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DanielPFlorian

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Transformers-Github-Issues

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[ "Hey @Muktan,\r\n\r\nThanks a lot for working on this! Could you add a test that shows how your code solves the issue? :-)\r\n\r\nIt should be in `tests/test_tokenization_wav2vec2.py`", "Welcome @patrickvonplaten, I will add a test that shows how the code solves the issue." ]

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[ "Hey @stancld,\r\n\r\nOne important thing we should implement before merging is the caching mechanismh similar to how it's done in GPT2: https://github.com/huggingface/transformers/blob/0b0a598452b02278075a75f84b5ca7bb457224ad/src/transformers/models/gpt2/modeling_flax_gpt2.py#L139. @patil-suraj, could you help here maybe? :-) ", "Sure @patrickvonplaten !\r\n\r\n@stancld let me know if you need help here or I could take this if you are okay with it :)", "@patrickvonplaten a couple of last questions about the API\r\n\r\n- Right now the `FlaxBartPretrainedModel.__call__` method also accepts the `encoder_outputs`, but think it would be cleaner to not do that as we already have `encode/decode` methods and let the user use `encode` or `decode` if they want to run just one part of the model. So we could make `decode` method available for every model (right now it's only available for the `ForConditionalGeneration` model) and it'll return the decoder outputs and for `*ForConditionalGeneration` models, it'll also return the `logits`.\r\n\r\n- The `decode` method returns `FlaxSeq2SeqLM` output, which includes both the encoder and decoder outputs, but when calling `decode` the user already has `encoder_outputs`, so maybe we should just return decoder outputs since the `decode` method only runs the decoder.\r\n\r\nWhat do you think? \r\n", "IMO:\r\n\r\n1) Agree, happy to remove `encoder_outptus` as an input argument from `call` & make `decode` available for all models\r\n2) Yes, it doesn't make too much sense to include all the encoder relevant output here!" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", "Got the same problem. Have you solved it yet?", "Finally I got to solve this problem. This error is caused by 3-D parameters. When the optimizer gets a `[dim1, dim2, dim3]` parameter, [transformers/optimization.py Line 544](https://github.com/huggingface/transformers/blob/master/src/transformers/optimization.py#L544) sets `state[\"exp_avg_sq_row\"]` as `[dim1, dim2]` and `state[\"exp_avg_sq_col\"]` as `[dim1, dim3]`. Then the two parameters in [line 508](https://github.com/huggingface/transformers/blob/master/src/transformers/optimization.py#L508) become `[dim1, dim2, 1]` and `[1, dim1, dim3]`, and the error occurs.\r\n\r\nTo solve this issue, I create my own adafactor optimizer and change line 506-508 to \r\n```\r\nr_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_().unsqueeze(-1)\r\nc_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt()\r\nreturn torch.mul(r_factor, c_factor)\r\n```\r\naccording to [fairseq's implementation](https://github.com/pytorch/fairseq/blob/main/fairseq/optim/adafactor.py#L159).", "Actually having the same problem", "@ybch14 - do you think this could also be fixed in `transformers` Adafactor implementation?", "> @ybch14 - do you think this could also be fixed in `transformers` Adafactor implementation?\r\n\r\nDefinitely, just change line 506-508 of [transformers/optimization.py](https://github.com/huggingface/transformers/blob/master/src/transformers/optimization.py#506) as I mentioned above then all done! I'm creating my custom optimizer just because I'm not familiar with pull request process and in a hurry with my development needs. I would really appreciate it if you can help initiate a pull request.\r\n\r\nI will attach my local test code here to help your local test:\r\n```\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom transformers.optimization import Adafactor\r\n\r\nclass Model(nn.Module):\r\n def __init__(self):\r\n super(Model, self).__init__()\r\n self.w = nn.Parameter(torch.randn(2, 3, 4), requires_grad=True)\r\n\r\n def forward(self):\r\n return self.w.mean().sigmoid()\r\n\r\ndevice = torch.device(\"cuda\")\r\ntarget = torch.tensor(1.).to(device)\r\nmodel = Model().to(device)\r\ny = model()\r\nloss = F.binary_cross_entropy(y, target)\r\nloss.backward()\r\noptimizer = Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None)\r\noptimizer.step()\r\n```\r\n", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", "Thanks a lot for your help here @ybch14 ! I've opened a PR to fix it just like you suggested and it seems to work just fine :-)", "BTW, we have some guidelines here on how you can open pull requests: \r\nhttps://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md", "@patrickvonplaten Thank you for your PR and hope pytorch gets better :)" ]

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[ "Following are the average test results for the `_compute_mask_indices` function when ran 100 times. Each X.1 subtest case is calculated with `attention_mask = None` and each X.2 subtest case is calculated with `attention_mask` calculated with the following code:\r\n```\r\nattention_mask = torch.ones((batch_size, sequence_length), device=torch_device, dtype=torch.long)\r\nattention_mask[:, -sequence_length // 2 :] = 0\r\n```\r\n1) Test - 100 times\r\nbatch_size = 4\r\nsequence_length = 60\r\nmask_prob = 0.5\r\nmask_length = 1\r\nTest **1.1** - \r\nResult - seconds \r\nNew Code GPU: 0.002872414588928223\r\nNew Code CPU: 0.0006633639335632324\r\nOld Code: 0.0003826594352722168\r\nTest **1.2** - \r\nResult - seconds\r\nNew Code GPU: 0.002973439693450928\r\nNew Code CPU: 0.0006422805786132813\r\nOld Code: 0.0004153728485107422\r\n\r\n2) Test - 100 times\r\nbatch_size = 100\r\nsequence_length = 60\r\nmask_prob = 0.5\r\nmask_length = 1\r\nTest **2.1** - \r\nResult - seconds \r\nNew Code GPU: 0.0663988971710205\r\nNew Code CPU: 0.014422652721405029\r\nOld Code: 0.008053600788116455\r\nTest **2.2** - \r\nResult - seconds\r\nNew Code GPU: 0.06568058252334595\r\nNew Code CPU: 0.01404146671295166\r\nOld Code: 0.008796172142028809\r\n\r\n3) Test - 100 times\r\nbatch_size = 1000\r\nsequence_length = 60\r\nmask_prob = 0.5\r\nmask_length = 1\r\nTest **3.1** - \r\nResult - seconds \r\nNew Code GPU: 0.6623778533935547\r\nNew Code CPU: 0.14311392545700075\r\nOld Code: 0.08917582988739013\r\nTest **3.2** - \r\nResult - seconds\r\nNew Code GPU: 0.6566315603256225\r\nNew Code CPU: 0.13569485664367675\r\nOld Code: 0.08646429538726806\r\n\r\n4) Test - 100 times\r\nbatch_size = 4\r\nsequence_length = 1000\r\nmask_prob = 0.5\r\nmask_length = 1\r\nTest **4.1** - \r\nResult - seconds \r\nNew Code GPU: 0.0031879472732543944\r\nNew Code CPU: 0.0013749027252197266\r\nOld Code: 0.00248842716217041\r\nTest **4.2** - \r\nResult - seconds\r\nNew Code GPU: 0.0031322765350341795\r\nNew Code CPU: 0.0010571050643920898\r\nOld Code: 0.0015622496604919434\r\n\r\n5) Test - 100 times\r\nbatch_size = 4\r\nsequence_length = 60\r\nmask_prob = 0.5\r\nmask_length = 4\r\nTest **5.1** - \r\nResult - seconds \r\nNew Code GPU: 0.003424525260925293\r\nNew Code CPU: 0.0008220672607421875\r\nOld Code: 0.0003489851951599121\r\nTest **5.2** - \r\nResult - seconds\r\nNew Code GPU: 0.0034962940216064454\r\nNew Code CPU: 0.0007469034194946289\r\nOld Code: 0.0003824186325073242\r\n\r\n6) Test - 100 times\r\nbatch_size = 4\r\nsequence_length = 1000\r\nmask_prob = 0.5\r\nmask_length = 4\r\nTest **6.1** - \r\nResult - seconds \r\nNew Code GPU: 0.003502027988433838\r\nNew Code CPU: 0.0014672994613647461\r\nOld Code: 0.0017711663246154786\r\nTest **6.2** - \r\nResult - seconds\r\nNew Code GPU: 0.0034971165657043455\r\nNew Code CPU: 0.0011277437210083009\r\nOld Code: 0.0011361241340637207\r\n\r\n7) Test - 100 times\r\nbatch_size = 128\r\nsequence_length = 1000\r\nmask_prob = 0.5\r\nmask_length = 4\r\nTest **7.1** - \r\nResult - seconds \r\nNew Code GPU: 0.10527128219604492\r\nNew Code CPU: 0.04762232780456543\r\nOld Code: 0.052808206081390384\r\nTest **7.2** - \r\nResult - seconds\r\nNew Code GPU: 0.1032623028755188\r\nNew Code CPU: 0.03513101100921631\r\nOld Code: 0.03523270606994629", "Hey @01-vyom,\r\n\r\nIt looks like the git history is messed up :-/ Sorry about that! This often happens when one does a wrong `git merge` -> could you maybe open a new PR with a clear git history / git diff? Thanks a lot!", "Ok sure", "I am closing this PR.", "@patrickvonplaten created the new PR." ]

