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DanielPFlorian

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Transformers-Github-Issues

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[ "Hi @BigBird01, thanks a lot for fixing these issues! I'd like to prevent as much as possible from having the `pre_load_hooks` in the code. When do you expect mismatched head dimensions?", "This is the case that if we want to fine-tune a task based on mnli models, e.g. MRPC, SST, QNLI. If we want to avoid this method, we need to fix the error reporting when load pretrained models.\n\nGet Outlook for iOS<https://aka.ms/o0ukef>\n________________________________\nFrom: Lysandre Debut <[email protected]>\nSent: Saturday, February 13, 2021 5:23:53 AM\nTo: huggingface/transformers <[email protected]>\nCc: Pengcheng He <[email protected]>; Mention <[email protected]>\nSubject: Re: [huggingface/transformers] Fix v2 model loading issue (#10129)\n\n\nHi @BigBird01<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2FBigBird01&data=04%7C01%7CPengcheng.H%40microsoft.com%7C301190f89ac34907b53408d8d0229db7%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637488194400010761%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=9%2BnuKLc3nPym5xSUhe%2Flko3Fd3jxus0WRwj3W%2Bks%2FuM%3D&reserved=0>, thanks a lot for fixing these issues! I'd like to prevent as much as possible from having the pre_load_hooks in the code. When do you expect mismatched head dimensions?\n\n—\nYou are receiving this because you were mentioned.\nReply to this email directly, view it on GitHub<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fhuggingface%2Ftransformers%2Fpull%2F10129%23issuecomment-778618852&data=04%7C01%7CPengcheng.H%40microsoft.com%7C301190f89ac34907b53408d8d0229db7%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637488194400010761%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=ka8Oo2xj%2Ba0u2X6Fobb77H27KO94ePzlbCeZ7a797ww%3D&reserved=0>, or unsubscribe<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FAJDNDRT5T5MFRSCQH3MXIEDS6Z4OTANCNFSM4XOC2W6A&data=04%7C01%7CPengcheng.H%40microsoft.com%7C301190f89ac34907b53408d8d0229db7%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637488194400020711%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=kdOpjyJfnJyaSLKNbPjNIMs3p2KbiceAVHHS4WCfVEA%3D&reserved=0>.\n", "Usually we recommend to load through the base model to lose the head:\r\n\r\n```py\r\nfrom transformers import DebertaV2Model, DebertaV2ForSequenceClassification\r\n\r\nseq_model = DebertaV2ForSequenceClassification.from_pretrained(\"xxx\", num_labels=4)\r\nseq_model.save_pretrained(directory)\r\n\r\nbase = DebertaV2Model.from_pretrained(directory) # Lose the head\r\nbase.save_pretrained(directory)\r\n\r\nseq_model = DebertaV2ForSequenceClassification.from_pretrained(directory, num_labels=8)\r\n```\r\n\r\nDoes that work in your case? I agree you're touching to something that has a bad API, and this should be handled in the `from_pretrained` method. I don't think we should handle it model-wise, however. I'll look into it soon.", "yes. but this looks a little bit tricky. And need to modify existing text classification code to benefit from mnli fine-tuned models. How about we keep current hook method, and finish current PR? After we work out a systematic solution for such scenario, we can drop the hook method.\n\nGet Outlook for iOS<https://aka.ms/o0ukef>\n________________________________\nFrom: Lysandre Debut <[email protected]>\nSent: Saturday, February 13, 2021 6:28:34 AM\nTo: huggingface/transformers <[email protected]>\nCc: Pengcheng He <[email protected]>; Mention <[email protected]>\nSubject: Re: [huggingface/transformers] Fix v2 model loading issue (#10129)\n\n\nUsually we recommend to load through the base model to lose the head:\n\nfrom transformers import DebertaV2Model, DebertaV2ForSequenceClassification\n\nseq_model = DebertaV2ForSequenceClassification.from_pretrained(\"xxx\", num_labels=4)\nseq_model.save_pretrained(directory)\n\nbase = DebertaV2Model.from_pretrained(directory) # Lose the head\nbase.save_pretrained(directory)\n\nseq_model = DebertaV2ForSequenceClassification.from_pretrained(directory, num_labels=8)\n\nDoes that work in your case? I agree you're touching to something that has a bad API, and this should be handled in the from_pretrained method. I don't think we should handle it model-wise, however. I'll look into it soon.\n\n—\nYou are receiving this because you were mentioned.\nReply to this email directly, view it on GitHub<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fhuggingface%2Ftransformers%2Fpull%2F10129%23issuecomment-778627044&data=04%7C01%7CPengcheng.H%40microsoft.com%7C4e6ebb9592e84114db8908d8d02ba69b%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637488233183179214%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=6B7Tw%2BulDyc50hCWtt40Y3Eu%2F9pDhycqsFcGGlbEWJA%3D&reserved=0>, or unsubscribe<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FAJDNDRSALWZ5T6MA5DFF7VDS62EBFANCNFSM4XOC2W6A&data=04%7C01%7CPengcheng.H%40microsoft.com%7C4e6ebb9592e84114db8908d8d02ba69b%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637488233183179214%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=DUoGLBFe%2Ff2Ms580JCNn5okYXrYWH9vqTIhAMve0tqE%3D&reserved=0>.\n", "Ok, will merge like that and we'll discuss with other team members for the main PR. Thanks!" ]

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[ "Indeed. Would you like to do a PR to fix this since you spotted the bug?", "sure thing!", "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! This is weird, it shouldn't happen. Could you try to install `sentencepiece` and let me know if it fixes your issue? Thank you!", "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.", "I'm still facing this issue.\r\n\r\n\r\n", "Do you have a reproducible colab notebook? Thanks", "Not sure how but it's working today.\r\n\r\n", "This is probably because you hadn't restarted your kernel after installing the `sentencepiece` dependency!" ]

