaved settings.\r\nCustom model name is TTFM\r\nStarting Dreambooth training...\r\nInitializing dreambooth training...\r\nReplace CrossAttention.forward to use default\r\nInstance Bucket 0: Resolution (512, 512), Count: 723\r\nTarget Bucket 0: Resolution (512, 512), Count: 0\r\nWe need a total of 0 images.\r\nNothing to generate.\r\nException importing 8bit adam: No module named 'bitsandbytes'\r\nWARNING:extensions.sd_dreambooth_extension.dreambooth.train_dreambooth:Exception importing 8bit adam: No module named 'bitsandbytes'\r\nTraceback (most recent call last):\r\n File \"/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions/sd_dreambooth_extension/dreambooth/train_dreambooth.py\", line 304, in inner_loop\r\n import bitsandbytes as bnb\r\nModuleNotFoundError: No module named 'bitsandbytes'\r\nPreparing dataset\r\nPreparing dataset\r\nPreparing Dataset (With Caching)\r\n100% 723/723 [00:46<00:00, 15.47it/s]\r\nTrain Bucket 1: Resolution (512, 512), Count: 723\r\nTotal images: 241\r\nTotal dataset length (steps): 241\r\nSched breakpoint is 108450\r\n ***** Running training *****\r\n Instance Images: 723\r\n Class Images: 0\r\n Total Examples: 723\r\n Num batches each epoch = 241\r\n Num Epochs = 300\r\n Batch Size Per Device = 3\r\n Gradient Accumulation steps = 3\r\n Total train batch size (w. parallel, distributed & accumulation) = 9\r\n Text Encoder Epochs: 210\r\n Total optimization steps = 216900\r\n Total training steps = 216900\r\n Resuming from checkpoint: False\r\n First resume epoch: 0\r\n First resume step: 0\r\n Lora: False, Adam: False, Prec: bf16\r\n Gradient Checkpointing: True\r\n EMA: True\r\n LR: 4.5e-05)\r\nSteps: 0% 0/216900 [00:00<?, ?it/s]Traceback (most recent call last):\r\n File \"/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions/sd_dreambooth_extension/scripts/dreambooth.py\", line 561, in start_training\r\n result = main(config, use_txt2img=use_txt2img)\r\n File \"/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions/sd_dreambooth_extension/dreambooth/train_dreambooth.py\", line 973, in main\r\n return inner_loop()\r\n File \"/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions/sd_dreambooth_extension/dreambooth/memory.py\", line 116, in decorator\r\n return function(batch_size, grad_size, prof, *args, **kwargs)\r\n File \"/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions/sd_dreambooth_extension/dreambooth/train_dreambooth.py\", line 829, in inner_loop\r\n noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample\r\n File \"/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\r\n return forward_call(*input, **kwargs)\r\n File \"/usr/local/lib/python3.8/dist-packages/accelerate/utils/operations.py\", line 490, in __call__\r\n return convert_to_fp32(self.model_forward(*args, **kwargs))\r\n File \"/usr/local/lib/python3.8/dist-packages/torch/amp/autocast_mode.py\", line 14, in decorate_autocast\r\n return func(*args, **kwargs)\r\n File \"/usr/local/lib/python3.8/dist-packages/diffusers/models/unet_2d_condition.py\", line 481, in forward\r\n sample, res_samples = downsample_block(\r\n File \"/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\r\n return forward_call(*input, **kwargs)\r\n File \"/usr/local/lib/python3.8/dist-packages/diffusers/models/unet_2d_blocks.py\", line 781, in forward\r\n hidden_states = torch.utils.checkpoint.checkpoint(\r\n File \"/usr/local/lib/python3.8/dist-packages/torch/utils/checkpoint.py\", line 249, in checkpoint\r\n return CheckpointFunction.apply(function, preserve, *args)\r\n File \"/usr/local/lib/python3.8/dist-packages/torch/utils/checkpoint.py\", line 107, in forward\r\n outputs = run_function(*args)\r\n File \"/usr/local/lib/python3.8/dist-packages/diffusers/models/unet_2d_blocks.py\", line 774, in custom_forward\r\n return module(*inputs, return_dict=return_dict)\r\n File \"/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\r\n return forward_call(*input, **kwargs)\r\n File \"/usr/local/lib/python3.8/dist-packages/diffusers/models/transformer_2d.py\", line 265, in forward\r\n hidden_states = block(\r\n File \"/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\r\n return forward_call(*input, **kwargs)\r\n File \"/usr/local/lib/python3.8/dist-packages/diffusers/models/attention.py\", line 285, in forward\r\n attn_output = self.attn1(\r\n File \"/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\r\n return forward_call(*input, **kwargs)\r\nTypeError: forward_default() got an unexpected keyword argument 'encoder_hidden_states'\r\nSteps: 0% 0/216900 [00:01<?, ?it/s]\r\nTraining completed, reloading SD Model.\r\nRestored system models.\r\nReturning result: Exception training model: 'forward_default() got an unexpected keyword argument 'encoder_hidden_states''.\r\n```" ]

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[ "Hi! I believe this issue could stem from your label being negative as well. Could you check that it doesn't fail when computing the loss with a negative label?", "I'm not sure what is a negative label in the context of multiple-choice, but here's what I did: it successfully computed loss when the label is 0 (which should be representing the first option?) but it fails for 1, 2, and -1.", "@KerenzaDoxolodeo Hello,I am also trying to finetune RACE on bert model. I am wondering if you had fixed this problem.\r\nAlso can you post your fine tuning command with exact hyperparameters? Thanks.", "@KerenzaDoxolodeo I ran into a similar problem. You are right in that the error is due to a shape mismatch. However you don't need to change the config file. Instead adapt your processor class by changing the context parameter of your InputExample from\r\n`contexts=[illogicalAnswer]` \r\nto\r\n`contexts=[illogicalAnswer, illogicalAnswer, illogicalAnswer]`\r\n\r\nIf you look at the original SwagProcessor, they copied the context several times such that both the context as well as the endings are lists of size num_labels.\r\n\r\nUnfortunately, the code does not raise an error if you ignore this requirement. If you look closely at what happens within the convert_examples_to_features() method in examples/utils_multiple_choice.py, you'll notice the line\r\n`enumerate(zip(example.contexts, example.endings))`\r\nThis is where everything breaks. Since you only provided a single context per example, this line will lead to also only taking into account a single ending from example.endings.\r\n\r\nYou don't need to adapt the config file as it will adapt to the number of labels automatically. If, for some reason, you still like to change the config, I think you should not manually overwrite the num_labels parameter as this is likely to introduce further errors (like in the config you showed above). Instead load the config from pretrained and provide the number of labels, e.g. like this\r\n`config = BertConfig.from_pretrained(\r\n 'bert-base-uncased',\r\n num_labels=5,\r\n)`\r\nThis will also change the mappings \"id2label\" as well as \"label2id\" appropriately (both are not set properly in your posted example).", "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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[ "NM, I was able to solve it by changing some of the hyperparameter values.", "Hi, glad you could make it work! Do you mind sharing what hyperparameter values you tuned in order to make it work?", "> Hi, glad you could make it work! Do you mind sharing what hyperparameter values you tuned in order to make it work?\r\n\r\nHi, have you solved the problem? I used bert-base-cased, roberta-base, and xlnet-base-cased to predict CoLA test.tsv, and I got 51.8, 55.6 and 24.7 respectively, I don't know why xlnet got such low Matthew's Corr. Can you help me? thx " ]