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[ "For this you could either initialize a random model, save it and pass it's path as `model_name_or_path` arg.\r\nOr modify the script to create a random model instead of pre-trained. i.e to init the model use\r\n\r\n```\r\nmodel = AutoModelForSeq2SeqLM(config)\r\n```\r\ninstead of using `.from_pretrained`.", "> For this you could either initialize a random model, save it and pass it's path as `model_name_or_path` arg.\r\n> Or modify the script to create a random model instead of pre-trained. i.e to init the model use\r\n> \r\n> ```\r\n> model = AutoModelForSeq2SeqLM(config)\r\n> ```\r\n> \r\n> instead of using `.from_pretrained`.\r\n\r\nThanks for your swift reply. I already try to use model = AutoModelForSeq2SeqLM(config)\r\ninstead of using .from_pretrained. But it has bugs:\r\n```{r}\r\n File \"examples/pytorch/summarization/run_summarization.py\", line 358, in main\r\n model = AutoModelForSeq2SeqLM(config)\r\nTypeError: __init__() takes 1 positional argument but 2 were given\r\n```\r\nIt seems I should use ```model = T5ForConditionalGeneration(config = config)``` or ```model = BartForConditionalGeneration(config = config)```\r\nwhen I want to train a Bart or T5 model from scratch without loading pretrained weights. Is that right? Thank you very much!", "Ohh sorry,\r\n\r\nIt should be `AutoModelForSeq2SeqLM.from_config(...)`\r\n\r\nand yeah, you could also use the individual classes if you want.", "> Ohh sorry,\r\n> \r\n> It should be `AutoModelForSeq2SeqLM.from_config(...)`\r\n> \r\n> and yeah, you could also use the individual classes if you want.\r\n\r\nThanks for your reply. One quick question, If I use ```AutoModelForSeq2SeqLM.from_config(...)```, when I mentioned t5-small or t5-base or t5-large, is it the same amone these models? Also If I use model = ```T5ForConditionalGeneration(config = config)```, which model I am using? t5-small or t5-base or t5-large? Thank you very much!", "> which model I am using? t5-small or t5-base or t5-large? \r\n\r\nThis depends on the `config`, this initializes models according the values in the `config`, so if the `config` is of `t5-small` the model will be of `t5-small` size with random weights.", "> > which model I am using? t5-small or t5-base or t5-large?\r\n> \r\n> This depends on the `config`, this initializes models according the values in the `config`, so if the `config` is of `t5-small` the model will be of `t5-small` size with random weights.\r\n\r\nI see, thank you so much! Last question, sorry for asking so many times lol. I am trying to train T5-large from scratch, but it is very slow even though I use gpu. Do you know how to run run_summarization. py with multi_gpu? Thank you very much! ", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Re: freezing layers, someone pointed me to your answer here as well: From your deepspeed talk: https://youtu.be/RG-yV5zgqjQ?t=2450\n\nI guess it's still a bit counterintuitive that the randomly initialized head doesn't cause havoc on the weights of base layers? Just to make sure, it's not that you unfreeze the whole thing after only training the head first? You just train the entire thing unfrozen from the very beginning? \n\nI've shared this with several folks and there was enough surprise that it could be worth mentioning this explicitly in this tutorial, as this is something I think lots of folks may not know! \n\nP.S. if it is the case that you unfreeze the entire thing from the very beginning this is such a surprising result I feel like it's worth a blog post or something! ", "This is a surprise for someone who comes from the fastai community but insisting a lot on this for a user who don't even know what freezing layers is won't be helpful either, which is why I'm not mentioning it in the new version. I'll try to find a compromise between the two :-)\r\n\r\nAnd yes, Transformers model are usually fine-tuned without freezing anything, which is what is done in all the research papers for GLUE/Squad etc. Training only the randomly initialized head usually does not achieve anything good and the state it ends in is so bad you can't recover by fine-tuning the whole model after.", "@sgugger thank you for clarifying, and thanks for your patience! I'm really glad I learned about this today as I have been doing it wrong all along. \n\nSeems like an interesting research project to find out \"why\" " ]