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[ "Thank you for your work @lordtt13 \r\n\r\nActually, there are already plenty of notebooks about how to fine-tune T5. It would be great if could add a notebook for missing/new models/task. Below are some of the new models which aren't used much\r\n\r\n- T5_v1_1, mT5\r\n- ProphetNet, xlm-prophetnet\r\n- Blenderbot\r\n- mBART, mBART-50\r\n\r\nUsing languages other than English would be even better, we now have so many languages in the `datasets` library after the sprint. So it's a good opportunity to use those datasets to fine-tune/evaluate multi-lingual models on them (mT5, mBART, xlm-prophetnet) \r\n\r\ncc @patrickvonplaten ", "I agree with @patil-suraj that notebooks on multilingual T5 would be super useful as well! \r\n\r\nBut nevertheless, I think we can merge this notebook :-) ", "Thank you for the suggestions, and yes maybe T5 has been trained on too much, I will change the notebook to have it train a different model and then request for merge.", "Have trained now on BlenderBotSmall, will add multilingual model training tutorial in next PR! \r\nPlease check @patil-suraj @patrickvonplaten " ]

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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.", "> I converted pretrained 'Rostlab/prot_bert_bfd' @ huggingface to onnx, then tried to convert a checkpoint from fine tuning of the pretrained model. Comparing to pretrained model, conversion of fine tuned model generated a lot of warnings. Basically the warnings said that parameters from first to last layer were not initialized. The converted model did not work correctly.\r\n> \r\n> This is how I called the conversion module:\r\n> \r\n> python3 -m transformers.convert_graph_to_onnx --model Rostlab/prot_bert_bfd --framework pt prot_bert_bfd.onnx\r\n> I did similarly for checkpoint model.\r\n> \r\n> Does the module work on checkpoint?\r\n\r\nHello @yzhang-github-pub ,I have used the same line of code as you did for a PEGASUS model on colab:\r\n`!python3 -m transformers.convert_graph_to_onnx --model jpcorb20/pegasus-large-reddit_tifu-samsum-256 --framework pt pegasus-large-reddit_tifu-samsum-256.onnx`\r\n**The following error keeps showing and the coversion fails. Can you please tell me how did you solve this problem??**\r\n**The error:**\r\n\r\n> Some weights of the model checkpoint at jpcorb20/pegasus-large-reddit_tifu-samsum-256 were not used when initializing PegasusModel: ['final_logits_bias', 'lm_head.weight']\r\n\r\n> - This IS expected if you are initializing PegasusModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n> - This IS NOT expected if you are initializing PegasusModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\n> Downloading: 100% 1.50k/1.50k [00:00<00:00, 1.29MB/s]\r\n> Downloading: 100% 1.91M/1.91M [00:01<00:00, 1.12MB/s]\r\n> Downloading: 100% 1.34k/1.34k [00:00<00:00, 1.18MB/s]\r\n> Error while converting the model: Folder /content is not empty, aborting conversion", "I wrote a script to do onnx conversion, by importing onnx and onnxruntime modules. I heard some versions of transformer have bugs in onnx conversion and model loading. \r\n" ]

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[ "I had to define the following parameters on `BertConfig`:\r\n\r\n`config = BertConfig(vocab_size=4+1, hidden_size=len(x), max_position_embeddings=len(x))`\r\n\r\nMy issue is now:\r\n\r\n`InvalidArgumentError: Index out of range using input dim 1; input has only 1 dims [Op:StridedSlice] name: tf_bert_model_8/bert/strided_slice/`", "The input to BERT (`input_ids`) must be a tensor of shape `(batch_size, sequence_length)` = `(1, 1200)`.\r\n\r\nHowever, currently the shape of your x is only `(1200,`). You should add a batch dimension, like so:\r\n\r\n```\r\nimport numpy as np\r\n\r\nx = np.random.randint(1,5,1200)\r\nx = np.expand_dims(x, axis=0)\r\n```\r\n\r\nThe embedding layer of BERT will then turn this into a tensor of shape `(batch_size, sequence_length, hidden_size)`, i.e. it will turn each of the 1200 integers into a vector of size `hidden_size`. As you're setting `hidden_size` equal to `len(x)`, this means that each integer will be turned into a vector of size 1200. Is this what you want (seems quite a big vector :p)?\r\n\r\nThen the following will work:\r\n\r\n```\r\nfrom transformers import TFBertModel, BertConfig\r\n\r\nconfig = BertConfig(vocab_size=4+1, max_position_embeddings=len(x))\r\nmodel = TFBertModel(config)\r\n\r\nmodel(x)\r\n```\r\n\r\nBtw, please ask questions which are not bugs or feature requests on the [forum](https://discuss.huggingface.co/) rather than here.", "Thank you @NielsRogge. I must apologize. Should I cut this issue and paste into the forum?\r\n\r\nI think I made a mistake... the vector size should not be that large and won't fit into memory :P\r\n\r\nI managed to complete a model forward cycle. But, if you allow me another question:\r\n\r\nShould I add an extra integer to the beginning of the sequence (of value != to the existing ones, e.g.: `5`), to act as the <CLS> token?\r\n\r\nI am asking this because the `model(inputs)` returns a pooled `(batch_size, 1,hidden_size)` token, instead of `(batch_size, seq_length, hidden_size)`, however I am not sure if I should be passing this to the `Dense(1)` layer in the next step.", "> Should I add an extra integer to the beginning of the sequence (of value != to the existing ones, e.g.: `5`), to act as the token?\r\n\r\nEach integer acts as a token, so adding one more will increase the number of tokens by one. The `vocab_size` is the total number of tokens for which the model learns an embedding vector.\r\n\r\n> I am asking this because the `model(inputs)` returns a pooled `(batch_size, 1,hidden_size)` token, instead of `(batch_size, seq_length, hidden_size)`, however I am not sure if I should be passing this to the `Dense(1)` layer in the next step.\r\n\r\nIf you use `TFBertModel`, then by default it returns a `TFBaseModelOutputWithPooling` object, with an attribute called `last_hidden_state`. This is a tensor of shape `(batch_size, sequence_length, hidden_size)` and this is probably what you want.\r\n\r\nYou may close the issue and if you have any further questions, feel free to ask them on the forum, we're happy to help!" ]