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[ "I am facing the same issue, how did you resolve this? @bestpredicts ", "Same issue with portuguese bert version\r\n", "I had the same issue and found that this problem occurs because the default \"RobertaConfig\" is based on \"bert-base-uncased\" config, which is different from \"roberta-base\" config. The right way to initialize the model and configuration is (LysandreJik's solution):\r\n\r\n```python\r\nconfig = RobertaConfig.from_pretrained(\"roberta-base\", output_hidden_states=True)\r\nmodel = RobertaForSequenceClassification.from_pretrained(\"roberta-base\", config=config)\r\n```\r\n\r\nPlease refer to the similar issue:\r\n[#1627](https://github.com/huggingface/transformers/issues/1627)", "This is annoying, one doesnt have to do this while setting config for TFDistilBert\r\n\r\nhttps://towardsdatascience.com/working-with-hugging-face-transformers-and-tf-2-0-89bf35e3555a" ]

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[ "@imayachita If your succeeded to use transfer learning on your own data, please update here.", "`examples/summarization/bart/run_bart_sum.py` now exists :)" ]

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[ "I got it working" ]

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[ "To add more, I fixed my code like below:\r\n\r\n```python\r\n# for loop to calculate TVD\r\ndef TVD_loop(test_i, test_dummy_i, nlayer, best_model):\r\n \r\n TVD_tensor = torch.zeros(test_i.size()[1], (nlayer+1), test_i.size()[0]).float()\r\n \r\n # replace every 0's in TVD_tensor to -2\r\n TVD_tensor = torch.where(TVD_tensor == 0.0, torch.tensor(-2.0), TVD_tensor)\r\n \r\n for m in range(test_i.size()[1]):\r\n \r\n gc.collect()\r\n \r\n input_ids = test_i[:,m]\r\n input_ids = torch.tensor(input_ids.tolist()).unsqueeze(0) \r\n\r\n # NOTE: Hidden states are in torch.FloatTensor,\r\n # (one for the output of each layer + the output of the embeddings)\r\n # jth layer\r\n for j in range(nlayer+1):\r\n \r\n del gc.garbage[:]\r\n gc.collect()\r\n \r\n for l in range(m * test_i.size()[0], (m+1) * test_i.size()[0]):\r\n \r\n del gc.garbage[:]\r\n gc.collect()\r\n \r\n tst_hidden_states = best_model(input_ids)[3][j][0, (test_i.size()[0] - 1), :]\r\n \r\n input_ids_dummy = test_dummy_i[:,l]\r\n input_ids_dummy = torch.tensor(input_ids_dummy.tolist()).unsqueeze(0) \r\n \r\n tst_hidden_states_dummy = best_model(input_ids_dummy)[3][j][0, (test_i.size()[0] - 1), :]\r\n \r\n del input_ids_dummy\r\n del gc.garbage[:]\r\n gc.collect()\r\n \r\n # TVD_tensor[i,j,k] denotes for TVD calculated at \r\n # batch i, layer j, and dummy output k\r\n TVD_tensor[m,j,(l % (test_i.size()[0]))] = TVD(tst_hidden_states, tst_hidden_states_dummy)\r\n \r\n del tst_hidden_states\r\n del tst_hidden_states_dummy\r\n del gc.garbage[:]\r\n gc.collect()\r\n \r\n print('l={}, gc_get_count={}'.format(l,gc.get_count()))\r\n \r\n del gc.garbage[:]\r\n gc.collect()\r\n print('j={}, gc_get_count={}'.format(j,gc.get_count()))\r\n \r\n del gc.garbage[:]\r\n del input_ids\r\n gc.collect()\r\n \r\n print('m={}, gc_get_count={}'.format(m,gc.get_count()))\r\n \r\n return TVD_tensor \r\n```\r\n\r\nfrom the code above, when ```m=0, j=0, l=0```, everything is fine, but once ```m=0, j=1, l=0``` is reached, the memory usage starts to accumulate rapidly. How should I fix my code?", "Did you try detaching the gradient from the hidden states by replacing \r\n`\r\ntst_hidden_states = best_model(input_ids)[3][j][0, (test_i.size()[0] - 1), :]\r\n`\r\nwith\r\n`\r\ntst_hidden_states = best_model(input_ids)[3][j][0, (test_i.size()[0] - 1), :].detach()\r\n`\r\n?\r\nThe gradient needed for backpropagation usually consumes a lot of RAM and is probably not needed in your case.\r\n\r\n> ", "> Did you try detaching the gradient from the hidden states by replacing\r\n> `tst_hidden_states = best_model(input_ids)[3][j][0, (test_i.size()[0] - 1), :]`\r\n> with\r\n> `tst_hidden_states = best_model(input_ids)[3][j][0, (test_i.size()[0] - 1), :].detach()`\r\n> ?\r\n> The gradient needed for backpropagation usually consumes a lot of RAM and is probably not needed in your case.\r\n> \r\n> >\r\n\r\nDetaching _before_ the slice might be even better? Not sure.", "Thank you! .detach() solved this RAM leakage issue :)" ]

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[ "will commit with another pull request" ]