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[ "@n1t0 might be knowledgeable about this :)", "@n1t0 depending where huggingface.co is hosted, solving this issue may just boil down to turning on dual-stack support in the hosting provider's console.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", "This issue hasn't been addressed ", "Might be interesting for @sterchelen as well as @n1t0 ", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", "This issue hasn't been addressed", "we are looking into this", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", "This issue hasn't been addressed\r\n\r\nJust commenting to keep the bot from closing the issue.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", "This issue hasn't been addressed. This can only be addressed by the huggingface team.", "@leezu Can you try again now?", "Thank you @n1t0. I verified `AutoTokenizer.from_pretrained` and `AutoModel.from_pretrained` from an IPv6-only instance (no IPv4 route to internet) works now. Thank you for enabling dual-stack support on your end!", "Yay great job on this @n1t0! You should tweet about it :)", "same issure！\r\n\r\nfailed: Cannot assign requsted address." ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", "The issue still persists and tokenizers in general still act weird with special tokens and whitespace.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", "Hello @LysandreJik, \r\nis there any update on this? We are also facing issues with added tokens for both, Rust and Python tokenizers, when using the default mt5 tokenizer.\r\n\r\nSimilar to the issues above, we experience inconsistent behavior with spaces in the immediate surroundings of added tokens.\r\n```\r\ntokenizer_fast = MT5TokenizerFast.from_pretrained(\"google/mt5-base\")\r\ntokenizer = MT5Tokenizer.from_pretrained(\"google/mt5-base\")\r\n\r\ntokenizer_fast.add_tokens(\"<new_token>\")\r\ntokenizer.add_tokens(\"<new_token>\")\r\n\r\ntext = \"This is a test <new_token>.\"\r\n\r\ntokens = tokenizer_fast.tokenize(text)\r\nprint(tokens)\r\ntokenizer_fast.convert_tokens_to_string(tokens)\r\n```\r\n`['▁This', '▁is', '▁', 'a', '▁test', '▁', '<new_token>', '▁', '.']` \r\n`'This is a test <new_token> .'`\r\n\r\nFor the fast tokenizer, a space is inserted after the added token.\r\n\r\nFor the slow one, also spaces in front of added tokens are removed:\r\n```\r\ntokens = tokenizer.tokenize(text)\r\nprint(tokens)\r\ntokenizer.convert_tokens_to_string(tokens)\r\n```\r\n`['▁This', '▁is', '▁', 'a', '▁test', '<new_token>', '▁', '.']` \r\n`'This is a test<new_token> .'`\r\n\r\nAt least for the Python tokenizer, I believe the problem lies in the way how texts with added tokens are passed to the underlying sentence_piece tokenizer. The texts are basically split by added tokens and the remaining parts are individually passed to sp. By default, the sp tokenizer adds a space at the start of each sequence and removes them at the end:\r\n```\r\ntokenizer.sp_model.encode(\"A test \", out_type=str)\r\n```\r\n`['▁A', '▁test']` \r\n\r\nWhen tokens are converted back into a single string, only the space at the very first position is removed, but not in case there is an added token in front of it\r\n```\r\ntokenizer.sp_model.decode_pieces(['▁This', '▁is', '▁', 'a', '▁test', '<new_token>', '▁', '.'])\r\n```\r\n`'This is a test<new_token> .'` \r\n\r\nFor the slow tokenizer, we could modify the tokens manually to e.g. take into account spaces in the original string. Unfortunately we lack the Rust skills to do this for the fast tokenizer.\r\n\r\nAre there any plans to adjust this in the near future (since this issue still has the WIP tag)?", "Pinging @SaulLu ", "Hey! This is being talked in the PR linked above! Sorry for the late reply", "Regarding the default MT5 problem with addition of a space, this is being handled here: #24565. The problem is not because of striping left right for ponctuation, but `rstrip` and `lstrip` are indeed ingored", "Fixing the rust tokenizer: it's a hack so I might have to change the rust code, but for now the following will strip anything on the right and left, giving the expected results. \r\n```python \r\nclass T5Converter(SpmConverter):\r\n def vocab(self, proto):\r\n num_extra_ids = self.original_tokenizer._extra_ids\r\n vocab = [(piece.piece, piece.score) for piece in proto.pieces]\r\n vocab += [(f\"<extra_id_{i}>_\", 0.0) for i in range(num_extra_ids - 1, -1, -1)]\r\n return vocab\r\n ..........\r\n``` \r\nI tested:\r\n```python \r\n>>> from transformers import AutoTokenizer\r\n>>> tokenizer=AutoTokenizer.from_pretrained(\"google/mt5-small\", from_slow = True)\r\n>>> tokenizer.tokenize(\"Hello, <extra_id_0>, \")\r\n['▁Hello', ',', '▁<extra_id_0>', ',', '▁']\r\n```\r\n" ]

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[ "One possible solution is to get the `encoder_outputs` by passing `inputs_embeds` to `encoder` and then passing that `encoder_outputs` to `.generate`, so for example\r\n\r\n```\r\nwith torch.no_grad():\r\n encoder_outputs = model.get_encoder()(inputs_embeds=input_embeds)\r\n\r\ngen_ids = model.generate(input_ids=None, encoder_outputs=encoder_outputs)\r\n```", "Thanks. " ]

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[ "Any kind soul please add fast tokenizer for Deberta V2. Would be really helpful and thanks in advance!", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Thank you, Sylvain ❤️ ", "Thank you, guys! That feels good!" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", "Ping\r\n", "@patrickvonplaten could make another review , I think the previous PR that reads `config.architectures` alleviated any issue for this PR.\r\n\r\nwhat do you think ?", "@sgugger\r\n\r\nMaybe a small sanity check if you don't mind (code has significantly changed since last review) ? Should be for the better." ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Hello, thanks for opening an issue! We try to keep the github issues for bugs/feature requests.\r\nCould you ask your question on the [forum](https://discusss.huggingface.co) instead?\r\n\r\nThanks!", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Hello, thanks for opening an issue! We try to keep the github issues for bugs/feature requests.\r\nCould you ask your question on the [forum](https://discusss.huggingface.co) instead?\r\n\r\nThanks!" ]

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[ "> Is this intentional? \r\n\r\nYes. It is in line with the original implementation. (Check [link](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) for more information).\r\n\r\n> I ran into two issues - this, and the fact that as per the comment \"RoBERTa does not make use of token type ids, therefore a list of zeros is returned.\" - this comment also doesn't appear correct, from what I can see Roberta's Tokenizer simply does not return token_type_ids I've not figured out why yet\r\n\r\nThe comment is also correct. The original RoBERTa doesn't even have a token_type layer and the huggingface Roberta has one which is just full of zeros (i.e. does nothing as long as you don't do something with it) and only exists due to legacy reasons (check #2871).\r\n\r\n", "Thanks, it appears I can put a BERT tokeniser in front of a RoBERTa mlm model and get why I want.", "You can put every tokenizer in front of RoBERTa, but when you use the pre-trained weights you should stick to the original one as it will otherwise lead to garbage. :)\r\n", "Yeah I understand that but I'm training from scratch." ]

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[ "Did you resize the embeddings after adding the new tokens ? To resize the embedding \r\n```python3\r\nmodel.resize_token_embeddings(len(tokenizer))\r\n```", "> Did you resize the embeddings after adding the new tokens ? To resize the embedding\r\n> \r\n> ```python\r\n> model.resize_token_embeddings(len(tokenizer))\r\n> ```\r\n\r\nThanks so much for your reply!\r\nThe issue has been solved by adding that line.\r\nJust curious, is it a common practice to add 'resize_token_emb' function? because previously, I have not seen this happens and was wondering why it is not included in the official run_summarization code.\r\n\r\nThanks!", "Yes, the embeddings need to be resized after adding new tokens.\r\n\r\nAnd yes, you are right, the embedding should be resized in the example." ]

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[ "There's a lot of changes relative to `black` - could you install the version the repo uses with:\r\n```\r\npip install -U -e .[quality]\r\n```\r\n?\r\n\r\n\r\nAlso I would put this behind a `if is_faiss_available():` rather than a platform check. Could you show the error you obtain on Windows when using an auto model?\r\n", " > Also I would put this behind a `if is_faiss_available():` rather than a platform check. Could you show the error you obtain on Windows when using an auto model?\r\n\r\nhow should that looks like ?\r\nputting faiss import into try catch block ?\r\n\r\nthe error is an \"dll not found error\" on windows\r\n\r\n```\r\nfrom transformers import ViTFeatureExtractor, ViTForImageClassification, AutoModel, AutoTokenizer\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\transformers\\__init__.py\", line 2487, in __getattr__\r\n return super().__getattr__(name)\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\transformers\\file_utils.py\", line 1700, in __getattr__\r\n value = getattr(module, name)\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\transformers\\file_utils.py\", line 1699, in __getattr__\r\n module = self._get_module(self._class_to_module[name])\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\transformers\\models\\auto\\__init__.py\", line 198, in _get_module\r\n return importlib.import_module(\".\" + module_name, self.__name__)\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\importlib\\__init__.py\", line 127, in import_module\r\n return _bootstrap._gcd_import(name[level:], package, level)\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\transformers\\models\\auto\\modeling_auto.py\", line 199, in <module>\r\n from ..rag.modeling_rag import ( # noqa: F401 - need to import all RagModels to be in globals() function\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\transformers\\models\\rag\\modeling_rag.py\", line 29, in <module>\r\n from .retrieval_rag import RagRetriever\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\transformers\\models\\rag\\retrieval_rag.py\", line 42, in <module>\r\n import faiss\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\faiss\\__init__.py\", line 17, in <module>\r\n from .loader import *\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\faiss\\loader.py\", line 39, in <module>\r\n from .swigfaiss import *\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\faiss\\swigfaiss.py\", line 13, in <module>\r\n from . import _swigfaiss\r\nImportError: DLL load failed while importing _swigfaiss: Das angegebene Modul wurde nicht gefunden.\r\n```", "What is your transformer version? \r\nI see this in your stack-trace:\r\n```\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\transformers\\models\\rag\\retrieval_rag.py\", line 42, in <module>\r\n import faiss\r\n```\r\nBut this should be behind the `is_faiss_available()` statement:\r\n\r\nhttps://github.com/huggingface/transformers/blob/db9dd09cf9d8f5de9a5293ec16e7b3d0c01dcbbb/src/transformers/models/rag/retrieval_rag.py#L31-L38", "latest release and master branche\r\nthen it looks like enviroment bug, I dont know which library did, but my pip freeze tells me faiss-cpu is installed on my notebook.\r\nI removed and now it's working again, so closing this PR" ]