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[ "Discussed offline with @n1t0: our current decision is to wait for https://github.com/huggingface/tokenizers/issues/659 to be resolved before moving on with this issue. \r\nThis is the better tradeoff as the alternative would imply duplicating a lot of logic in `transformers` that's already present but not exposed by `tokenizers`." ]

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[ "Thank you @LysandreJik, this will alleviate much of the pain Windows users had with git in the past!", "To hack my way up the contributors list, can I change all line endings to CRLF then revert? 😂 " ]

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[ "Hi!\r\nTry this one, but do it from an empty folder:\r\n`python3 -m transformers.convert_graph_to_onnx --framework pt --model bert-base-cased bert-base-cased.onnx`", "@Denovitz Thanks a lot. I was able to convert the BERT model to ONNX successfully. Do you have a sample code of how the converted ONNX model can be used further for inferences? I am able to use ONNX for TF, Keras, Sklearn, Xgboost and other models but stuck with transformer model. Appreciate any inputs. ", "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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[ "Go Patrick!!!!! YES! someone who cares!", "Merging since @LysandreJik is off today and this is blocking me a bit" ]

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[ "Thanks!" ]

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[ "I'm quite puzzled too, to be honest. I know that sometimes, PyTorch will trigger a CUDA OOM error even if there is enough memory in theory just because it's not able to find a contiguous chunk or has some leftovers for some reason, exactly like what your message suggests (22.53GB allocated but 23.21GB reserved by PyTorch). I don't have any suggestion apart from trying the usual strategies to lower a bit the memory footprint (slightly lower the batch size or block size).", "@sgugger Appreciate your reply! I am wondering that can I resume the training processing if I change the batch size or block size of the training args. I have no idea whether it will fit the saved schedule or optimizer parameters.", "> @sgugger Appreciate your reply! I am wondering that can I resume the training processing if I change the batch size or block size of the training args. I have no idea whether it will fit the saved schedule or optimizer parameters.\r\n\r\n你好，请问你解决了这个问题了吗", "@xinjicong Not yet. If you have some ideas, please shares.", "> @xinjicong Not yet. If you have some ideas, please shares.\r\n\r\ni try to make max_seq_length smaller but it can't not work. ", "> @xinjicong Not yet. If you have some ideas, please shares.\r\n\r\n我检查了代码，发现是我在使用tokenizer的时候，出现了问题。tokenizer输出的维度多了一维，然后后面batch的时候就出错了。", "I observe the same issue, if I train a model, save a checkpoint and reload from this, I get memory issues for the code which was training fine before. ", "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.", "Same Issue", "+1", "I have this issue as well. Model trains for 1 epoch and goes through validation step, then I get OOM somewhere in the second epoch. These are large models I am training and I often get OOM after it has been training for a couple of hours.", "@dinsausti-vir Try reducing validation batch size to 1. I'm not sure how I fixed the error but batch size is usually the cause for OOM", "@perceptiveshawty Thanks for the tip. I will give that a shot!" ]

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[ "hi @krishanudb \r\n\r\nThank you for reporting this @krishanudb !", "Is there any update on this issue? @patil-suraj ", "It's fixed now on master!\r\nsee #10167", "The issue persists in transformers 4.3.3\r\n\r\n/usr/local/lib/python3.7/dist-packages/transformers/models/rag/tokenization_rag.py in prepare_seq2seq_batch(self, src_texts, tgt_texts, max_length, max_target_length, **kwargs)\r\n 75 if max_target_length is None:\r\n 76 max_target_length = self.generator.model_max_length\r\n---> 77 return super().prepare_seq2seq_batch(\r\n 78 src_texts, tgt_texts, max_length=max_length, max_target_length=max_target_length, **kwargs\r\n 79 )\r\n\r\nAttributeError: 'super' object has no attribute 'prepare_seq2seq_batch'", "Hi @rajasekar-venkatesan \r\n\r\nThe issue is fixed on master after the `4.3.3` release. This fix will be available in the next release." ]

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[ "Hi @krishanudb \r\n\r\nCould you post a code snippet so we can reproduce the issue? Please post your env info, short code snippet, and stack trace if possible when reporting bugs. 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 @IndianMLGay \r\n\r\nYou should `cd` into `simple-small-kvantorium` directory and then do `git add/commit/push` etc", "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 @vin-ivar \r\n\r\nThe `tokenizer` does not need the model config file, there is no need to pass it when initializing the tokenizer.", "That fixes it, I was using an older script without taking that bit out." ]

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[ "Oh boy, that is rather bad! Thanks a lot for fixing this!\r\nDid you want to add the test in this PR?", "> Oh boy, that is rather bad! Thanks a lot for fixing this!\r\n> Did you want to add the test in this PR?\r\n\r\nI can also create a follow up PR if you want to merge this asap. I can't implement the test cases right away. Maybe in ~4 days. @sgugger", "In that case maybe a follow-up PR, this fix is needed badly so I will merge. Thanks again!" ]

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[ "@abhishekkrthakur - this doesn't look good to me. \r\n\r\nJust changing the hardcoded integration test to values that make the test pass does not seem like the way to go here. The PyTorch integration test: https://github.com/huggingface/transformers/blob/7c7962ba891864f9770b9e9424f87d158b839a59/tests/test_modeling_convbert.py#L430 still has the old values and passes, which to me is an indicator that the TF implementation or the PyTorch implementation is not correct.\r\n\r\nAlso, it would be great if we could not merge PRs that have no description and that neither @sgugger, @LysandreJik or I approved." ]

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[ "As expected the two RAG tests are failing", "Thanks for fixing!" ]