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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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[ "What is the code that you are executing that leads to this error? ", "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", "How did you fix the problem?" ]

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[ "Your result is fine. This [poster](https://web.stanford.edu/class/cs224n/posters/15848021.pdf) says that they achieved **76.70**. Maybe you can get there as well when you train for 2 more epochs.", "Thank you for your reply! :)", "Please close the question if the answer suits your needs.", "Sorry, I closed it!" ]

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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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[ "I also use --save_total_limit=10 which triggered another exception in checkpoint deletion code as it tried to delete the cache file like it was a folder\r\n\r\n```\r\nDeleting older checkpoint [./output\\checkpoint-200_cached_lm_512_dataset.txt] due to args.save_total_limit\r\nTraceback (most recent call last):\r\n File \".\\transformers\\examples\\run_lm_finetuning.py\", line 721, in <module>\r\n main()\r\n File \".\\transformers\\examples\\run_lm_finetuning.py\", line 671, in main\r\n global_step, tr_loss = train(args, train_dataset, model, tokenizer)\r\n File \".\\transformers\\examples\\run_lm_finetuning.py\", line 360, in train\r\n _rotate_checkpoints(args, checkpoint_prefix)\r\n File \".\\transformers\\examples\\run_lm_finetuning.py\", line 169, in _rotate_checkpoints\r\n shutil.rmtree(checkpoint)\r\n File \"...\\lib\\shutil.py\", line 500, in rmtree\r\n return _rmtree_unsafe(path, onerror)\r\n File \"...\\lib\\shutil.py\", line 382, in _rmtree_unsafe\r\n onerror(os.listdir, path, sys.exc_info())\r\n File \"...\\lib\\shutil.py\", line 380, in _rmtree_unsafe\r\n names = os.listdir(path)\r\nNotADirectoryError: [WinError 267] The directory name is invalid: './output\\\\checkpoint-200_cached_lm_512_dataset.txt'\r\n```", "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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[ "- xlnet-base-cased : https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-config.json\r\n- xlnet-large-cased : https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-config.json\r\n\r\nI got it from [here](https://huggingface.co/transformers/_modules/transformers/configuration_xlnet.html#XLNetConfig) \r\nGo to the source of that model in hugging face repo and there you can find the links set in config.", "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 used [this link ](https://huggingface.co/jplu/tf-xlm-roberta-large#tensorflow-xlm-roberta)to download the tf-xlm-roberta-base. But such information is not available for every model.\r\n\r\nAs @nauman-chaudhary indicated some are available [here](https://huggingface.co/transformers/_modules/transformers/configuration_xlnet.html#XLNetConfig).." ]

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[ "Are you sure that you are using the right models and not just `BertModel`? You also have to change the tokenizer completely.\r\n\r\nInstead of something like\r\n\r\n```python\r\nmodel = BertModel.from_pretrained('bert-base-uncased')\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n```\r\n\r\nYou should use something like this:\r\n\r\n```python\r\nmodel = XLNetModel.from_pretrained('xlnet-base-cased')\r\ntokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')\r\n```\r\n\r\nFor tokenisation, I recommend that you use the `encode` method so that special tokens are added correctly and automatically.\r\n\r\n```python\r\ninput_ids = tokenizer.encode(text, return_tensors='pt')\r\n```\r\n\r\nIt is normal that not all models behave the same way. You can't just use the same hyperparameters and expect the same or bettter results. Try longer finetuning, other learning rate, stuff like that.\r\n\r\nFinally, you may wish to update your PyTorch version if possible. We're already at 1.4.\r\n\r\n", "> \r\n> \r\n> Are you sure that you are using the right models and not just `BertModel`? You also have to change the tokenizer completely.\r\n> \r\n> Instead of something like\r\n> \r\n> ```python\r\n> model = BertModel.from_pretrained('bert-base-uncased')\r\n> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n> ```\r\n> \r\n> You should use something like this:\r\n> \r\n> ```python\r\n> model = XLNetModel.from_pretrained('xlnet-base-cased')\r\n> tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')\r\n> ```\r\n> \r\n> For tokenisation, I recommend that you use the `encode` method so that special tokens are added correctly and automatically.\r\n> \r\n> ```python\r\n> input_ids = tokenizer.encode(text, return_tensors='pt')\r\n> ```\r\n> \r\n> It is normal that not all models behave the same way. You can't just use the same hyperparameters and expect the same or bettter results. Try longer finetuning, other learning rate, stuff like that.\r\n> \r\n> Finally, you may wish to update your PyTorch version if possible. We're already at 1.4.\r\n\r\nYes, I have made these modifications, but the acc is still low. After training for an epoch, it is 28% still.\r\nI'm using:\r\n```\r\n MODEL_CLASSES = {\"bert\": (BertConfig, BertForMultipleChoice, BertTokenizer),\"xlnet\": (XLNetConfig, XLNetForMultipleChoice, XLNetTokenizer),\"roberta\": (RobertaConfig, RobertaForMultipleChoice, RobertaTokenizer)}\r\n ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLNetConfig, RobertaConfig)), ())\r\n config_class, model_class, tokenizer_class = MODEL_CLASSES[\"xlnet\"]\r\n transformer_tokenizer = tokenizer_class.from_pretrained(\"xlnet-base-cased\", do_lower_case=True, cache_dir=None)\r\n```\r\n\r\nand\r\n\r\n```\r\n text_a=context_tokens_choice\r\n text_b=ending_tokens\r\n inputs=transformer_tokenizer.encode_plus(text_a, text_b, add_special_tokens=True, max_length=max_seq_length)\r\n input_ids, token_type_ids = inputs[\"input_ids\"], inputs[\"token_type_ids\"]\r\n attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\r\n padding_length = max_seq_length - len(input_ids)\r\n if pad_on_left:\r\n input_ids = ([pad_token] * padding_length) + input_ids\r\n segment_ids = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask\r\n input_mask = ([pad_token_segment_id] * padding_length) + token_type_ids\r\n else:\r\n input_ids = input_ids + ([pad_token] * padding_length)\r\n segment_ids = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)\r\n input_mask = token_type_ids + ([pad_token_segment_id] * padding_length) \r\n``` \r\n\r\nand\r\n\r\n` self.sub_model=model_class.from_pretrained(\"xlnet_base-cased\",from_tf=bool(\".ckpt\" in \"xlnet-base-cased\"),config=config,cache_dir=None)`\r\n\r\n`out=self.sub_model(all_input_ids,all_segment_ids,all_input_mask)`\r\n\r\nI thought it is because segment_ids and input_mask are in reversed order, but after switching them, the acc is still low.\r\n\r\nI tried RoBERTa, the acc tends to be normal.", "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, @chlorane ,I got the same problem,and did you solve it???" ]