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[ "Hi @Aniruddha-JU \r\n\r\nRight now the `run_summarization.py` does not support fine-tuning mBART for summarization, we need to set the proper language tokens for mBART50. For now, you could easily modify the script to adapt it for mBART50 by setting the correct language tokens, as is done in the translation example.\r\n\r\nhttps://github.com/huggingface/transformers/blob/master/examples/pytorch/translation/run_translation.py#L340-L380\r\n\r\nThe difference here would be that the source and target language will be similar.\r\n\r\nAlso, could you please post the full stack trace the error seems unrelated to mBART.", "All the weights of MBartForConditionalGeneration were initialized from the model checkpoint at facebook/mbart-large-50.\r\nIf your task is similar to the task the model of the checkpoint was trained on, you can already use MBartForConditionalGeneration for predictions without further training.\r\n 0%| | 0/3 [00:00<?, ?ba/s]\r\nTraceback (most recent call last):\r\n File \"run_summarization.py\", line 596, in <module>\r\n main()\r\n File \"run_summarization.py\", line 428, in main\r\n train_dataset = train_dataset.map(\r\n File \"/home/aniruddha/anaconda3/envs/mbart/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1474, in map\r\n return self._map_single(\r\n File \"/home/aniruddha/anaconda3/envs/mbart/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 174, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/home/aniruddha/anaconda3/envs/mbart/lib/python3.8/site-packages/datasets/fingerprint.py\", line 340, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \"/home/aniruddha/anaconda3/envs/mbart/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1798, in _map_single\r\n batch = apply_function_on_filtered_inputs(\r\n File \"/home/aniruddha/anaconda3/envs/mbart/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1706, in apply_function_on_filtered_inputs\r\n function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n File \"run_summarization.py\", line 409, in preprocess_function\r\n with tokenizer.as_target_tokenizer():\r\n File \"/home/aniruddha/anaconda3/envs/mbart/lib/python3.8/contextlib.py\", line 113, in __enter__\r\n return next(self.gen)\r\n File \"/home/aniruddha/anaconda3/envs/mbart/lib/python3.8/site-packages/transformers/models/mbart/tokenization_mbart50_fast.py\", line 210, in as_target_tokenizer\r\n self.set_tgt_lang_special_tokens(self.tgt_lang)\r\n File \"/home/aniruddha/anaconda3/envs/mbart/lib/python3.8/site-packages/transformers/models/mbart/tokenization_mbart50_fast.py\", line 235, in set_tgt_lang_special_tokens\r\n prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)\r\n File \"/home/aniruddha/anaconda3/envs/mbart/lib/python3.8/site-packages/transformers/tokenization_utils_fast.py\", line 295, in convert_ids_to_tokens\r\n index = int(index)\r\nTypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'", "@patil-suraj ", "For translation json format is not supporting. core-dumped is happening.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", " with self.tokenizer.as_target_tokenizer():\r\n File \"/home/rahulpal/anaconda3/envs/rebel/lib/python3.7/contextlib.py\", line 112, in __enter__\r\n return next(self.gen)\r\n File \"/home/rahulpal/anaconda3/envs/rebel/lib/python3.7/site-packages/transformers/models/mbart50/tokenization_mbart50_fast.py\", line 215, in as_target_tokenizer\r\n self.set_tgt_lang_special_tokens(self.tgt_lang)\r\n File \"/home/rahulpal/anaconda3/envs/rebel/lib/python3.7/site-packages/transformers/models/mbart50/tokenization_mbart50_fast.py\", line 240, in set_tgt_lang_special_tokens\r\n prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)\r\n File \"/home/rahulpal/anaconda3/envs/rebel/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py\", line 307, in convert_ids_to_tokens\r\n index = int(index)\r\nTypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'\r\n" ]

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[ "Thanks for the very in-detail issue description! @Rocketknight1 do you maybe want to give it a try here? Otherwise I'm happy to take a look :-)", "Taking a look now!", "Hi @cytwill, can you share a few lines of the data you're loading as X_train and y_train? If it's a private dataset, you can replace the text with random text - I just want to see the format of the data and try to reproduce the error here.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", "Hi. I am currently experiencing the same issue as the OP where the classification layer seems to be inserted before the main GPT layer. I basically have the same model summary and a similar error so I thought I'd try to reopen this.\r\n\r\nI know it's not an ideal dataset for the model but here's a copy of the Fine Tuning with Keras tutorial to illustrate the problem: https://colab.research.google.com/drive/1UJdB5QG_6L1qeWxM8Fa-CuDZQR32cshL?usp=sharing\r\n\r\nBelow the tensorflow implementation is the pytorch version that seems to work well enough." ]

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[ "heh not sure why CI failed, just see this error message\r\n\r\n> Received \"killed\" signal", "@sgugger sorry for the double comment (I commented on an old commit by accident), what I meant to say I made the changes you suggested, LMK if this does a good job of conveying the message!", "Thanks again!" ]

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[ "Could you elaborate more? ", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "One More thing i want to mention is below code,\r\nhttps://github.com/huggingface/transformers/blob/ad1f7bef13f03287af00f819605d696138a5e6ec/examples/pytorch/question-answering/run_qa_no_trainer.py#L543-L547\r\ni changed to\r\n```python\r\n eval_dataset.set_format(type=\"torch\", columns=[\"attention_mask\", \"input_ids\"])\r\n eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)\r\n\r\n if args.do_predict:\r\n predict_dataset.set_format(type=\"torch\", columns=[\"attention_mask\", \"input_ids\"])\r\n```\r\nbecause somehow for local files `token_type_ids` was giving an error. at below line\r\nhttps://github.com/huggingface/transformers/blob/ad1f7bef13f03287af00f819605d696138a5e6ec/examples/pytorch/question-answering/run_qa_no_trainer.py#L543\r\nFor the dataset, it was working fine.\r\nWhen I remove the `token_type_ids` script run successfully for both part!", "In the Readme.md of question-answering, i think there is a typo! in below line\r\n```\r\nexport TASK_NAME=mrpc\r\n```", "Hi @sgugger,\r\nI have removed two columns since the eval_dataset is having following features,\r\n```\r\n['attention_mask', 'example_id', 'input_ids', 'offset_mapping']\r\n```\r\nand dataloader also had issue with `offset_mapping`", "Hi @sgugger,\r\nThere is an error in the post_processing of `examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py`\r\n```\r\nTraceback (most recent call last):\r\n File \"transformers/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py\", line 815, in <module>\r\n main()\r\n File \"transformers/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py\", line 746, in main\r\n prediction = post_processing_function(eval_examples, eval_dataset, outputs_numpy)\r\n File \"transformers/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py\", line 577, in post_processing_function\r\n prefix=stage,\r\n File \"/content/transformers/examples/pytorch/question-answering/utils_qa.py\", line 323, in postprocess_qa_predictions_with_beam_search\r\n feature_null_score = cls_logits[feature_index]\r\nIndexError: index 1 is out of bounds for dimension 0 with size 1\r\n100% 9/9 [01:42<00:00, 11.35s/it]\r\n```\r\nIt can be reproduced using this [colab](https://github.com/bhadreshpsavani/UnderstandingNLP/blob/master/PostProcessingErrorInQAWithBeamSearchWithNoTrainer.ipynb),\r\n\r\nWhen I checked i found that `cls_logits` is coming like `tensor([-0.3511])` instead it should have length equal to number of samples (Five in this case) like `[-0.45879194 -0.46871808 -0.3622135 -0.4451167 -0.4400767 ]` \r\n", "Hi @sgugger,\r\nPlease let me know if the above changes don't seem fine.\r\nI think there was a typo earlier it should be like this. Please correct me if I am wrong! ", "Yes, thanks for catching that last problem! I believe the last thing to do is to remove the lines that reset the columns of the `eval_dataset` and `test_dataset` in the post processing ([here](https://github.com/huggingface/transformers/blob/1b0af5f4ed01c227179589722cd658d68f90be6a/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py#L736) and [there](https://github.com/huggingface/transformers/blob/1b0af5f4ed01c227179589722cd658d68f90be6a/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py#L794) in run_qa_beam_search_no_trainer, but they also are in run_qa_no_trainer)", "Sure @sgugger,\r\nI forgot that, Thanks!", "Thanks a lot for your work on this!", "Thank you, @sgugger, for catching parts I missed in my review! Much appreciated!", "Hi!\r\nThere is a typo in line 794 in run_qa_no_trainer.py : \r\n end_logits = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)\r\nwhich should be:\r\n end_logits = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)\r\n\r\nI'm not sure if it has been corrected in the latest version of transformers. I guess it's still there in https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa_no_trainer.py", "Hi @JiaQiSJTU ,\n\nThanks for pointing out,\n\nLet me check on it.", "Hi @sgugger,\r\n\r\nShall I fix this typo?\r\nhttps://github.com/huggingface/transformers/blob/ffd19ee1de36188c6208855160b5ff930caa00c0/examples/pytorch/question-answering/run_qa_no_trainer.py#L794", "Yes please!" ]

