Datasets:

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DanielPFlorian

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Transformers-Github-Issues

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[ "You're welcome ;-)", "cc @patil-suraj " ]

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[ "This seems to be a duplicate of #7169, finishing something and will investigate now that I have a proper multi-GPU steup.", "Yes, most likely a duplicate.\r\nOne clue might be the call to `DataParallel` right before the call to `DistributedDataParallel`. ", "Investigation seems to lead to: this is normal to have slightly more memory use per GPU in distributed mode since PyTorch keeps two copies in the gradients in that case. See [this issue](https://github.com/pytorch/pytorch/issues/37030).", "You are correct, thank you for investigating. The difference was using `--adafactor` which is saving about 3GB.\r\nI added that option to Seq2SeqTrainer. Take it if you want it :) \r\n" ]

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[ "Hello! Could you run `transformers-cli env` in your environment and paste the result here? Thank you!", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hey @sashank06 - could you please post a full code snippet so that we can reproduce your error? \r\n\r\nAlso @lhoestq - this looks like one of your favorite errors haha ", "It's already fixed on `datasets` master branch, I'm going to do a release soon :) ", "```\r\nimport ast\r\nimport logging\r\nimport os\r\nimport sys\r\n\r\nimport pandas as pd\r\nimport torch\r\nfrom tqdm import tqdm\r\n\r\nfrom transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration\r\nfrom transformers import logging as transformers_logging\r\n\r\nlogger = logging.getLogger(__name__)\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\ntransformers_logging.set_verbosity_info()\r\n\r\n\r\ndef infer_model_type(model_name_or_path):\r\n\tif \"token\" in model_name_or_path:\r\n\t\treturn \"rag_token\"\r\n\tif \"sequence\" in model_name_or_path:\r\n\t\treturn \"rag_sequence\"\r\n\tif \"bart\" in model_name_or_path:\r\n\t\treturn \"bart\"\r\n\treturn None\r\n\r\ndef evaluate_batch_retrieval(rag_model, questions):\r\n\tdef strip_title(title):\r\n\t\tif title.startswith('\"'):\r\n\t\t\ttitle = title[1:]\r\n\t\tif title.endswith('\"'):\r\n\t\t\ttitle = title[:-1]\r\n\t\treturn title\r\n\r\n\tretriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(\r\n\t\tquestions,\r\n\t\treturn_tensors=\"pt\",\r\n\t\tpadding=True,\r\n\t\ttruncation=True,\r\n\t)[\"input_ids\"] #.to(args.device)\r\n\r\n\tquestion_enc_outputs = rag_model.rag.question_encoder(retriever_input_ids, return_dict=True)\r\n\tquestion_enc_pool_output = question_enc_outputs.pooler_output\r\n\r\n\tresult = rag_model.retriever(\r\n\t\tretriever_input_ids,\r\n\t\tquestion_enc_pool_output.cpu().detach().to(torch.float32).numpy(),\r\n\t\tprefix=rag_model.rag.generator.config.prefix,\r\n\t\tn_docs=rag_model.config.n_docs,\r\n\t\treturn_tensors=\"pt\",\r\n\t)\r\n\tall_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)\r\n\tprovenance_strings = []\r\n\tfor docs in all_docs:\r\n\t\tprovenance = [strip_title(title) for title in docs[\"title\"]]\r\n\t\tprovenance_strings.append(\"\\t\".join(provenance))\r\n\treturn provenance_strings\r\nmodel_kwargs = {}\r\nmodel_type = \"rag\"\r\nif model_type.startswith(\"rag\"):\r\n\tmodel_class = RagTokenForGeneration if model_type == \"rag_token\" else RagSequenceForGeneration\r\n\tmodel_kwargs[\"n_docs\"] = 5 #args.n_docs\r\n\tindex_name = \"hf\"\r\n\tif index_name is not None:\r\n\t\tmodel_kwargs[\"index_name\"] = index_name\r\n# if args.index_path is not None:\r\n# model_kwargs[\"index_path\"] = args.index_path\r\nelse:\r\n\tmodel_class = BartForConditionalGeneration\r\n\r\ncheckpoint = \"facebook/rag-sequence-base\"\r\nif model_type.startswith(\"rag\"):\r\n\tretriever = RagRetriever.from_pretrained(checkpoint, **model_kwargs)\r\n\tmodel = model_class.from_pretrained(checkpoint, retriever=retriever, **model_kwargs)\r\n\tmodel.retriever.init_retrieval()\r\nelse:\r\n\tmodel = model_class.from_pretrained(checkpoint, **model_kwargs)\r\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\nmodel.to(device)\r\n\r\nquestions = []\r\nquestions.append(\"where was the first super bowl held?\".strip())\r\n\r\n\r\nevaluate_batch_retrieval(model, questions)", "@patrickvonplaten I have attached the code above. Do let me know if I am doing something wrong with the code as well. ", "@lhoestq When would the release be made? Would that fix the issue I am facing?", "Tomorrow most probably.\r\nYes this will fix your issue", "The new release is out :)\r\nYou can do\r\n```\r\npip install --upgrade datasets\r\n```", "will test it out and let you know if that fixes my problem." ]