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[ "This has been suggested a while back when this was first introduced (we're at 1.4 now). This is possibly impractical to do since it is likely that many people are still on 1.0<=x<1.2. ", "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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[ "python version is 3.7.3", "Hi, you would need to provide more information than that for us to help you. What code made you run into this error? ", "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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[ "I'm afraid I don't understand your question. The pretrained model is an architecture whose weights have already been trained on some task (typically (M)LM and NSP/SOP). When you finetune the model, the architecture stays exactly the same but the weights are finetuned to best fit your task.", "@BramVanroy I tried “print(model)”，it showed the information of every layer. Thank you so much! " ]

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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, GLUE is a sequence classification task, not a token classification task. The model you're using classifies tokens instead of entires sequences, and therefore has a different output than what is expected by the GLUE task.\r\n\r\nChange this line:\r\n\r\n```py\r\nmodel = TFBertForTokenClassification.from_pretrained('bert-base-uncased')\r\n```\r\n\r\nto this:\r\n\r\n```py\r\nmodel = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')\r\n```\r\n\r\nfor it to work.", "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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[ "# Load dataset, tokenizer, model from pretrained model/vocabulary\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-cased')\r\nmodel = TFBertForSequenceClassification.from_pretrained('bert-base-cased', force_download=True)\r\ndata = tensorflow_datasets.load('glue/mrpc')\r\n\r\nprint(\"checkpoint on data\")\r\n# Prepare dataset for GLUE as a tf.data.Dataset instance\r\n\r\nprint(type(data['train']))\r\ntrain_data = data['train']\r\ntrain_dataset = glue_convert_examples_to_features(train_data, tokenizer, max_length=128 , task='mrpc')\r\nvalid_dataset = glue_convert_examples_to_features(data['validation'], tokenizer, max_length=128, task='mrpc')\r\ntrain_dataset = train_dataset.shuffle(100).batch(32).repeat(2)\r\nvalid_dataset = valid_dataset.batch(64)\r\n\r\n\r\nThis is the whole code I am running!! \r\nthis is the error: \r\n\r\n\r\ntrain_dataset = glue_convert_examples_to_features(train_data, tokenizer, max_length=128 , task='mrpc')\r\n File \"./transformers/data/processors/glue.py\", line 84, in glue_convert_examples_to_features\r\n logger.info(\"Writing example %d/%d\" % (ex_index, len(examples)))\r\nTypeError: object of type '_OptionsDataset' has no len()", "Hi! Indeed, it seems there was an error in the code. It was fixed by @neonbjb in https://github.com/huggingface/transformers/pull/2564.\r\n\r\nCould you install from source and let me know if it fixes your issue? `pip install git+https://github.com/huggingface/transformers`. Thank you.", "Yes, the issue is resolved. Thank you!! " ]

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[ "The CI was a dependency glitch that was fixed on master since, you can rebase on master if you want it to go away.", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2579?src=pr&el=h1) Report\n> :exclamation: No coverage uploaded for pull request base (`master@cb13c8a`). [Click here to learn what that means](https://docs.codecov.io/docs/error-reference#section-missing-base-commit).\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2579?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2579 +/- ##\n=========================================\n Coverage ? 74.61% \n=========================================\n Files ? 87 \n Lines ? 14802 \n Branches ? 0 \n=========================================\n Hits ? 11044 \n Misses ? 3758 \n Partials ? 0\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2579?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/2579?src=pr&el=footer). Last update [cb13c8a...36339e7](https://codecov.io/gh/huggingface/transformers/pull/2579?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2579?src=pr&el=h1) Report\n> :exclamation: No coverage uploaded for pull request base (`master@cb13c8a`). [Click here to learn what that means](https://docs.codecov.io/docs/error-reference#section-missing-base-commit).\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2579?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2579 +/- ##\n=========================================\n Coverage ? 74.61% \n=========================================\n Files ? 87 \n Lines ? 14802 \n Branches ? 0 \n=========================================\n Hits ? 11044 \n Misses ? 3758 \n Partials ? 0\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2579?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/2579?src=pr&el=footer). Last update [cb13c8a...36339e7](https://codecov.io/gh/huggingface/transformers/pull/2579?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "@julien-c I'll just do a new PR." ]