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[ "I've been having the same problem", "Maybe @patrickvonplaten can chime in here", "Hey @TomUdale-debug - thanks for reporting your issue. It's quite difficult to debug problems with training, but I'll try my best to help you here. However, I would need full access to the training data etc...Could you please make a google colab that I can just run to reproduce your error and link it here. I think this will be the easiest way to check whether there is a problem with Longformer :-) ", "Sure thing I will set up a Colab, thanks!", "Many apologies for going slow on this, here is a [Colab](https://colab.research.google.com/drive/12ALD3gJS9rMpW7fvdIwj5mJGx1JGYgLb?usp=sharing) which demonstrates the issue. After one epoch of training (5k docs)the model logit outputs become constant, recall goes to 1 so the model if just predicting everything as 1 (binary classification task). I have the model checkpoint for that if it would be helpful. Any help on this would be great! Thanks, Tom", "Hmm, at first sounds to me this sounds like the classic overfitting to one class, I'm not so sure whether this is due to using Longformer. \r\n\r\nSome tips:\r\n\r\n- Get more info about your dataset. Is the dataset balanced? Could it be that one class is much more present in the dataset then other classes, which would then be a reason why the model overfits to one class\r\n- Increase the batch_size. Batch_size of 1 is too small IMO, try 8, 16 or 32\r\n- Play around with learning_rate / weight_decay\r\n- If nothing works, try whether you are able to fine-tune BERT well on this dataset. If BERT works well and Longformer doesn't then this is a strong indication that there is a problem with Longformer. But just from looking at the colab, I can't really draw any conclusions and it doesn't really seem to me that the problem is Longformer.\r\n\r\nHope this is somewhat helpful!", "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 @patrickvonplaten,\r\nI just realised that major step will be adding cross-attention layer to `TFDecoderLMHeadModel` for enabling this support. I will start doing that next.", "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 @205g0 \r\n\r\nThank you for reporting this! \r\n\r\n`microsoft/deberta-xlarge-v2` uses `sentencepiece` vocab and it's not implemented for deberta, which is the reason for this error. ", "Hey Suraj, thanks for the quick response and good to know!", "@BigBird01 do you think you could add the missing tokenizer, otherwise, I could add it. Thanks!", "DeBERTa-v2 is not available in the library yet. We're working towards it with @BigBird01.", "Thanks @205g0 for the interest in DeBERTa-v2. We are working on it with @LysandreJik, hopefully, it will be available soon. You can check our [PR](https://github.com/huggingface/transformers/pull/10018) for the progress.", "Oh sorry, @BigBird01, I did not realize that this was a work in progress", "> Oh sorry, @BigBird01, I did not realize that this was a work in progress\r\n\r\nNo worry, @patil-suraj. Thanks for your quick response. We are glad to integrate these SOTA NLU models with HF to benefit the community:) ", "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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[ "Wow this is a huge bug actually! Thanks a lot for fixing it @patil-suraj! \r\n\r\n@LysandreJik @sgugger - Sadly Circle CI is skipping all RAG tests at the moment -> therefore we should first fix the faiss import (#10103), then rebase this PR to see that everything is correctly solved, merge it and then do a patch " ]

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[ "I guess @patil-suraj is the expert in this, check out his [repo](https://github.com/patil-suraj/question_generation) explaining all the details.", "ok..\r\n@patil-suraj , plz revert on [this](https://github.com/patil-suraj/question_generation/issues/69) issue I raised in your repo" ]

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[ "I'm only aware that the PyTorch official documentation is using the RPC: https://pytorch.org/tutorials/intermediate/dist_pipeline_parallel_tutorial.html", "This is more of a question for the PyTorch GitHub then ours since this is a question about to use `torch.distributed.launch`. That's why I'll close the issue. Still, I can share the command I run on my side:\r\n```\r\npython -m torch.distributed.launch --nproc_per_node 8 \\\r\n --nnodes 2 \\\r\n --node_rank rank_of_your_machine \\\r\n --master_addr main_machine_ip \\\r\n --master_port open_port_on_main_machine \\\r\n run_mlm.py \\\r\n --sharded_ddp \\\r\n --all_other_args_to_script\r\n```\r\nwhere `rank_of_your_machine` should be 0 for the main machine and 1 for the other one, `main_machine_ip` the IP of the machine of rank 0 and `open_port_on_main_machine` the port to use to communicate between the two machines.", "Thanks, this is really helpful" ]

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[ "Hi! Could you provide the code you used to get the predictions with the BERT checkpoint on TF Hub? The two should be identical.", "> Hi! Could you provide the code you used to get the predictions with the BERT checkpoint on TF Hub? The two should be identical.\r\n\r\nI copied from https://tfhub.dev/tensorflow/bert_zh_L-12_H-768_A-12/3\r\n```python\r\ntext_input = tf.keras.layers.Input(shape=(), dtype=tf.string)\r\npreprocessor = hub.KerasLayer(\r\n \"https://tfhub.dev/tensorflow/bert_zh_preprocess/3\")\r\nencoder_inputs = preprocessor(text_input)\r\nencoder = hub.KerasLayer(\r\n \"https://tfhub.dev/tensorflow/bert_zh_L-12_H-768_A-12/3\",\r\n trainable=False)\r\noutputs = encoder(encoder_inputs)\r\npooled_output = outputs[\"pooled_output\"] # [batch_size, 768].\r\nsequence_output = outputs[\"sequence_output\"] # [batch_size, seq_length, 768].\r\nembedding_model = tf.keras.Model(text_input, pooled_output)\r\nsentences = tf.constant([\"今天天气怎么样\"])\r\nprint(embedding_model(sentences))\r\n```\r\n", "Any update ? @LysandreJik ", "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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[ "> LGTM! Could we remove `Wav2Vec2ForMaskedLM` from the documentation?\r\n\r\nYes! It's better than adding a note saying that the model is deprecated? => yeah let's just remove it!" ]

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[ "Hello!\r\n\r\nIndeed there is an issue in the naming for `TFMobileBertForMaskedLM`. This will be fixed in the next release.", "OK, thanks for the quick response!", "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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[ "All slow tests passing in PT!", "@patrickvonplaten (Bart, MBart, Pegasus, Marian, Blenderbot, BlenderbotSmall) slow tests are passing in TF as well.", "Applied Sylvain's suggestions. Merging!" ]

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[ "There's also `docs/source/example.md` but I understand it is built automatically from `examples/README.md`" ]