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[ "Anwer is included in the main post. Thanks.", "Another way that \"fixed\" the problem for me was setting the number of torch threads to 1: torch.set_num_threads(1) BEFORE loading the model in the worker.", "Thanks, man, it took me a day to find your comment as well. 😆 " ]

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[ "I am working on a pull request to address this. I don't see any major challenge so far, but this made me realize how much `attentions` in Bert-like models and in Longformers are different. Why not replace `attentions` in the Longformer by `local_attentions`?\r\n\r\nThis means that the interface of Longformers would become incompatible with every other Transformer, but maybe it should be? I don't think that there is a way to plug Longformer `attentions` into a code that expects Bert-like `attentions` and get meaningful results, so users always have to write a special case for Longformers if they use them. As is, the risk is that they get bogus output and won't realize it until they carefully read the doc (that is not yet written).\r\n\r\nWhat are your thoughts on this @patrickvonplaten?", "I have made the [pull request](https://github.com/huggingface/transformers/pull/7562).\r\n\r\nI checked that the Longformer tests passed with my changes, and I added one more test to check the output of attention probabilities.\r\n\r\nQuite stupidly I made the pull request to the __master__ branch, I am sorry about this. I left it as is to avoid duplicating pull requests for now. You can reject it and I will make a cleaner pull request to a separate branch.\r\n", "sorry to have been so super inactive on this issue :-/ I will find time to solve it in ~1 week :-) . This issue is related as well: https://github.com/huggingface/transformers/pull/8007/files#r514633097.", "No worries, there is no hurry on my side. Anyway, the issue is a little trickier than it looks because you guys have to decide how to encode attention probabilities when they are too large to be represented by a dense matrix. Let me know if there is anything I can do to help.", "Hi @patrickvonplaten. I did not use the 🤗 Transformers since our discussion in November 2020. Today I came back to it (`transformers` version: 4.4.2) and I realized that this issue is still not completely solved. I could open a new issue, but I believe that the fix is really simple so I hope we can address it here: In some models, the global attentions are computed, stored in `outputs`, but at the very last stage they are not returned.\r\n\r\nIf I am not mistaken, the issue is in [modeling_longformer.py](https://github.com/huggingface/transformers/blob/master/src/transformers/models/longformer/modeling_longformer.py). At lines 1784-1789 the code is\r\n\r\n return LongformerMaskedLMOutput(\r\n loss=masked_lm_loss,\r\n logits=prediction_scores,\r\n hidden_states=outputs.hidden_states,\r\n attentions=outputs.attentions,\r\n )\r\n\r\nbut I think it should be\r\n\r\n return LongformerMaskedLMOutput(\r\n loss=masked_lm_loss,\r\n logits=prediction_scores,\r\n hidden_states=outputs.hidden_states,\r\n attentions=outputs.attentions,\r\n global_attentions=outputs.global_attentions, # <=====\r\n )\r\n\r\nThe same goes for lines 1876 and 2124 (but it is fine for lines 2029 and 2235).\r\n", "This sounds correct to me! Would you mind opening a new PR? ", "I will do it, no problem.", "I made a minimal pull request https://github.com/huggingface/transformers/pull/10906." ]

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[ "I agree! Deprecation until v4.0.0 seems like a reasonable solution,", "(We'll need at least one release with it before 4.0.0 if we want to remove the deprecation at 4.0.0)", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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