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[ "Hi, could you specify your versions of `tokenizers` and `transformers`? I believe you're running on an older version of `transformers`, could you install from source: `pip install git+https://github.com/huggingface/transformers` and let me know if it fixes this issue? Thank 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", "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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[ "It seems like the paths to your data files are incorrect.\r\n\r\nAre you sure they're not at `./dataset/wiki.train.raw ` (notice the leading `.`)", "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/2576?src=pr&el=h1) Report\n> Merging [#2576](https://codecov.io/gh/huggingface/transformers/pull/2576?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/9d87eafd118739a4c121d69d7cff425264f01e1c?src=pr&el=desc) will **decrease** coverage by `29.56%`.\n> The diff coverage is `6.25%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2576?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2576 +/- ##\n===========================================\n- Coverage 74.51% 44.94% -29.57% \n===========================================\n Files 87 87 \n Lines 14920 14951 +31 \n===========================================\n- Hits 11117 6720 -4397 \n- Misses 3803 8231 +4428\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/2576?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/2576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `77.1% <0%> (-21.69%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/2576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `0% <0%> (-61.1%)` | :arrow_down: |\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/2576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `61.51% <11.76%> (-6.95%)` | :arrow_down: |\n| [src/transformers/modeling\\_xlm\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/2576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ194bG1fcm9iZXJ0YS5weQ==) | `0% <0%> (-100%)` | :arrow_down: |\n| [src/transformers/modeling\\_camembert.py](https://codecov.io/gh/huggingface/transformers/pull/2576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19jYW1lbWJlcnQucHk=) | `0% <0%> (-100%)` | :arrow_down: |\n| [src/transformers/optimization.py](https://codecov.io/gh/huggingface/transformers/pull/2576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb24ucHk=) | `0% <0%> (-96%)` | :arrow_down: |\n| [src/transformers/modeling\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/2576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19kaXN0aWxiZXJ0LnB5) | `0% <0%> (-95.86%)` | :arrow_down: |\n| [src/transformers/modeling\\_ctrl.py](https://codecov.io/gh/huggingface/transformers/pull/2576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19jdHJsLnB5) | `0% <0%> (-94.28%)` | :arrow_down: |\n| [src/transformers/modeling\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/2576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `0% <0%> (-87.91%)` | :arrow_down: |\n| [src/transformers/modeling\\_xlm.py](https://codecov.io/gh/huggingface/transformers/pull/2576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ194bG0ucHk=) | `0% <0%> (-86.42%)` | :arrow_down: |\n| ... and [20 more](https://codecov.io/gh/huggingface/transformers/pull/2576/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2576?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/2576?src=pr&el=footer). Last update [9d87eaf...55069c7](https://codecov.io/gh/huggingface/transformers/pull/2576?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "This feels like a very similar method to [generate](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_utils.py#L567). If we want to add it to the library's internals, don't you think it would make sense to add it directly to the models' internals like `generate`? I feel it would be consistent to mirror it but for mask filling.\r\n\r\nThis way it could be called like this:\r\n\r\n```py\r\nmodel.fill_mask(x)\r\n```\r\n\r\nFurthermore, I think that handling strings is nice, but handling lists of tokens would be better, like `generate` does -> more coherent with `generate`, only handle model data (tokens) and no need to pass a tokenizer to a `modeling_utils` internal method.", "Ok I'll take a deeper look next week @LysandreJik ", "Ok, I see what you mean. \r\n\r\nOur desired use case is that we would be able to do this in one line, on a string. It's a usage sample we've been seeing more and more (initially from RoBERTa), as it lets one check that the LM works well, in one line.\r\n\r\nExamples:\r\n- https://github.com/musixmatchresearch/umberto#fairseq-1\r\n- https://camembert-model.fr/\r\n\r\nI see two options:\r\n- define it in its own utils-like file, e.g. `fill_mask.py` or `hub_utils.py` or whatever\r\n- create a `FillMaskPipeline`, as the pipeline already has a tokenizer and a model\r\n\r\nWdyt?", "The second option would look something like the last commit and would be used like:\r\n\r\n```python\r\nmasked_line = \"Le camembert est <mask> :)\"\r\n\r\nmodel = CamembertForMaskedLM.from_pretrained(\"camembert-base\")\r\ntokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\r\n\r\n\r\nfill_mask = FillMaskPipeline(model, tokenizer)\r\n\r\nprint(fill_mask(masked_line))\r\n```", "I'm hyped by the pipeline option. I believe filling a mask, being the main use-case of an MLM trained model would be a very nice pipeline to have, alongside sequence classification, question answering and named entity recognition.", "I think you are very right and I wholeheartedly agree with you. <3 <3", "Looks good to me, you can merge", "@mfuntowicz As the pipeline exposes its tokenizer, I'm guessing you can already do something like (untested):\r\n\r\n```python\r\nfill_mask = pipeline(\"fill-mask\")\r\nfill_mask(f\"My name is {fill_mask.tokenizer.mask_token}.\")\r\n```", "Nice one @julien-c!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2575?src=pr&el=h1) Report\n> Merging [#2575](https://codecov.io/gh/huggingface/transformers/pull/2575?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/1a8e87be4e2a1b551175bd6f0f749f3d2289010f?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2575?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2575 +/- ##\n=======================================\n Coverage 74.53% 74.53% \n=======================================\n Files 87 87 \n Lines 14819 14819 \n=======================================\n Hits 11046 11046 \n Misses 3773 3773\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2575?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/2575?src=pr&el=footer). Last update [1a8e87b...bd3fe2f](https://codecov.io/gh/huggingface/transformers/pull/2575?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "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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[ "Yes this file is correct." ]

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[ "Yes, this is how RoBERTa was trained. " ]

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[ "Hi, your error states: \r\n\r\n```\r\nOne possible root cause is the client and server binaries are not built with the same version. Please make sure the operation or function is registered in the binary running in this process.\r\n```\r\n\r\nDo you have the same TensorFlow versions for your TPU and your VM?", "The tensorflow version for my VM is 2.1.0. As I understand it, older TF versions are not supported by Huggingface. \r\n\r\nHow would I check the tf version on my TPU? Better yet, is there a recommendation or code sample to provision a Huggingface-compatible TPU?", "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", "> \r\n> \r\n> The tensorflow version for my VM is 2.1.0. As I understand it, older TF versions are not supported by Huggingface.\r\n> \r\n> How would I check the tf version on my TPU? Better yet, is there a recommendation or code sample to provision a Huggingface-compatible TPU?\r\n\r\nHi,\r\nI am facing the same issue. TF and TPU versions are the same.\r\nDid you manage to have it resolved?\r\nThanks" ]

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[ "Even if I do `add_special_tokens=True` when encoding, I get\r\n\r\n```\r\n[CLS] why isn ' t my card working [SEP]\r\n```\r\n\r\nwhich is still not wordpiece tokenization.", "When using `encode` and `decode` you're performing the full tokenization steps each time:\r\n\r\nencode: tokenizing -> convert tokens to ids\r\ndecode: convert tokens to ids -> detokenizing\r\n \r\nIf you want to see the middle step, you can use the `tokenize` method:\r\n\r\n```py\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\ntext = '''why isn't my card working'''\r\nprint(tokenizer.tokenize(text)) # ['why', 'isn', \"'\", 't', 'my', 'card', 'working']\r\n```\r\n\r\nAll the words are in the vocabulary, but if you use more complex words:\r\n\r\n```py\r\ntokenizer.tokenize(\"Why isn't my text tokenizing\") # ['why', 'isn', \"'\", 't', 'my', 'text', 'token', '##izing']\r\n```\r\n\r\nYou'll see the `##ing` you were looking for.\r\n\r\n", "Looks great! Thank you!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2570?src=pr&el=h1) Report\n> Merging [#2570](https://codecov.io/gh/huggingface/transformers/pull/2570?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/65a89a89768f5922e51cdc7d49990d731e3f2c03?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2570?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2570 +/- ##\n=======================================\n Coverage 74.61% 74.61% \n=======================================\n Files 87 87 \n Lines 14802 14802 \n=======================================\n Hits 11044 11044 \n Misses 3758 3758\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2570?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/2570?src=pr&el=footer). Last update [65a89a8...55939b5](https://codecov.io/gh/huggingface/transformers/pull/2570?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Looks good to me, feel free to merge it when you feel ready @julien-c ", "Yo! merge this shit up!! ", "Thanks, A Lot Guys your the best!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2569?src=pr&el=h1) Report\n> Merging [#2569](https://codecov.io/gh/huggingface/transformers/pull/2569?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/65a89a89768f5922e51cdc7d49990d731e3f2c03?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2569?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2569 +/- ##\n=======================================\n Coverage 74.61% 74.61% \n=======================================\n Files 87 87 \n Lines 14802 14802 \n=======================================\n Hits 11044 11044 \n Misses 3758 3758\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2569?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/2569?src=pr&el=footer). Last update [65a89a8...3ef04e1](https://codecov.io/gh/huggingface/transformers/pull/2569?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Indeed", "Thanks!" ]