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[ "> I propose that the best approach would be to finish everything that is planned and then we will run tests side by side and note any small discrepancies if any and fix them in one go? Does that work?\r\n\r\nYes, this was the last major missing piece from this script. Now I'm going to start running both scripts side by side (manually converting the old datasets to new datasets format) and note the discrepancies, I'll also wait for your tests.\r\n\r\n> I'm waiting for the datasets hub to port the datasets to be able to compare the old and the new.\r\n\r\nLet's not wait for the hub, for now, we could just manually convert the datasets for tests and later upload them to the hub once it's ready. ", "> > I propose that the best approach would be to finish everything that is planned and then we will run tests side by side and note any small discrepancies if any and fix them in one go? Does that work?\r\n> \r\n> Yes, this was the last major missing piece from this script. Now I'm going to start running both scripts side by side (manually converting the old datasets to new datasets format) and note the discrepancies, I'll also wait for your tests.\r\n\r\nThat works.\r\n\r\n> > I'm waiting for the datasets hub to port the datasets to be able to compare the old and the new.\r\n> \r\n> Let's not wait for the hub, for now, we could just manually convert the datasets for tests and later upload them to the hub once it's ready.\r\n\r\nSure - I already wrote the code for wmt en-ro https://github.com/huggingface/transformers/issues/10044#issuecomment-774413928 need to adapt to others.", "Changed `eval_beams` to `num_beams`. Hopefully final name change. Merging!" ]

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[ "Hi,\r\n\r\nYes the column ranks may cause issues when a table is too big, as the vocab size is only 256. See also [my reply](https://github.com/huggingface/transformers/issues/9221#issuecomment-749093391) on #9221.\r\n\r\nActually, the authors of TAPAS did release a new method in a [follow-up paper](https://arxiv.org/abs/2010.00571) to prune columns that are not relevant to a question to be able to serve large tables to the BERT-like model, so this is something that maybe could be added in the future.", "My suggestion would be to compute the column ranks on the truncated table. (Not sure if and how this is feasible.)\r\nOtherwise I would suggest returning a more informative error message.", "Yes, good suggestion. I've investigated this a bit and it seems that the original implementation also computes the column ranks on the original table, rather than the truncated one. I've asked the original authors [here](https://github.com/google-research/tapas/issues/106#issue-804538477). Will keep you updated.", "So the author replied:\r\n\r\n> IIRC, then we compute them before pruning the table.\r\nThat was by design so that those ranks would match the original numeric rank (pre-pruning).\r\nIt's true that the rank could thus exceed the vocab size.\r\nWe could add some trimming to prevent that.\r\n\r\nSo this is something that could be added in the future (together with the `prune_columns` option). I put it on my to-do list for now.", "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.", "@NielsRogge Thanks for the explanations above. Has there been any update on this issue? I have also run into this issue when running Tapas on the WTQ dataset, and it took me a lot of efforts to get to the bottom of this and realize that this is an issue with the `column_rank` IDs from oversized tables.\r\n\r\nThe painful part is that there is currently no guard or no warning against feeding oversized tables into the tokenizer, and the issue will only come out as a \"CUDA error: device-side assert triggered\" message when the Tapas forward pass is run.\r\n\r\nI think there are several potential ways to solve this or make this less painful:\r\n\r\n1. Computing the column rank after the table truncation (as already suggested by another comment above). This makes a ton of sense because the table will only be presented to the model after truncation in the tokenizer anyway, so there is no point to maintain a non-continuous column rank for large tables (with some ranks removed due to truncation). I understand that the original TF implementation might not handle this, but can this be added as a behavior in the Huggingface implementation?\r\n\r\n2. Add an option to re-map all the large column ranks to the max rank value. This can be implemented in this tokenizer function: https://github.com/huggingface/transformers/blob/7fcee113c163a95d1b125ef35dc49a0a1aa13a50/src/transformers/models/tapas/tokenization_tapas.py#L1487\r\nThis is less ideal than 1, but can make sure that the model won't crash due to an index-out-of-range error.\r\n\r\n3. The easiest fix would be to add some warning/exception in the tokenizer that reminds users about this. Or let the tokenizer return a `None` value in the output, or return a special boolean variable such as `table_oversized`. This does not solve anything, but can make the capture of this issue much easier.\r\n\r\nLook forward to some updates on this issue.", "Is there any way to bypass the token limit ?", "@KML1337,\r\n\r\nI do not have sure if you could consider the following approach:\r\n\r\nFirst, you split the table into n-subtables that generate tokens under the limit tokens;\r\nThen, process each subtable with the model;\r\nFinally, aggregate all responses and select the one with the highest logit score.\r\n" ]

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[ "Hey @swethmandava,\r\n\r\nSorry I don't understand the issue here - what do you mean by `model.generate` runs into errors? Your above code snippet works fine for me. Could you clarify the issue? Thank you!", "`summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True, num_beam_groups=1, output_scores=False, return_dict_in_generate=False, encoder_no_repeat_ngram_size=0, diversity_penalty=0.0)\r\n`\r\n\r\nworks for me. I have to define the following defaults (num_beam_groups, output_scores, return_dict_in_generate, encoder_no_repeat_ngram_size, diversity_penalty) explicitly since they are not in BARTConfig and default to None", "Hey @swethmandava \r\n\r\nYou shouldn't need to define these param. All these config params have default values defined in the `PretrainedConfig` class from which all other configs inherit. \r\n\r\nCould you try again with the newest transformers version?" ]

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[ "Hi, thanks for opening an issue! We have the `padding_side` tokenizer attribute, but it doesn't work for truncation unfortunately.\r\n@n1t0, what do you think?", "@LysandreJik Thanks a lot for your response! @n1t0 I'm wondering what your thoughts are on this feature?" ]