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

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[ "Also \r\nhttps://github.com/huggingface/transformers/blob/a42f62d34f7b2acdb7298e586adbe9f0f28864ea/examples/seq2seq/seq2seq_trainer.py#L56\r\n\r\nwe are using `model.config` here, this breaks on multi-gpu, right ?\r\nand IMO we might not need this `assert` here ", "```\r\nself.pad_token_id = self.model.config.pad_token_id\r\n```\r\nThis will also break.\r\nMaybe we could just stuff the config or (`pad_token_id`) on `data_args`?\r\n\r\n\r\n\r\n", "I may start `metrics.py` and move rouge/bleu funcs and their helpers in there as well. ", "> ```\r\n> self.pad_token_id = self.model.config.pad_token_id\r\n> ```\r\n> \r\n> This will also break.\r\n> Maybe we could just stuff the config or (`pad_token_id`) on `data_args`?\r\n\r\nwe could pass `config` directly to `init`", "works for me! <3 config" ]

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[ "Sorry by the way for not catching these at the same time. This should be all for now though!", "Oh I remembered seeing this last week and thinking: This is wrong, I should fix it... but forgot...\r\nThanks for following through!" ]

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[ "Just to be sure, is it also backward compatible with older versions of Ray Tune?", "Yes! Return values were never required, and in fact disregarded until recently.\n\nOn October 1, 2020 2:38:00 PM GMT+01:00, Sylvain Gugger <[email protected]> wrote:\n>Just to be sure, is it also backward compatible with older versions of\n>Ray Tune?\n>\n>-- \n>You are receiving this because you authored the thread.\n>Reply to this email directly or view it on GitHub:\n>https://github.com/huggingface/transformers/pull/7507#issuecomment-702141017\n", "Thanks for the fix then!", "I'm confused about this PR. Why do we only evaluate once (metrics = local_trainer.evaluate()) and then report done=True directly? Some schedulers, like PopulationBasedTraining, need to evaluate many times during training to decide if the trial is good or bad." ]

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[ "Hi, as you can see in [this example NER configuration file](https://s3.amazonaws.com/models.huggingface.co/bert/dslim/bert-base-NER/config.json), the `id2label` and `label2id` are actually dictionaries. \r\n\r\nInstead of doing the change you propose, changing the documentation to reflect that would be better. Thank you!", "Wow, seems like someone else already fixed the documentation in the mean time." ]

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

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[ "Try adding `decoder_start_token_id=2` to `best_tfmr/config.json` and let me know if that changes anything!", "I just did the above-mentioned change and decoded (run_eval.py) without training any further. Still, the issue persists. ", "Ok, you could try re-training/training more with new training code. We can't reproduce this on `master`", "okay, thanks!" ]

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[ "Or is this model only available for pytorch and not TF?", "https://huggingface.co/distilbert-base-german-cased\r\n\r\n^^ currently only the Pytorch weights, but you can load into TF pretty easily (follow the README/doc). If needed we can upload the converted TF weights (cc @stefan-it who I believe trained this model? This predates our user/organization namespaces)", "And I've added a `de` language tag so that the model is discoverable via https://huggingface.co/models?filter=de&search=distilbert\r\n\r\nThanks for reporting!", "Oh, we've an open issue regarding to the TF checkpoint 😅\r\n\r\nhttps://github.com/dbmdz/berts/issues/8\r\n\r\n@julien-c could you convert the model and upload it (not sure if I've access to root S3), thanks :heart: \r\n\r\n", "> but you can load into TF pretty easily (follow the README/doc).\r\n\r\nCOuld you show me where I can read about htis? Cab just find from the getting stared tour:\r\n\r\n`bert_model = TFDistilBertModel.from_pretrained('distilbert-base-german-cased', from_tf=False)\r\n`\r\n...but this does not work...", "`from_pt=True`?", "Ok, great. So easy.^^ " ]

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[ "Here's an example using beam search with GPT-2:\r\n\r\n```py\r\nfrom transformers import GPT2LMHeadModel, GPT2Tokenizer\r\n\r\ntokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\nmodel = GPT2LMHeadModel.from_pretrained(\"gpt2\")\r\n\r\ninput_ids = tokenizer(\"The day starts with\", return_tensors='pt')['input_ids']\r\nprint(tokenizer.decode(model.generate(input_ids, num_beams=3)[0]))\r\n```\r\nResult:\r\n```\r\nThe day starts with a long walk to the top of the hill.\r\n\r\nThe first thing you\r\n```", "[Here's the doc for the `generate` method](https://huggingface.co/transformers/main_classes/model.html#transformers.generation_utils.GenerationMixin.generate)", "Thanks @LysandreJik for the response but I have a custom model, I want to know how can I generate using my model", "@LysandreJik - How can I generate sentences using my custom model?", "I would recommend you check the[ source code for the `generate` method](https://github.com/huggingface/transformers/blob/master/src/transformers/generation_utils.py#L111) and see how the beam search is implemented. It is not trivial, however.\r\n\r\nMaybe @sshleifer and @patrickvonplaten have better tips on how to best do this.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "@nrjvarshney Did you find any suitable way to use `generate` function for a custom model? I am facing a similar issue with a model of mine, and would be really grateful if you could let me know how to solve the issue. " ]