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[ "You're right, ALBERT should work out of the box with the fine tuning script as addressed at #2008 by @thomwolf. It's not too tough to fine-tune ALBERT with the script as reference, and there should also be a PR to add ALBERT and some other language models sometime in the near future", "Thank you!" ]

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[ "Well, how are you actually using it? Are you actually fine-tuning the model? What's your train loop?", "I actually just solved the issue. It seems that the code I posted was\ncorrect, but it has to do with where I placed the scheduler.step().\n\nOn Mon, Jan 20, 2020 at 6:57 AM Bram Vanroy <[email protected]>\nwrote:\n\n> Well, how are you actually using it? Are you actually fine-tuning the\n> model? What's your train loop?\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/transformers/issues/2567?email_source=notifications&email_token=AFB56KPQC3V5YOYAOQN5YRLQ6WGRXA5CNFSM4KIOCMLKYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOEJMMIKY#issuecomment-576242731>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AFB56KMFFHCCUDF7SFZNR63Q6WGRXANCNFSM4KIOCMLA>\n> .\n>\n", "Glad to hear that you solved the issue. Please close the question. " ]

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[ "Hi @lambdaofgod,\r\n\r\n.tolist() call is there as Python's lists are more compatible with CSV/JSON serialisation than numpy array.\r\n\r\nDid you have a chance to get number of how actually slower it is ? If the difference is non negligible then we might have a look to optimise .tolist() only when serialising through JSON/CSV", "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/2564?src=pr&el=h1) Report\n> Merging [#2564](https://codecov.io/gh/huggingface/transformers/pull/2564?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6d5049a24d5906ece3fd9b68fb3abe1a0b6bb049?src=pr&el=desc) will **decrease** coverage by `0.01%`.\n> The diff coverage is `0%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2564?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2564 +/- ##\n==========================================\n- Coverage 74.6% 74.58% -0.02% \n==========================================\n Files 87 87 \n Lines 14802 14805 +3 \n==========================================\n Hits 11043 11043 \n- Misses 3759 3762 +3\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/2564?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/data/processors/glue.py](https://codecov.io/gh/huggingface/transformers/pull/2564/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvZ2x1ZS5weQ==) | `27.53% <0%> (-0.34%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2564?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/2564?src=pr&el=footer). Last update [6d5049a...17f172e](https://codecov.io/gh/huggingface/transformers/pull/2564?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "A proper fix for this would probably be to add a unit test that sends tf datasets through GLUE. Let me know if you want me to add that in as well.", "That's great, thanks @neonbjb " ]

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[ "Great, thanks!" ]

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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", "Did you check out [DialoGPT](https://huggingface.co/microsoft/DialoGPT-large) by @dreasysnail?", "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 @miki537, we already have `transformers-cli s3 rm ____` but it is not super well documented.\r\n\r\nI'll improve the documentation on that point. Also `transformers-cli upload` will overwrite existing files with the same name so you can already update files.\r\n\r\nS3 doesn't not support moving/renaming files so I'm reluctant to introduce a `rename` (which would need to download the files locally then re-upload with new name – this is what the official aws-cli does)", "I'll close this for now, feel free to reopen if it's not well documented enough (or even better, improve it and create a PR :)", "Hi Julien, I'm also trying to delete some of my shared models, however, transformers-cli s3 rm ____ seems not working for me. All the models are still there after the command is done", "Which model(s) do you intend to delete @Jiaxin-Pei?", "@julien-c \r\nHere are they:\r\npedropei/question-intimacy-DEMO\r\npedropei/question-intimacy-demo\r\npedropei/random-demo\r\n\r\n", "done" ]

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[ "The layer normalization happening after the attention is visible [here](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_albert.py#L255).\r\n\r\nThe `inner_group_num` is used to better understand how many layers are in a specific group. It is set to 1 in all the configurations that google-research has output as they all have a single repeating layer, but it would be necessary to increase the number of inner groups if you pre-trained an ALBERT model that used more than one repeating layer.\r\n\r\nYou can see the `inner_group_num` in the official configuration files, for example the [xxlarge-v3](https://tfhub.dev/google/albert_xxlarge/3)." ]

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[ "Yes, the output is wrong. \r\nI think the run_tf_ner.py script has a bug where the labels are off by 1. \r\nAnd the off-by-1 prediction result is sent for evaluation `metrics.classification_report(y_true, y_pred, digits=4)` therefore the evaluation result is wrong too. ", "Thanks @HuiyingLi! The workaround works." ]

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[ "Hi! I'd like to replicate the error you had with the `AttributeError`. Could you let me know in which situation you faced this error?" ]

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[ "Not sure how to fix that last CI build, seems unrelated?", "Unrelated Heisenbug, relaunched the CI", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2557?src=pr&el=h1) Report\n> Merging [#2557](https://codecov.io/gh/huggingface/transformers/pull/2557?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/23a2cea8cb95864ddb7e7e80e126e4f083640882?src=pr&el=desc) will **increase** coverage by `<.01%`.\n> The diff coverage is `100%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2557?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2557 +/- ##\n==========================================\n+ Coverage 74.6% 74.61% +<.01% \n==========================================\n Files 87 87 \n Lines 14802 14802 \n==========================================\n+ Hits 11043 11044 +1 \n+ Misses 3759 3758 -1\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/2557?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/2557/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmVydC5weQ==) | `96.62% <100%> (+0.42%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2557?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/2557?src=pr&el=footer). Last update [23a2cea...c0afe26](https://codecov.io/gh/huggingface/transformers/pull/2557?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Thank you sir!" ]