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[ "Hello! Do you mind giving us a reproducible example, for example the sequence that makes this pipeline crash? Without such an example we won't be able to find out what's wrong. Thank you for your understanding", "Hello! Thank you very much for your quick reply. While there are many entities in my dataset that cause the error, I just found the following entry and reproduced the error in a seperate script:\r\n\r\n> Hi Jan! Nice post and I’m jealous that you get to go to both the SAP sessions and the AppleDevCon. But I think you inadvertent discovery of the aging of the SAP developer population vs the non-enterprise developers is a telling one. SAP tools and platforms remain a niche area that are only utilised by SAP developers. They may be brilliant, indeed I think in some area SAP is well ahead of the rest of the pack. The problem is I am 1 in 10,000 in thinking this (conservative estimate I fear). Those with plenty of experience in enterprise development (hence older) appreciate the ways that SAPs tools work with an enterprise way of doing things (translatable, solid, standard, accessible, enhanceable, etc). Whereas those that are used to pushing code changes to production every few hours just don’t understand. Why would you want your app to look like it is an SAP app? (Hello UI5 I can see you from across the room, you can’t hide.) Of course if you’re using this as an enterprise-wide approach, it makes sense. Thankfully for the livelihood of all of us aging SAP developers, enterprises have architects that insist on standards and enterprise-wide approaches. In the meantime, however, our younger, and likely less well paid, colleagues in the non SAP developer space will continue to use whatever framework offers the best(fastest/easiest) result and most jobs. Since to get a job in the SAP space customers are used to asking for a minimum of multiple years of experience, it’s hard to get a gig – so it’s much more profitable to just develop in Firebase, Angular, etc and get a job. After all, having a paying job is quite often more important that working with your framework of choice. I am sure that many of us older SAP devs will hire many people and teach them the minor cross-over skills to be proficient in the SAP iOS SDK, and we’ll probably make a decent amount of money from the companies that have architects that insist on SAP UI5 looking applications. But I don’t think this will change the overall conversation. In another 3 years, the developers in SAP will have aged another 3 years (there will still be a huge demand and the pay will be too good to move on). A bunch of new talent will have been trained in the new tools and will by now have 3 years experience and will be able to find enterprise SAP jobs of their own, but we will be no closer to getting anyone to adopt SAP tools for anything other than SAP customer usage. Grim outlook – sorry. The alternative (as I see it) is that SAP gives up on building its own (even if open source and rather excellent) frameworks and just starts adding to some existing ones. All of a sudden instead of trying to convince people to use a new framework, you ask them to use a variant of one they already know. At the same time SAP invests some serious money into “public API first” development and makes everything in S4 and their other cloud products able to be accessed and updated via well documented APIs. (Thus the end of the need for ABAP developers and those who understand the black arts of the SAP APIs.) The costs per developer hour plummet and then we see a new group of developers helping customers realise their dreams. And some very happy customers. As for the SAP iOS SDK, I think it has a very niche area, even more so than standard UI5 development. Not only is it specific to a requirement that only a large SAP customer would have, it’s also mobile platform specific. Given that it will not translate to Android devices I fear that it will not interest the generic mobile app developer. Due to being quite SAP specific quite probably not the iOS only developer either. We’ll see SAP devs training up or being hired & trained for specific tasks, not adopting the platform just because it’s cool. Perhaps I’m just being too much of a grumpy old git (meant in the non-awesome code sharing/management/versioning way) and we will find that these open frameworks are adopted. That would be awesome. It would make a lot of SAP customers a lot happier too to be able to have some decent choice as to who to do their work. Cheers, Chris", "Hello! There were two issues here:\r\n\r\n- The configuration for the tokenizer of `distilbert-base-uncased-finetuned-sst-2-english` was ill-configured and was lacking the `max_length`. I've manually fixed this in [huggingface#03b4d1](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/commit/03b4d196c19d0a73c7e0322684e97db1ec397613)\r\n- You should truncate your sequences by setting `truncation=True` so that your sequences don't overflow in the pipeline:\r\n\r\n```py\r\nclassifier = pipeline('sentiment-analysis')\r\nclassifier(text, truncation=True)\r\n```\r\n\r\nLet me know if this fixes your issue!", "Hello!\r\n\r\nThank you so much! That fixed the issue. I already thought the missing `max_length` could be the issue but it did not help to pass `max_length = 512` to the _call_ function of the pipeline.\r\n\r\nI used the truncation flag before but I guess it did not work due to the missing `max_length` value.\r\n\r\nAnyway, works perfectly now! Thank you!", "Unfortunately this was due to the ill-configured tokenizer on the hub. We're working on a more general fix to prevent this from happening in the future.\r\n\r\nHappy to help!" ]

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[ "> I no longer can use the libraries from seq2seq since I can't do a relative import from the script.\r\n\r\nWe can add things to the sys path if needed (see the [general text examples](https://github.com/huggingface/transformers/blob/master/examples/test_examples.py)).\r\n\r\nThanks for doing this, it looks good to me!", "Indeed, that's what we have been doing, but having a library named \"`utils.py`\" and importing it from far away is too ambiguous. So will probably need to rename such libraries, or start moving their functionality into a more central area." ]

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[ "Maybe @n1t0 can chime in here!", "> When I output tokenizer name_or_path = nothing is displayed. This is normal?\r\n\r\nI think it is yes, you are loading using `tokenizer_file=` instead of using the normal path with `from_pretrained`. No need to worry about this.\r\n\r\nConcerning the error, I think the way to avoid it is by specifying `legacy_format=False`:\r\n```python\r\ntokenizer.save_pretrained(\"/content/tokennizerrrr\", legacy_format=False)\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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[ "> When two new dimensions are need, can we use a different operator to make the code more readable? Suggested tf.reshape but there might be something else available?\r\n\r\nYes, this is doable with `tf.reshape`.", "All the slow tests of the concerned models are ok!" ]