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[ "Hi! I believe this checkpoint does not have a slow tokenizer, only a fast tokenizer. Can you try with:\r\n\r\n```py\r\nfrom transformers import RobertaTokenizerFast\r\ntokenizer = RobertaTokenizerFast.from_pretrained('kssteven/ibert-roberta-base')\r\n```", "Thank you! It loaded the tokenizer without showing an error." ]

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[ "When I run code supplied by another user from [another issue](https://github.com/huggingface/transformers/issues/2439), which supposedly worked at one point in time, I get a similar dimension mismatch. Is there a golden combination of tf and transformers I am supposed to be using?", "Ah, the 16 above is the batch size, which must be 16. If I create a dataset with this batch size, then my model with train with the fit function. Like\r\n\r\n`dataset = tf.data.Dataset.from_tensor_slices((x, y)) ` \r\n`dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)`", "Hi, Tensorflow maintainer here! Can you paste me a minimal example that reproduces the problem? You don't have to share your data or anything, just give me a few lines with a made-up tiny dataset that I can run here to recreate the problem - it'll make it much easier for me to track it down. Alternatively, if you're loading your data from HF datasets, that's fine too.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Thanks for the review, yes you've made it a lot more clear for me now:\r\n* it's only `rgb2id` and `id2rgb` which are used from `panopticapi`, and it's only about 20 lines of code I see now. So indeed, we can just copy that code into the library (and cite the authors).\r\n* however, I think it's still useful to have a `task` attribute at the init of `DetrFeatureExtractor`, because then we can do input type checking depending on the task, and it will also be useful regarding postprocessing the outputs of DETR (the task attribute is also done in `LukeTokenizer` for example). \r\n* regarding `config.return_intermediate_layers`, there's indeed to reason for it to be user-facing anymore (it is in the original repo - but for other reasons), so let's remove that from the config.", "Addressed most comments. Once the draft is done, I will create a new branch, squash all commits and open up a new PR, with the remaining comments copied.\r\n\r\n@sgugger I've also added dummies for timm. But CI doesn't seem to be happy, as timm is not installed on it." ]

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[ "Hi there!\r\n\r\nIt would be nice if you ask such questions on the [forum](https://discuss.huggingface.co/). Use issues to report bugs and for feature requests or anything else that can't be discussed on the forum. Thanks!", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Hi @oToToT,\r\nThanks for the PR, The changes seem valid to me. It should be as per changes in this PR.\r\n@sgugger what's your view on these changes?", "Hi, @sgugger\r\nI'm not familiar with how huggingface do with pull requests, did I miss something to make it be merged?\r\nOr I just need to stay and wait it.\r\n\r\nThanks!", "Thanks for the ping! I just forgot to click the merge button 🤦 " ]

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[ "Hi @KMFODA \r\n\r\nThe `generate` method already supports penalizing n-gram repetition, this can be done by passing the argument `no_repeat_ngram_size` , if it's passed it will ensure that all n-grams of the given size will only occur once.\r\n\r\nThis however does not mean that the different `num_returned_sequences` sequences will be diverse since AFAIK in beam search usually the sequences are very close to each other. \r\n\r\nYou could try using beam sampling bypassing `do_sample=True` which will use sampling in each beam, which could introduce some diversity.\r\n\r\nThere's also an option of using [diverse beam search](https://arxiv.org/abs/1610.02424) which can be enabled using the option `diversity_penalty`.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Could you try with the full revision, i.e., `672273686ecedd6fbbd5c0593b17df082ab65e31`?", "same issues @LysandreJik \r\n\r\n```\r\nmodel = pipeline('sentiment-analysis', model='textattack/bert-base-uncased-yelp-polarity',revision=\"672273686ecedd6fbbd5c0593b17df082ab65e31\")\r\n\r\n\r\n404 Client Error: Not Found for url: https://huggingface.co/textattack/bert-base-uncased-yelp-polarity/resolve/main/tf_model.h5\r\nTraceback (most recent call last):\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/transformers/modeling_tf_utils.py\", line 702, in from_pretrained\r\n local_files_only=local_files_only,\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/transformers/file_utils.py\", line 1007, in cached_path\r\n local_files_only=local_files_only,\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/transformers/file_utils.py\", line 1128, in get_from_cache\r\n r.raise_for_status()\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/requests/models.py\", line 940, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/textattack/bert-base-uncased-yelp-polarity/resolve/main/tf_model.h5\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/transformers/pipelines.py\", line 2936, in pipeline\r\n framework = framework or get_framework(model)\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/transformers/pipelines.py\", line 108, in get_framework\r\n model = TFAutoModel.from_pretrained(model, revision=revision)\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/transformers/models/auto/modeling_tf_auto.py\", line 561, in from_pretrained\r\n pretrained_model_name_or_path, *model_args, config=config, **kwargs\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/transformers/modeling_tf_utils.py\", line 711, in from_pretrained\r\n raise EnvironmentError(msg)\r\nOSError: Can't load weights for 'textattack/bert-base-uncased-yelp-polarity'. Make sure that:\r\n\r\n- 'textattack/bert-base-uncased-yelp-polarity' is a correct model identifier listed on 'https://huggingface.co/models'\r\n\r\n- or 'textattack/bert-base-uncased-yelp-polarity' is the correct path to a directory containing a file named one of tf_model.h5, pytorch_model.bin.\r\n\r\n```\r\n```\r\n>>> model = pipeline('sentiment-analysis', model='textattack/bert-base-uncased-yelp-polarity')\r\n\r\n\r\n404 Client Error: Not Found for url: https://huggingface.co/textattack/bert-base-uncased-yelp-polarity/resolve/main/tf_model.h5\r\nTraceback (most recent call last):\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/transformers/modeling_tf_utils.py\", line 702, in from_pretrained\r\n local_files_only=local_files_only,\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/transformers/file_utils.py\", line 1007, in cached_path\r\n local_files_only=local_files_only,\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/transformers/file_utils.py\", line 1128, in get_from_cache\r\n r.raise_for_status()\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/requests/models.py\", line 940, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/textattack/bert-base-uncased-yelp-polarity/resolve/main/tf_model.h5\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/transformers/pipelines.py\", line 2936, in pipeline\r\n framework = framework or get_framework(model)\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/transformers/pipelines.py\", line 108, in get_framework\r\n model = TFAutoModel.from_pretrained(model, revision=revision)\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/transformers/models/auto/modeling_tf_auto.py\", line 561, in from_pretrained\r\n pretrained_model_name_or_path, *model_args, config=config, **kwargs\r\n File \"/home/tiru/anaconda3/lib/python3.7/site-packages/transformers/modeling_tf_utils.py\", line 711, in from_pretrained\r\n raise EnvironmentError(msg)\r\nOSError: Can't load weights for 'textattack/bert-base-uncased-yelp-polarity'. Make sure that:\r\n\r\n- 'textattack/bert-base-uncased-yelp-polarity' is a correct model identifier listed on 'https://huggingface.co/models'\r\n\r\n- or 'textattack/bert-base-uncased-yelp-polarity' is the correct path to a directory containing a file named one of tf_model.h5, pytorch_model.bin.\r\n\r\n````", "Ah, I think the error message could be clearer here; what I understand from this error is that you don't have PyTorch installed in your environment - only TensorFlow; however, that model does not have a TensorFlow checkpoint uploaded to the hub by `textattack`, only a PyTorch variant, so it fails at loading it.\r\n\r\nCould you install torch in your environment so as to benefit from the torch model? `pip install torch`\r\n\r\nRunning your code should work fine after this.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "That's correct. \r\n\r\nEarlier I tried to use `cur_version>pre_version` so it'd already work with master version, but then people were reporting bugs because they were on some older master version, so while this is less convenient, it avoids invalid bug reports and saves time to all ;)\r\n\r\n " ]