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[ "@LysandreJik any ideas on this? I am itching to use a quantized BERT model in production, but it does not work when loaded in :(", "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 @ElektrikSpark , I am also facing similar issue, were you able to resolve this? \r\nI am unable to get good results while loading the quantized bert model. " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2555?src=pr&el=h1) Report\n> Merging [#2555](https://codecov.io/gh/huggingface/transformers/pull/2555?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6e2c28a14a3d171e8c4d3838429abb1d69456df5?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2555?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2555 +/- ##\n=======================================\n Coverage 74.66% 74.66% \n=======================================\n Files 87 87 \n Lines 14802 14802 \n=======================================\n Hits 11052 11052 \n Misses 3750 3750\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2555?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/2555?src=pr&el=footer). Last update [6e2c28a...e268f1c](https://codecov.io/gh/huggingface/transformers/pull/2555?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "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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[ { "id": 1314768611, "node_id": "MDU6TGFiZWwxMzE0NzY4NjEx", "url": "https://api.github.com/repos/huggingface/transformers/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": null } ]

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[ "The interesting things is that why do u use another model’s tokenizer to\nprocess data .\n\nDo u know each model tokenizer is a map function which map ID to token ?\nSo for each model , the same word for them is mapping to different iD . So\nit will not learning . Do u read paper ?\n\nOn Fri, Jan 17, 2020 at 03:19 Trevor Pfizenmaier <[email protected]>\nwrote:\n\n> 🐛 Bug\n>\n> Model I am using (Bert, XLNet....): AlbertForSequenceClassification\n>\n> Language I am using the model on (English, Chinese....): English\n>\n> The problem arise when using:\n> When I use albert-base-v2 instead of albert-base-v1 for the model and\n> tokenizer, the model does not learn during training.\n>\n> The tasks I am working on is:\n> https://colab.research.google.com/drive/1Y4o3jh3ZH70tl6mCd76vz_IxX23biCPP\n> To Reproduce\n>\n> Steps to reproduce the behavior:\n>\n> 1. Open the colab notebook I have referenced above\n> 2. Change the word Bert to Albert in necessary places\n> 3. Run 4.3. Training Loop, you will see that the model does not learn\n>\n> Expected behavior\n>\n> I would expect the model to learn on the given task.\n> Environment\n>\n> Colab\n> Additional context\n>\n> I can not pinpoint exactly what the problem is, but it almost seems like\n> the data that is being fed to the model is not understood. If I use the\n> AlbertTokenizer for a BertForSequenceClassification model, which the BERT\n> model would obviously not understand, the same behavior is exhibited.\n>\n> —\n> You are receiving this because you are subscribed to this thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/transformers/issues/2553?email_source=notifications&email_token=AIEAE4DURWPRQ22RW6PWX5DQ6CXMXA5CNFSM4KHZJIJ2YY3PNVWWK3TUL52HS4DFUVEXG43VMWVGG33NNVSW45C7NFSM4IGXUZ2Q>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AIEAE4DTEA5RJ36IGEJQVGTQ6CXMXANCNFSM4KHZJIJQ>\n> .\n>\n", "I know that you can not use a different tokenizer, as I said \"which the BERT model would obviously not understand\". I did this to extend my understanding of the problem, which yields the exact same behavior (loss does not go down/accuracy does not go down/model does not learn) as using `albert-base-v2` + `AlbertTokenizer` with `albert-base-v2` + `AlbertForSequenceClassification`.", "I'm getting similar bad results. seems like ALBERT v2 isn't converging on the hyperparameters published in the original 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", "Has anyone figured this out? I've attempted the same thing (in tensorflow) by swapping out bert model & tokenizer for Albert V1 and V2 and no learning is done. Bert works just fine, but Albert is a no-go.\r\n", "@1337-Pete, current version of ALBERT V1 and V2 work well but are very sensitive to the training data and hyperparameters. If you use the hyperparameters from the paper you will get similar results for both models on all GLUE tasks." ]

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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", "=(", "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, this should have been fixed with the release of `v3.0.0`. Thanks a lot for your contribution!" ]