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[ "The examples are not tests, so `examples/deepspeed` should not be created to host some deepspeed tests. I'm wondering why we would need an `examples/deepspeed` since the base concept of having deepspeed integrating in our `Trainer` is to have it work out of the box for **all** our examples.\r\n\r\nMy suggestion was to create an `examples/tests` folder where all tests should go (so `test_examples`, and all `seq2seq/test_xxx`), to keep the examples folder themselves clean so that user can easily use them.", "> The examples are not tests, so `examples/deepspeed` should not be created to host some deepspeed tests. I'm wondering why we would need an `examples/deepspeed` since the base concept of having deepspeed integrating in our `Trainer` is to have it work out of the box for **all** our examples.\r\n\r\nI only meant it as a grouping, same as we did for models. first it was all flat and then we grouped them together under `models`.\r\n\r\n> My suggestion was to create an `examples/tests` folder where all tests should go (so `test_examples`, and all `seq2seq/test_xxx`), to keep the examples folder themselves clean so that user can easily use them.\r\n\r\nSure, as long as you feel that it's OK that we test core integrations under `examples` (as it is now) that works for me.\r\n\r\nCould you pelase clarify, do you prefer most/all of the `examples/tests` to be flat, or would grouping make things easier to make sense of - I'm asking since some tests come with extra files (as is the case with ds_config files) - so `examples/tests/deepspeed`, ...\r\n", "I agree with Sylvain, fairscale/deepspeed is supposed to work with all of our existing examples, so IMO we shouldn’t add `examples/deepspeed`.\r\n\r\n `examples/tests` makes sense to me.", "> Could you pelase clarify, do you prefer most/all of the examples/tests to be flat, or would grouping make things easier to make sense of - I'm asking since some tests come with extra files (as is the case with ds_config files) - so examples/tests/deepspeed, ...\r\n\r\nWe can certainly have several files once they are all together in one folder. I'd just like the examples subfolders to be clean, our internal testing should be setup so it's the easiest for us to understand/debug.", "Thank you for the clarification, @sgugger. I will start working on that transition." ]

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[ "Maybe @jplu has an idea!", "I dug up a bit into the model and it seems that the activation is `tanh`. Shouldn't it be a `sigmoid` or a `softmax`? That's why it is producing predictions lower than 0.\r\n\r\n```python\r\nIn [2]: model.classifier.dense\r\nOut[2]: <tensorflow.python.keras.layers.core.Dense at 0x7f6f70357350>\r\n\r\nIn [3]: model.classifier.dense.activation\r\nOut[3]: <function tensorflow.python.keras.activations.tanh(x)>\r\n```", "Hello!\r\n\r\nFrom what I can see from your dataset example, you have two labels `0` and `1` and not one so that's might be why you get this issue. For regression task, (single label output), they all have to have a float value between `0` and `1`. You can have an example with the `stsb` glue task in our example here https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_tf_glue.py", "Alright, I'll take a look then. Predicting a binary classification with 2 neurons and `linear` or `tanh` activations seemed strange to me. I've always used a single neuron with `sigmoid`.", "For binary classification, you have two labels and then neurons, it is more intuitive to proceed that way :) but yes you can also do what you propose and round the float value to 0 or 1 depending of the output of your sigmoid activation. Nevertheless, our models don't propose such approach.", "Figured out that the error was thrown by the `Precision` and `Recall` classes because they require values between 0 and 1. In case someone wants to use them when training with native TensowFlow I managed to add the `argmax` to the the classes with this:\r\n\r\n```python\r\nfrom tensorflow.python.keras.utils import metrics_utils\r\n\r\nclass PrecisionArgmax(Precision):\r\n def update_state(self, y_true, y_pred, sample_weight=None):\r\n y_pred = tf.math.argmax(y_pred, -1)\r\n return metrics_utils.update_confusion_matrix_variables(\r\n {\r\n metrics_utils.ConfusionMatrix.TRUE_POSITIVES: self.true_positives,\r\n metrics_utils.ConfusionMatrix.FALSE_POSITIVES: self.false_positives\r\n },\r\n y_true,\r\n y_pred,\r\n thresholds=self.thresholds,\r\n top_k=self.top_k,\r\n class_id=self.class_id,\r\n sample_weight=sample_weight)\r\n```\r\n\r\nSo the code that I posted works with `num_classes=2` and using the the overridden classes as metrics." ]

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[ "Did you fix this, and if so, how? I'm having the same problem and nothing's working for me so far.\r\n\r\n**EDIT**: Fixed it, I was using `tf` tensors with a (PyTorch) `AutoModel` instead of a `TFAutoModel`." ]

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[ "One of the model templates test (the pull request target) will fail because it doesn't take into account the changes made in the PR. Following this in https://github.com/huggingface/transformers/issues/10065", "Thanks for fixing!" ]

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[ "@LysandreJik I think there is a problem with the Template test. It doesn't seem to take into account the changes in the current PR.", "Good catch @jplu, thanks for fixing! Yes indeed, this test needs to be reworked. Tracking it in https://github.com/huggingface/transformers/issues/10065" ]