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[ "\"unuglify\" -> best branch name ever haha" ]

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[ "Hey,\r\n\r\nCould you leave more details:\r\n- Your environment\r\n- Your code(or edited so i can try it)\r\n\r\nLooking here:\r\nhttps://huggingface.co/transformers/model_doc/mbart.html?highlight=config#transformers.MBartConfig\r\n\r\nIt doesnt seem to be using hidden_states. Depending how you use the model, you may be grabbing its output incorrectly.", "Thanks.\r\nI'm using python 3.7, torch 1.7.1 and installed transformers from the source (4.6.0.dev0).\r\nI'm following the exact implementations from [here](https://colab.research.google.com/drive/1WIk2bxglElfZewOHboPFNj8H44_VAyKE?usp=sharing), with minor edits:\r\n```\r\nmodel = MBartForConditionalGeneration.from_pretrained(\"facebook/mbart-large-50\")\r\ntokenizer = MBart50TokenizerFast.from_pretrained(\"facebook/mbart-large-50\", src_lang=\"cs_CZ\", tgt_lang=\"cs_CZ\")\r\n```\r\nchanged the function _process_data_to_model_inputs_\r\n```\r\ndef process_data_to_model_inputs(batch):\r\n # tokenize the inputs and labels\r\n inputs = tokenizer(batch['src'], padding=True, truncation=True, return_tensors=\"pt\")\r\n with tokenizer.as_target_tokenizer():\r\n outputs = tokenizer(batch['tgt'], return_tensors=\"pt\", padding=True, truncation=True)\r\n labels = outputs.input_ids.clone()\r\n data = TensorDataset(torch.tensor(inputs['input_ids']), torch.tensor(inputs['attention_mask']),\r\n torch.tensor(outputs['input_ids']), torch.tensor(outputs['attention_mask']),\r\n torch.tensor(labels))\r\n\r\n dataloader = DataLoader(data, batch_size=batch_size)\r\n return dataloader\r\n\r\n\r\n```\r\nand then training:\r\n```\r\nbart2bart = EncoderDecoderModel.from_encoder_decoder_pretrained(\"facebook/mbart-large-50\", \"facebook/mbart-large-50\")\r\n\r\nfor i in range(EPOCH):\r\n bart2bart.train()\r\n for step, batch in enumerate(train_data):\r\n batch = tuple(t.to(device) for t in batch)\r\n b_input_ids, b_attention_masks_enc, b_input_ids_de, b_attention_masks_de, b_labels= batch\r\n outputs = bart2bart(input_ids=b_input_ids, attention_mask=b_attention_masks_enc,\r\n labels=b_labels, decoder_input_ids=b_input_ids_de, decoder_attention_mask=b_attention_masks_de)\r\n loss, logits = outputs.loss, outputs.logits\r\n optimizer.zero_grad()\r\n bart2bart.zero_grad()\r\n loss.backward()\r\n optimizer.step()\r\n \r\n```\r\nI'm very new to this, so I'm probably not using the model correctly as you mentioned. But I'm not sure how to fix it.", "Hey,\r\n\r\nUnfortunately i dont use torch, just tensorflow functional API. However i did note that for EncoderDecoder there can be a special configuration procedure. See here:\r\nhttps://huggingface.co/transformers/model_doc/encoderdecoder.html#transformers.EncoderDecoderConfig\r\n\r\nIt is possible that the default config doesn't behave well with MBart as it does with Bert(they are significantly different).\r\n\r\nTry passing in the configs for your encoder and decoder (both MBart) or load config from pretrained, there is example code in the above link. It certainly an error in what the decoder expects.", "I tried this, thanks!\r\nThe issue still remains though...it's not working.\r\n@patrickvonplaten any tips for using mbart for an Encoder-Decoder Model based on your example notebook for bert?", "Hey,\r\n\r\nFair enough, one last thing id note is:\r\n\"The EncoderDecoderModel can be used to initialize a sequence-to-sequence model with any pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder.\"\r\nfrom https://huggingface.co/transformers/model_doc/encoderdecoder.html\r\n\r\nI am not sure BART can be used for this. ", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Hi guys, just one comment/question from my side: instead of introducing new commandline options (allowing to resume from a checkpoint), what do you think about the following workflow: \r\n\r\na) load the model (with AutoModel)\r\nb) check if the loaded model is from type \"TokenClassification\"\r\n\r\n:thinking: ", "> I'm also thinking - if we know we are going to resume from checkpoint for sure, then replace from_pretrained with from_config and save the double loading of the weights?\r\n\r\nI'm leaving this to your PoC on one of the seq2seq scripts if you don't mind? This PR is also kind of an urgent bug fix.", "Oh, of course, I had no idea about the urgency. I will do it in another PR then." ]