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[ "Can you try again? It seems that the server is reachable now. Of course you must be connected to the Internet.", "The server was reachable. I try the same URL in my browser at the time of doing it and it loaded fine. Its just via python / transformers that he problem occurs (I've tried everyday for 3 days now). Could this be something to do with file locks? I get a similar message above. I will try again and post the full error message", "Full Error Message:\r\n```\r\n01/17/2020 15:52:28 - WARNING - lib.squad - Process rank: -1, device: cuda, n_gpu: 1, distributed training: False, 16-bits training: True\r\n01/17/2020 15:52:29 - INFO - filelock - Lock 1473387147720 acquired on TensorflowQA/cache2\\4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.bf3b9ea126d8c0001ee8a1e8b92229871d06d36d8808208cc2449280da87785c.lock\r\n01/17/2020 15:52:29 - INFO - transformers.file_utils - https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json not found in cache or force_download set to True, downloading to D:\\GBKaggleChallenges\\NLP\\TensorflowQA\\cache2\\tmpjccrll9f\r\nHBox(children=(FloatProgress(value=0.0, description='Downloading', max=313.0, style=ProgressStyle(description_…\r\n01/17/2020 15:52:29 - INFO - transformers.file_utils - storing https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json in cache at TensorflowQA/cache2\\4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.bf3b9ea126d8c0001ee8a1e8b92229871d06d36d8808208cc2449280da87785c\r\n01/17/2020 15:52:29 - INFO - filelock - Lock 1473387147720 released on TensorflowQA/cache2\\4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.bf3b9ea126d8c0001ee8a1e8b92229871d06d36d8808208cc2449280da87785c.lock\r\n\r\nTraceback (most recent call last):\r\n\r\n File \"d:\\cudaenv\\lib\\site-packages\\transformers\\configuration_utils.py\", line 179, in from_pretrained\r\n resume_download=resume_download,\r\n\r\n File \"d:\\cudaenv\\lib\\site-packages\\transformers\\file_utils.py\", line 212, in cached_path\r\n user_agent=user_agent,\r\n\r\n File \"d:\\cudaenv\\lib\\site-packages\\transformers\\file_utils.py\", line 392, in get_from_cache\r\n os.rename(temp_file.name, cache_path)\r\n\r\nPermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'D:\\\\GBKaggleChallenges\\\\NLP\\\\TensorflowQA\\\\cache2\\\\tmpjccrll9f' -> 'TensorflowQA/cache2\\\\4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.bf3b9ea126d8c0001ee8a1e8b92229871d06d36d8808208cc2449280da87785c'\r\n\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n\r\n File \"<ipython-input-2-e8fb1981d786>\", line 18, in <module>\r\n squad.main(args)\r\n\r\n File \"D:\\GBKaggleChallenges\\NLP\\lib\\squad.py\", line 743, in main\r\n cache_dir=args.cache_dir if args.cache_dir else None,\r\n\r\n File \"d:\\cudaenv\\lib\\site-packages\\transformers\\configuration_utils.py\", line 200, in from_pretrained\r\n raise EnvironmentError(msg)\r\n\r\nOSError: Couldn't reach server at 'https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json' to download pretrained model configuration file.```", "You're on Windows right? Might there be something wrong with the paths?", "> You're on Windows right? Might there be something wrong with the paths?\r\n\r\nI can see that the D:\\\\GBKaggleChallenges\\\\NLP\\\\TensorflowQA\\\\cache2\\\\tmpjccrll9f\r\nis created (suggesting the paths are fine) and in fact contains the following, which makes the message even more confusing:\r\n\r\n```\r\n{\r\n \"attention_probs_dropout_prob\": 0.1,\r\n \"hidden_act\": \"gelu\",\r\n \"hidden_dropout_prob\": 0.1,\r\n \"hidden_size\": 768,\r\n \"initializer_range\": 0.02,\r\n \"intermediate_size\": 3072,\r\n \"max_position_embeddings\": 512,\r\n \"num_attention_heads\": 12,\r\n \"num_hidden_layers\": 12,\r\n \"type_vocab_size\": 2,\r\n \"vocab_size\": 30522\r\n}\r\n```", "I just installed from source and has now gotten passed that particular error so I believe this was something that was fixed within the last week. ", "@BramVanroy I have this issue from time to time. I think it's a network timeout issue when the connection is not stable. Unfortunately, it happens.\r\n It would be nice if this configuration can be cached too, there is no need to download the same config file each time.", "> @BramVanroy I have this issue from time to time. I think it's a network timeout issue when the connection is not stable. Unfortunately, it happens.\r\n> It would be nice if this configuration can be cached too, there is no need to download the same config file each time.\r\n\r\nYou can restrict your script to using only local files by using e.g.\r\n\r\n```python\r\ntokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased', local_files_only=True)\r\n```\r\n\r\nNote that this will only work if the required files were already downloaded once before." ]

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[ "Have given some thought to using TorchScript but since my input sequence length changes each time, the easier tracing approach won't work.\r\n\r\nONNX also faced the same problem.", "Hi rajarsheem,\r\nCan you please share your code that doing the batch inference with variable-length sequences?\r\n\r\nThanks", "Is gpt2 model traceable? ", "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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[ "I am fixing this issue and just understood that `return_attention_masks` is supposed to work only if `return_tensors is not None`. But it is not mentioned in the docstring nor raises an error. Also, in the case of `is_tf_available` and `return_tensors == 'pt'` current code would return tensorflow maks.\r\nI'd suggest to increase the identation of this block\r\n\r\n```python\r\n# encoder_attention_mask requires 1 for real token, 0 for padding, just invert value\r\nif return_attention_masks:\r\n if is_tf_available():\r\n batch_outputs[\"attention_mask\"] = tf.abs(batch_outputs[\"attention_mask\"] - 1)\r\n else:\r\n batch_outputs[\"attention_mask\"] = torch.abs(batch_outputs[\"attention_mask\"] - 1)\r\n```\r\n\r\nand to raise ValueError if `return_attention_masks` and `return_tensors is None`", "This [PR](https://github.com/huggingface/transformers/pull/2552) is my proposed fix of the issue.\r\n", "It seems that tf2.0 can trigger `is_tf_available()`. I've documented the effects of tf version on this functionality [here](https://colab.research.google.com/drive/1a4qmiiZpPXu4mhscJkN_Q0tbd_-HR4jY)", "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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[ "Indeed, this looks like a bug, thank you for opening an issue. I'll take a look at it.", "This issue stems from the two arguments: `max_seq_length=128` and `doc_stride=128`.\r\n\r\nWould you mind telling me the expected behavior when putting a doc stride as big as the maximum sequence length? Since the sequence length considers both the document and the question, I don't see a reason for putting such a high document stride: it is larger than the maximum document length that will be in the sequence, and therefore breaks the stride.\r\n\r\nAccording to your use-case we'll do our best to accommodate it with our script.", "You are correct, I would expect a warning or an error in this case.\r\nAnyway, even if the `doc_stride` was less than the `max_seq_len` there will still be a possibility that doc tokens will be skipped in the current implementation so I think a warning should occur. I would expect that `doc_tokens` will never be skipped but that's me", "Indeed, I agree with you that raising a warning, in this case, would be best. Thanks for your feedback!", "I've added a warning in 6e2c28a", "I think the solution is to specify the question token max length and\npadding the question part to the max question token length, then you can\njust set the max doc_stride is max seq_length - max question length . It\nwill not loss any information\n\nOn Fri, Jan 17, 2020 at 03:00 Lysandre Debut <[email protected]>\nwrote:\n\n> I've added a warning in 6e2c28a\n> <https://github.com/huggingface/transformers/commit/6e2c28a14a3d171e8c4d3838429abb1d69456df5>\n>\n> —\n> You are receiving this because you are subscribed to this thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/transformers/issues/2548?email_source=notifications&email_token=AIEAE4FWVAXHHAENIDH2JETQ6CVFNA5CNFSM4KHW5362YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOEJFFA4Y#issuecomment-575295603>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AIEAE4HZCAYY5EHQTOLRQEDQ6CVFNANCNFSM4KHW536Q>\n> .\n>\n", "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" ]