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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! Can you try installing the brand new v4.3.0 to see if it resolves your issue?", "same problem here and my transformers is v4.3.0, still not working", "Could you try to load the model/tokenizer and specify the `local_files_only=True` kwarg to the `from_pretrained` method, before passing them to the pipeline directly?\r\n\r\ni.e., instead of:\r\n\r\n```py\r\npipeline('sentiment-analysis')('I love you')\r\n```\r\n\r\ntry:\r\n\r\n```py\r\nfrom transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer\r\n\r\nmodel = AutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\", local_files_only=True)\r\ntokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\", local_files_only=True)\r\n\r\npipeline('sentiment-analysis', model=model, tokenizer=tokenizer)('I love you')\r\n```", "Thanks @LysandreJik.\r\n\r\nTried your suggestions. \r\nNot working. Tried by keeping local_files_only both True and False while loading the model. It did not work.", "@PrinceMohdZaki were you able to find a solution for this error?", "Can anyone having those kind of issues, please try #10235, and let us know if it provides more insight into the cause (networking issue, proxy error, etc.)?\r\n\r\nThanks!", "> @PrinceMohdZaki were you able to find a solution for this error?\r\n\r\nYeah. Finally we resolved it by exporting the https_proxy same as http_proxy as shown here : https://stackoverflow.com/questions/56628194/sslerror-installing-with-pip/56628419 ", "I am facing the same issue when trying to do `spacy.load`. Is there an obvious solution to that?\r\n\r\nI am following [this](https://turbolab.in/build-a-custom-ner-model-using-spacy-3-0/) tutorial to build a custom NER pipeline on an HPC cluster where the compute nodes do not have access to the internet. \r\n\r\nHere's the log:\r\n\r\n```\r\npython3 -m spacy train data/config.cfg --paths.train ./train.spacy --paths.dev ./valid.spacy --output ./models/output --gpu-id 0\r\nℹ Saving to output directory: models/output\r\nℹ Using GPU: 0\r\n\r\n=========================== Initializing pipeline ===========================\r\n[2022-11-18 15:12:08,973] [INFO] Set up nlp object from config\r\n[2022-11-18 15:12:08,982] [INFO] Pipeline: ['transformer', 'ner']\r\n[2022-11-18 15:12:08,984] [INFO] Created vocabulary\r\n[2022-11-18 15:12:08,986] [INFO] Finished initializing nlp object\r\nTraceback (most recent call last):\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/runpy.py\", line 86, in _run_code\r\n exec(code, run_globals)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/spacy/__main__.py\", line 4, in <module>\r\n setup_cli()\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/spacy/cli/_util.py\", line 71, in setup_cli\r\n command(prog_name=COMMAND)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/click/core.py\", line 1128, in __call__\r\n return self.main(*args, **kwargs)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/click/core.py\", line 1053, in main\r\n rv = self.invoke(ctx)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/click/core.py\", line 1659, in invoke\r\n return _process_result(sub_ctx.command.invoke(sub_ctx))\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/click/core.py\", line 1395, in invoke\r\n return ctx.invoke(self.callback, **ctx.params)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/click/core.py\", line 754, in invoke\r\n return __callback(*args, **kwargs)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/typer/main.py\", line 500, in wrapper\r\n return callback(**use_params) # type: ignore\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/spacy/cli/train.py\", line 45, in train_cli\r\n train(config_path, output_path, use_gpu=use_gpu, overrides=overrides)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/spacy/cli/train.py\", line 72, in train\r\n nlp = init_nlp(config, use_gpu=use_gpu)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/spacy/training/initialize.py\", line 84, in init_nlp\r\n nlp.initialize(lambda: train_corpus(nlp), sgd=optimizer)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/spacy/language.py\", line 1317, in initialize\r\n proc.initialize(get_examples, nlp=self, **p_settings)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/spacy_transformers/pipeline_component.py\", line 355, in initialize\r\n self.model.initialize(X=docs)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/thinc/model.py\", line 299, in initialize\r\n self.init(self, X=X, Y=Y)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/spacy_transformers/layers/transformer_model.py\", line 131, in init\r\n hf_model = huggingface_from_pretrained(name, tok_cfg, trf_cfg)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/spacy_transformers/layers/transformer_model.py\", line 251, in huggingface_from_pretrained\r\n tokenizer = AutoTokenizer.from_pretrained(str_path, **tok_config)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py\", line 471, in from_pretrained\r\n tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py\", line 332, in get_tokenizer_config\r\n resolved_config_file = get_file_from_repo(\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/transformers/file_utils.py\", line 2310, in get_file_from_repo\r\n resolved_file = cached_path(\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/transformers/file_utils.py\", line 1921, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/hyadav/.conda/envs/spacy_venv/lib/python3.10/site-packages/transformers/file_utils.py\", line 2177, in get_from_cache\r\n raise ValueError(\r\nValueError: Connection error, and we cannot find the requested files in the cached path. Please try again or make sure your Internet connection is on.\r\n```\r\n\r\n\r\n", "First you need to run internet on the hpc. Read your hpc's documentation or ask system administrator about it.", "> First you need to run internet on the hpc. Read your hpc's documentation or ask system administrator about it.\r\n\r\nWe have access to the internet on the login nodes but not on the compute nodes. So, I can download everything on the login nodes I need before I finally start computations on the compute nodes.", "Follow the same protocol to run internet on the compute nodes. If you are\nrunning the script by submitting a job on compute nodes, insert the\ncommands to run internet in the job script before the python command.\n\nIf proxies are also required to be exported, you can either export them\nbefore the python command or export proxies within the code using os module.\n\nOn Sat, 19 Nov 2022, 00:27 Himanshi Yadav, ***@***.***> wrote:\n\n> First you need to run internet on the hpc. Read your hpc's documentation\n> or ask system administrator about it.\n>\n> We have access to the internet on the login nodes but not on the compute\n> nodes. So, I can download everything on the login nodes I need before I\n> finally start computations on the compute nodes.\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/transformers/issues/10067#issuecomment-1320540933>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AFATXOFCQS2LFR2I2KNVXN3WI7YFFANCNFSM4XI2ROIA>\n> .\n> You are receiving this because you modified the open/close state.Message\n> ID: ***@***.***>\n>\n", "> Follow the same protocol to run internet on the compute nodes. If you are running the script by submitting a job on compute nodes, insert the commands to run internet in the job script before the python command. If proxies are also required to be exported, you can either export them before the python command or export proxies within the code using os module.\r\n> […](#)\r\n> On Sat, 19 Nov 2022, 00:27 Himanshi Yadav, ***@***.***> wrote: First you need to run internet on the hpc. Read your hpc's documentation or ask system administrator about it. We have access to the internet on the login nodes but not on the compute nodes. So, I can download everything on the login nodes I need before I finally start computations on the compute nodes. — Reply to this email directly, view it on GitHub <[#10067 (comment)](https://github.com/huggingface/transformers/issues/10067#issuecomment-1320540933)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFATXOFCQS2LFR2I2KNVXN3WI7YFFANCNFSM4XI2ROIA> . You are receiving this because you modified the open/close state.Message ID: ***@***.***>\r\n\r\nThere is no protocol to run the internet on the compute nodes, you **can not** use internet on the compute nodes. ", "Getting this error, and found out today that **HuggingFace is down** so this is likely not because of the issues mentioned above at the moment", "Thank you ! \r\nI solved the problem when I set export HTTPS_PROXY and https_proxy", "changing the default value of force_download=True\r\nin cached_path line 1037 \r\n\\home\\username\\anaconda3\\envs\\punct\\lib\\python3.8\\site-packages\\transformers\\file_utils.py solved it for me\r\n" ]

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[ "Sure! Change applied", "Done, command `make style` applied. Thanks for the guidance", "Build still not succeeding. I will check ", "Ah yes, those links don't need the underscores! Good catch and sorry for giving you the wrong example to follow. Just waiting for the last tests and we can merge :-)", "No worries, @sgugger. Thanks for the guidance :)" ]

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