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[ "I think you should replace `transformer_outputs = transformer[1]` with `transformer_outputs = transformer[0]`. ", "> I think you should replace `transformer_outputs = transformer[1]` with `transformer_outputs = transformer[0]`.\r\n\r\n@fredo838 Thanks for the reply, yes i have tried this -- in this case the output shape is (None,4096,768) as per config. I tried taking 64 of these entries but i get the exact same error, in a slightly different format.", "how about also changing `[transformer_outputs[i] for i in hiddes_states_ind]` to `[transformer_outputs[:, i] for i in hiddes_states_ind]` (that way you index the token dimension, not the batch dimension)", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Shouldn't this only be installed for python versions inferior to 3.8?", "@LysandreJik, thanks for reviewing my PR.\r\n\r\nI think at the moment, we cannot use selectors with `noarch` python packages. Please see warning at - https://docs.conda.io/projects/conda-build/en/latest/resources/define-metadata.html?highlight=preprocess-selectors#architecture-independent-packages. ", "I see! Let's go with it then. Thanks for the fix!", "Could you open a PR against the `master` branch so that we can also apply that fix for future conda releases?", "Also, are you savvy about `conda`? We're having an issue with our recent version releases: [failing suite](https://github.com/huggingface/transformers/runs/2334821335?check_suite_focus=true), do you have an idea of what might be the conflict happening here?", "Thanks @LysandreJik for merging this one. Yes I will open a PR against master too. ", "Added PR https://github.com/huggingface/transformers/pull/11591 for master branch.", "> Also, are you savvy about `conda`? We're having an issue with our recent version releases: [failing suite](https://github.com/huggingface/transformers/runs/2334821335?check_suite_focus=true), do you have an idea of what might be the conflict happening here?\r\n\r\nI think this is failing because `python` version in the build env is `3.9` and we do not have `tokenizers` for `py39` on `HuggingFace` channel. \r\n```\r\n$ conda search tokenizers=0.10.2 -c HuggingFace\r\nLoading channels: done\r\n# Name Version Build Channel\r\ntokenizers 0.10.2 py35_0 HuggingFace\r\ntokenizers 0.10.2 py36_0 HuggingFace\r\ntokenizers 0.10.2 py37_0 HuggingFace\r\ntokenizers 0.10.2 py38_0 HuggingFace\r\n```\r\nLooks like anaconda upload failed for tokenizers 3.9 - https://github.com/huggingface/tokenizers/runs/2272898351#step:8:17\r\n\r\nI was able to build transformers locally with py38. ", "Ah, I thought we were already building on 3.8, that's my bad. Thanks for your help!" ]

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[ "Hi! The model is working fine here, but the problem is that \"[MASK]\" and \"Paris\" are being tokenized as different numbers of tokens, which is where your shape error is coming from. Can you link me to the exact script you got this example from?", "It's under this headline, here's the permalink: https://huggingface.co/transformers/model_doc/longformer.html#tflongformerformaskedlm", "ah so it's probably just updating `inputs[\"labels\"] = tokenizer(\"The capital of France is Paris.\", return_tensors=\"tf\")[\"input_ids\"]\r\n` to `inputs[\"labels\"] = tokenizer(\"The capital of [MASK] is Paris.\", return_tensors=\"tf\")[\"input_ids\"]`, no?", "I checked and you're absolutely right, the example as written does not work. I did some digging and the problem is that the mask sequence for this model is actually '\\<mask\\>' and not '[MASK]'. Therefore, 'Paris' actually does get correctly tokenized as one token but '[MASK]' does not get recognized as a special character and is 'spelled out' with three word-piece tokens instead. (You can see what splits the tokenizer chose by using `tokenizer.convert_ids_to_tokens()` on the tokenized inputs).\r\n\r\nThe example should work if you replace '[MASK]' with '\\<mask\\>'. Can you try that and let me know? If it works, we can make a PR to fix this example!", "So now the following example:\r\n\r\n```from transformers import LongformerTokenizer, TFLongformerForMaskedLM\r\nimport tensorflow as tf\r\ntokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')\r\nmodel = TFLongformerForMaskedLM.from_pretrained('allenai/longformer-base-4096')\r\ninputs = tokenizer(\"The capital of France is <mask>.\", return_tensors=\"tf\")\r\ninputs[\"labels\"] = tokenizer(\"The capital of France is Paris.\", return_tensors=\"tf\")[\"input_ids\"]\r\noutputs = model(inputs)\r\nloss = outputs.loss\r\nlogits = outputs.logits\r\npreds = tf.argmax(logits, axis=2)\r\npredicted_tokens = tokenizer.convert_ids_to_tokens(tf.squeeze(preds))\r\nprint(\"predicted_tokens: \", predicted_tokens)\r\n```\r\n\r\nyields:\r\n\r\n`['<s>', 'The', 'Ġcapital', 'Ġof', 'ĠFrance', 'Ġis', 'ĠParis', '.', '</s>']`\r\n\r\nSo at least we're doing something right, but there's still this weird `Ġ` character on every non-first token.", "Ah, yes! The Ġ character is used to indicate word breaks. If you want to see the pure string output without it, try using the `decode()` method instead of `convert_ids_to_tokens()`.\r\n\r\nOther than that, though, your example looks good! I talked with people on the team and we can't use it directly, annoyingly - the examples are all built from the same template, so we can't easily change just one. Still, we can pass some arguments to make sure our example works for Longformer in future.\r\n\r\nThe relevant bit is [here](https://github.com/huggingface/transformers/blob/master/src/transformers/models/longformer/modeling_tf_longformer.py#L2080). If you'd like to try it yourself, you can submit a PR to add the argument `mask='<mask>'` to the `add_code_sample_docstrings` decorator. If that sounds like a lot of work, just let me know and I'll make the PR and credit you for spotting it!", "@Rocketknight1 I added a PR (https://github.com/huggingface/transformers/pull/11559)", "Closing this because we have the PR now!" ]

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[ "try the newest version 4.6.0.dev0\r\n\r\n\r\n\r\n", "Could you install `sentencepiece` and try again? The `PegasusTokenizer` is based on the `sentencepiece` library.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", "Iam also facing the same issue" ]

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[ "Thanks! Failure in the tests is unrelated (some connection problem), so merging." ]

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[ "The command runs for me and according to your logs, the `Trainer` is loading a local checkpoint named `roberta-base`. Do you have a local folder named `roberta-base`? It looks like it contains a checkpoint different from the actual `roberta-base` model, which messes up and creates the error. Could you move that folder and try again?", "@sgugger \r\nYes, I create a local folder named `roberta-base`, but the `roberta-base` folder contents is download from `huggingface` (https://huggingface.co/roberta-base/tree/main)\r\n\r\nthe `language-modeling` folder screenshot as shown below:\r\n\r\n\r\nthe `roberta-base` folder screenshot as shown below:\r\n\r\n\r\nso i am confused...", "I think it's linked to the bug #11492 is fixing. Should be merged today and then you can try on a source install!" ]

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[ "I can load the model without any issues, I think the issue here is the `from_pretrained` call is hitting the cache and the model is not cached properly. You could force the download by passing `force_download=True`\r\n\r\n```python\r\nmodel = MBartForConditionalGeneration.from_pretrained(\"facebook/mbart-large-50\", force_download=True)\r\n```", "I am also still facing the problem, can you please mention if there is verson specific", "but is is successfully loading for BART-large model\r\n ", "You could try deleting the cache in that case.", "Not happening aging, same error is coming\r\n", "See this [colab](https://colab.research.google.com/drive/1ENrFbZIxmK0ZtrtUduCADEZDZtg_QKZT?usp=sharing) it uses 4.5.0 and can load mbart. ", "> I can load the model without any issues, I think the issue here is the `from_pretrained` call is hitting the cache and the model is not cached properly. You could force the download by passing `force_download=True`\r\n> \r\n> ```python\r\n> model = MBartForConditionalGeneration.from_pretrained(\"facebook/mbart-large-50\", force_download=True)\r\n> ```\r\n\r\nHi, do you know how to run transformer model like t5-small, facebook/bart-large-cnn without loading pre-trained weights? When using run_summarization.py, I only want to train their original model architecture without pre-trained model. Thank you very much!\r\n", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Hi there,\r\n\r\nPlease use the [forum](https://discuss.huggingface.co/) to ask these types of questions, use issues to report bugs or feature requests.\r\n\r\nThanks!", "> Hi there,\r\n> \r\n> Please use the [forum](https://discuss.huggingface.co/) to ask these types of questions, use issues to report bugs or feature requests.\r\n> \r\n> Thanks!\r\n\r\nOk I will. Thank you!" ]