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[ "> * `transformers` version: 3.3.0\r\n> \r\n> * Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n> \r\n> * Python version: 3.7.4\r\n> \r\n> * PyTorch version (GPU?): 1.6.0 (False)\r\n> \r\n> * Tensorflow version (GPU?): not installed (NA)\r\n> \r\n> * Using GPU in script?: No\r\n> \r\n> * Using distributed or parallel set-up in script?: No\r\n> \r\n> \r\n> @LysandreJik @sshleifer\r\n> \r\n> I'm trying to use BartForSequenceClassification() to do a regression, which should use a MSELoss() when (num_labels=1), as stated in the documentation. However, when I went over the source code for modeling_Bart.py, it didn't seem like the regression functionality with MSELoss() was added in the source code. Within the class BartForSequenceClassfication(), It only has CrossEntropyLoss() to do classification. I wonder if you will be adding this functionality.\r\n> \r\n> So I went to BertForSequenceClassification() class to see how it did it, and I found that it might have a problem.\r\n> \r\n> In the class BertForSequenceClassification(BertPreTrainedModel) (line 1352):\r\n> \r\n> if labels is not None:\r\n> if self.num_labels == 1:\r\n> # We are doing regression\r\n> loss_fct = MSELoss()\r\n> loss = loss_fct(logits.view(-1), labels.view(-1))\r\n> else:\r\n> loss_fct = CrossEntropyLoss()\r\n> loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\r\n> \r\n> Here the MSELoss() and CrossEntropyLoss() are both loss functions from pytorch.\r\n> \r\n> So you passed in the logits, which are unnormalized probabilities, to both of the loss functions. It is ok to do so for the CrossEntropyLoss(), since from pytorch's documentation they expect the inputs to be unnormalized logits and from their source code they first log softmax it before actually computing the loss, but I don't think it's okay to do the same for the MSELoss() functions. If you look at their implementation of it, it did not normalize it first using either softmax or sigmoid, and they also indicate that the function expects unnormalized logits in their documentation. I'm not sure if this behavior is intended (which is unlikely since we want to normalize it first before loss functions or there could be gradient explosions), but I think this confusion/inconsistency from pytorch may cause a problem and you probably want to change it. Please correct me if I'm wrong and thanks for this wonderful package!\r\n> \r\n> Thanks!\r\n\r\nI am trying to use BertforSequeneClassification to do regression too and faced the same issue. Do u have any walk around? thx! ", "@liusiyi641 Just to clarify - do you expect to rewrite that code snippet as follows:\r\n```python\r\nif labels is not None:\r\n if self.num_labels == 1:\r\n # We are doing regression\r\n loss_fct = MSELoss()\r\n normalizer = nn.Sigmoid()\r\n logits = normalizer(logits) * (B - A) + A # we expect the regression values be on [A, B] interval\r\n loss = loss_fct(logits.view(-1), labels.view(-1))\r\n else:\r\n loss_fct = CrossEntropyLoss()\r\n loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\r\n```\r\nright?\r\n\r\nI tried this change but it doesn't have any influence on the training process in my case: the same accuracy, the same behavior of the learning curve.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "This issue has been automatically marked as stale because it has not had recent activity. 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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[ "Bert is a Masked Lanuage Model. ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hi! This depends on the checkpoint. If you're using a checkpoint pre-trained on multiple-choice, then it's very possible that it is pre-trained. If you're using a checkpoint pre-trained on another task, it might not be pre-trained.\r\n\r\nYou should be wary of the task on which the model was trained when leveraging a model with a pre-trained head, as it might not overlap with your current task." ]