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[ "The problem lies mostly on the model page in that case, it should show the text classification widget, not the masked LM widget. As for adding a model card or expanding its capabilities, the feedback should go on the [forums](https://discuss.huggingface.co/), this is not really an issue with transformers per se.\r\n\r\nThis is a great example of where pull requests on the model hub would be useful! (cc @julien-c )", "Ok thanks I'll close this issue and open an appropriate PR/Issue in other places. I'll try to find where to update the filter and I'll put the model card on my todo list. \n\nThanks for the pointers", "For anyone that finds this issue:\r\n\r\n - [here is the forum post that describes how to suggest model cards](https://discuss.huggingface.co/t/about-the-model-cards-category/2777)\r\n\r\n- For clarification `bert-base-cased-finetuned-mrpc` does indeed do masked LM but it additionally also does text classification (I tried doing both). But, it looks like models on the hub can only associated with one widget at a time? so It could be the case that models have hidden functionality, or does this particular model violate some kind of norm? ", "- @hamelsmu The model card for this model should a minima reference the `mrpc` dataset, though we don't have it as a standalone dataset so the way to go would be to link to `glue` instead. (right @lhoestq?)\r\n- you can also add a `tags: - paraphrase-classification ` to the YAML (tags are pretty much open)\r\n- read the doc about the model hub here http://huggingface.co/docs (should it be linked more prominently from the transformers doc?)\r\n- As this is a \"legacy\" model (not inside an organization), it's hard to remember who trained it and therefore could answer more questions (the original BERT authors? Someone from HF?)\r\n- To your last question, most models only have one head so one task – for simplicity we enforce this constraint of only having one widget or Inference API endpoint per model", "Yes for now we have to link to `glue`.\r\nThough I've noticed that many models use the `mrpc` tag that doesn't link to glue\r\n\r\nMaybe we can define a syntax that mentions both glue (to link to the glue dataset page) and MRPC (to mention which config if the glue dataset was used). Maybe `glue/mrpc` or `glue:mrpc`. Shall I open an issue on the website repo about this @julien-c ?" ]

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[ "Hi, thank you for the report! I'm examining it now and I'm hoping to push some updates to that script today.", "I haven't been able to reproduce this issue, but I have a PR open to make modifications to the script. I'll let you know as soon as the updated version is available - would you be willing to check if the issue is still there once it is?", "The script has been updated! Please let me know if you encounter the same problems.", "@Rocketknight1 Thank you i am able to run predictions and it gives correct prediction for trained data.. \r\n\r\nHere is my Wish List if you can provide:\r\nI am trying to integrate run_text_classification.py to my program where i will provide it a sentence and it gives the prediction as per labelling. and my program uses that label for something useful.\r\n\r\n1. It writes to a file the prediction result, will that be possible if I import run_text_classification.py in my program and you exposed a function which takes input list of strings for sentences to classify with all other parameters required to run. And returns list of strings with predictions in same order. That way i do not have to read a file always for the result.\r\n\r\n2. Default fallback label: Meaning if i passed a sentence for classification, if model is not able to classify as per trained labelled data, it returns the Default fallback label set by user. Tat way i can know for which sentences i have to retrain the model\r\n\r\n@Rocketknight1 thanks in advance\r\n\r\nRajesh Dhiman\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "Hm! Number 2 in particular is a fairly advanced ML topic - getting models to know which inputs they can and can't classify accurately is surprisingly hard. This is a fairly fundamental problem that people are still writing papers about, and not one we can really tackle well in an introductory example.\r\n\r\nYour suggestion for 1) is certainly possible, though, and we'll think about it! Our intention isn't to support every possible use case with the examples, though! We really just want to show one working example that shows off a lot of the features of the library, and we expect that users will have to modify the code themselves in a lot of cases.", "@Rocketknight1 \r\nHi \r\nHow I can get the Confidence score in the prediction results.. i need to have that, Is there any option i can set in settings ", "@Rocketknight1\r\ni got it.. Sorry it was a dumb Question.. I am a nerd..", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "This was written by @julien-c so I'll let him reply on this :-)", "This was written before we moved from S3 to our own Cloudfront-served repositories so we could also probably just remove that paragraph.", "Interesting. Maybe it could _still_ be a good reminder as many folks would forget to do this (I know I might have and have wasted so much of my own compute!)?\r\n\r\nHowever, I'll open a PR to remove the paragraph if that's preferred 🙇🏽 " ]

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[ "Wow this sounds awesome! Let me know if you need help! (I think we will need to look at the `generate()` function) together :-)", "Hi @patrickvonplaten, as I indicated I've started working on `FlaxBart` which can be found on this branch https://github.com/stancld/transformers/tree/FlaxBart . So far, I've implemented `FlaxBartModel` and `FlaxBartForConditionalGeneration` with some remaining pieces to do, but it is possible to run them. \r\n\r\nAs there is no official template for Flax encoder-decoder models, I've tried to follow Torch implementation of Bart and Flax implementation of Bert. Before diving deeper and finishing all the components, tests etc, could I, please, ask you to provide me with short feedback if this structure seems ok to you? Thanks a lot in advance! :)\r\n(I guess I left some redundant code there but I'm gonna polish it soon)", "This sounds great :-) Thanks a lot for tackling this! Could you maybe make a [WIP] PR from your branch and ping me - this would make it a bit easier to review the code", "Hey @stancld, \r\n\r\nI looked quickly and in general the PR already looks great :-) \r\n\r\nA couple of things:\r\n- we don't allow `labels` as an input argument to Flax models (and actually probably even won't do this in the future). Flax/Jax is inherently functional which means that a loss function should wrap the model forward function and not the other way around\r\n- Weight tying is done a bit differently as in PyTorch, thus we don't need ` def get_input_embeddings(self):` for now\r\n- `return_dict` is now also implemented in `FlaxBert` -> so this can be copied from there :-) \r\n\r\n=> In short the design already looks great :-) I think you can open a PR & we'll discuss everything on the PR ", "@patrickvonplaten Thanks a lot for the feedback! I will create [WIP] PR later today :)", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", "ping" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "@LysandreJik @patrickvonplaten\r\n\r\nForgot to add you for review, my bad." ]

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[ "Let's also:\r\n1. add some basic usage doc - we can start with just docstring\r\n2. add one test for one of the models, polish it and then see how to replicate it for other models.", "My understanding was that this is experimental and as we start using this side of library we will generalize and improve things. Hence the more slack approach.\r\n\r\nSame for tests, I thought it was good to start with unique tests because the workarounds are unique and then over time as more models are ported to come up with common tests. \r\n\r\n@michaelbenayoun, one way to approach this puzzle is to create common tests for what's the same in all of them, and if something is unique to a given model then have just that tested in that model's test file. If you need help with that, please don't hesitate to ask.\r\n", "Even for experimental features like model parallelism, we are using common tests. This should not be different IMO.", "@sgugger, Michael merged the custom tests into common_tests and significantly simplified the mods to the models - yay!\r\n\r\nSo it looks ready for your review whenever you have a chance. \r\n\r\nThank you!", "Sorry for jumping in. \r\nOut of curiosity, what is the scenario to use this symbolic tracing feature? Didn't find any example/doc...\r\nThanks.", "Well, I initially wanted this in order to be able to try https://github.com/flexflow/FlexFlow, which requires symbolic tracing - but I haven't had a chance to do so yet.", "Got it, thanks for the explanation.", "> Sorry for jumping in.\r\n> Out of curiosity, what is the scenario to use this symbolic tracing feature? Didn't find any example/doc...\r\n> Thanks.\r\n\r\nThis would be also be helpful to quantize models using [ FX Graph Mode Quantization](https://pytorch.org/docs/stable/quantization.html?highlight=quantization) which automate the quantization process in Pytorch. ", "Are these updates still functional currently? As no modeling_fx_utils.py can be seen in the source code directory. " ]

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[ "Could you please post more details and a code snippet? \r\n\r\nTo import a modeling use\r\n```python\r\nfrom transformers.models.mbart import modeling_mbart\r\n```" ]

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[ "Please follow the issue template." ]