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[ "Hi @golsun! Thanks a lot for opening an issue and offering to contribute it!\r\n\r\nIndeed, there is no `GPT2ForSequenceClassification` model in the library (yet!) I'm adding it right now with the goal of supporting DialogRPT. I'll get back to you in a bit.", "Hi @golsun! `GPT2ForSequenceClassification` has been implemented on #7501 and I verified that I obtain the same results as you do on your README using your examples.\r\n\r\nYou should only need to upload your models on the model hub now! Some helpers regarding the configuration:\r\n\r\n- You should upload a model configuration on the hub, for every model.\r\n- You can simply copy-paste the `gpt2-medium` configuration that you can find [here](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json).\r\n- You will need to add a `num_labels=1` field to these configurations.\r\n- In the `architectures` field, you should put `GPT2ForSequenceClassification`", "wow, super fast!!! \r\nthank you @LysandreJik , I'll update my repo to reflect this once the [pull](https://github.com/huggingface/transformers/pull/7501) is merged.\r\n\r\n", "The pul request is now merged @golsun!", "Thank you so much @LysandreJik !\r\nI just tried `GPT2ForSequenceClassification` and it works! 👍 \r\nThen I created this [model card](https://huggingface.co/microsoft/DialogRPT-updown), but `model = AutoModelForSequenceClassification.from_pretrained(\"microsoft/DialogRPT-updown\")` gives me the following error, which can be reproduced with [this Notebook](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing):\r\n```\r\n/content/transformers/src/transformers/modeling_auto.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)\r\n 1203 config.__class__,\r\n 1204 cls.__name__,\r\n-> 1205 \", \".join(c.__name__ for c in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys()),\r\n 1206 )\r\n 1207 )\r\n\r\nValueError: Unrecognized configuration class <class 'transformers.configuration_gpt2.GPT2Config'> for this kind of AutoModel: AutoModelForSequenceClassification.\r\nModel type should be one of DistilBertConfig, AlbertConfig, CamembertConfig, XLMRobertaConfig, BartConfig, LongformerConfig, RobertaConfig, SqueezeBertConfig, BertConfig, XLNetConfig, MobileBertConfig, FlaubertConfig, XLMConfig, ElectraConfig, FunnelConfig, DebertaConfig.\r\n```\r\n", "Indeed, this should be solved by #7630.", "thank you @LysandreJik `AutoModelForSequenceClassification` works now.\r\nThe [inference webpage](https://huggingface.co/microsoft/DialogRPT-updown) still gives the `Unrecognized configuration class` error but I guess it will sync with the latest code soon. \r\nI'm going to introduce model card in the original repo.\r\nThanks again for the help!", "We just updated the API inference so that it uses the latest code. I've taken the liberty to add a padding token to your models, in your configuration (`pad_token_id: 50256`) and in the `special_tokens_map.json`: `pad_token: \"<|endoftext|>\"`, as it is necessary for the models to have a padding token to run in the API inference.\r\n\r\nI've taken these values from your code [here](https://github.com/golsun/DialogRPT/blob/master/src/feeder.py#L51) and [here](https://github.com/golsun/DialogRPT/blob/master/src/feeder.py#L18). \r\n\r\nModels should now work correctly in the [inference webpage :) ](https://huggingface.co/microsoft/DialogRPT-width?text=I+like+you.+I+love+you)", "Great! Thank you for updating the config and special_tokens_map for us! :)\r\nThe inference webpage will output a score of 1 no matter what input is. I guess it's because it outputs `softmax(logits)`, which is always 1 if `num_labels==1`. Maybe the following if-else will fix it? \r\n```\r\nif num_labels == 1:\r\n return torch.sigmoid(logits)\r\nelse:\r\n return torch.softmax(logits)\r\n```\r\nthe case `num_labels==1` follows the DialogRPT code [here](https://github.com/golsun/DialogRPT/blob/master/src/model.py#L95)", "You're correct! Solving that in #7726.", "Also @golsun on the inference API, you can have custom label names (instead of just `LABEL_0` here) if you set your label names in your `config.json`\r\n\r\nSee https://huggingface.co/roberta-large-mnli's config.json file for an example", "Awesome! thank you @LysandreJik @julien-c " ]

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[ "Hello! Indeed, the Rust tokenizers are not handled by the SQuAD data processing. This is one item we would like to resolve when refactoring the data processing methods, which will soon be implemented directly in `datasets` rather than in `transformers`.\r\n\r\nThank you for your detailed issue!", "For what it's worth, my main issue is with the behavioral issues with the Python vs. Rust tokenizers, not with the SQuAD data processing itself (I can easily write my own SQuAD processor but writing my own tokenizers is more work--the tokenizers are part of why I've been using the library in the first place). \r\n\r\nIt places a sizeable burden on people coding against the library that different kwargs result in completely different behaviors (and different errors) between the Rust vs. Python implementations, and these differences often aren't documented. And Item # 6 seems like a fundamental error somewhere in the Rust codebase.\r\n\r\nAre there any plans to address these issues in the near future? For what it's worth, I've never had an issue with the Python tokenizers. I'd like to use the Rust ones because they're Fast, plus I can train them myself easily, but navigating the API weirdness has been a slog.", "You're correct that there is currently a mismatch between the python and rust tokenizers, and thank you for writing such a detailed issue explaining all of your pain points. We'll keep a close eye to the issues mentioned here as we continue working and improving the compatibility between the two APIs, which is something we will be focusing on in the near future.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Nice! FYI we'll have model versioning rolled out in ~1 month or so" ]

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[ "Tests pass in a multi-GPU environment and the specific `test_distributed_trainer` passes too. There is just one test that requires a batch size not too big so I manually skip it if there are more than 2 GPUs.", "Nice PR!!!\r\n\r\nI think it is a nice addition. Only the `global_step` won't be necessary as it is already integrated by default into the TF checkpoints. This is perfect!\r\n\r\nI plan also to add Keras callbacks into the TF Trainer that can handles few more arguments that are in this \"state\" class such as best checkpoint, and metrics.\r\n\r\nIt won't change much in the TF Trainer and could be very easily integrated as most of the \"state\" arguments work the same way for both Trainers. We can clearly imagine this state class for a much broader usage.", "Checked the tests on TPU are passing, so can safely merge this." ]

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[ "Hey @alexyalunin - you are completely right! I'm working on a bigger generation refactor at the moment to better handle cases like this. \r\n\r\nFor this case, I would propose to modify the code yourself and do a little hack, something along the lines \r\n\r\n```python \r\nif \"global_attention_mask\" in model_kwargs:\r\n encoder_outputs: ModelOutput = encoder(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask, return_dict=True)\r\nelse:\r\n encoder_outputs: ModelOutput = encoder(input_ids, attention_mask=attention_mask, return_dict=True) \r\n```\r\n\r\nI don't want to merge this into master because it's quite hacky and the `generate()` function needs a refactor before we start adding more and more hacks. Hope this works for you for now.", "This should be solved soon by the new generate() design: #6949 in like ~1,2 weeks", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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

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[ "~Hi! What's the problem?~ Edited your message so that we can read it.\r\n\r\n", "Could you put the error you had, as well as the environment? i.e., everything asked in the issue template.", "I am running it in colab CPU\r\n\r\n`TypeError` Traceback (most recent call last)\r\n<ipython-input-5-0c3c85adea42> in <module>()\r\n 5 start = time()\r\n 6 for i in range(100):\r\n----> 7 output, past = infer([context, past])\r\n 8 logits = output[0, -1, :]\r\n 9 tok = tf.argmax(logits)\r\n\r\n2 frames\r\n/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _call_with_flat_signature(self, args, kwargs, cancellation_manager)\r\n 1719 raise TypeError(\"{}: expected argument #{}(zero-based) to be a Tensor; \"\r\n 1720 \"got {} ({})\".format(self._flat_signature_summary(), i,\r\n-> 1721 type(arg).__name__, str(arg)))\r\n 1722 return self._call_flat(args, self.captured_inputs, cancellation_manager)\r\n 1723 \r\n\r\nTypeError: signature_wrapper(input_ids): expected argument #0(zero-based) to be a Tensor; got list ([<tf.Tensor: shape=(1, 10), dtype=int32, numpy=\r\narray([[2061, 389, 345, 1804, 706, 345, 423, 5201, 1762, 30]],\r\n dtype=int32)>, None])``\r\n\r\n> `", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "What is the resolution for this. Having the same issue.", "If you can open a new issue with the issue template filled out (environment information, code that fails, expected behavior), then we can help you! Thank you." ]

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[ "Indeed, this example (and all the others) is missing a `return_dict=True` in the call to `from_pretrained`. Thanks for flagging, the PR mentioned above will fix this." ]

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[ "Closing as it seems the changes were added in another PR" ]

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[ "Yes this is a known issue with our current system that will be fixed in ~1 month.\r\n\r\nIn the meantime, if you can upload to a different S3 bucket I can cp the files to your account on ours. Would you be able to do this?", "I don't have access to S3. However, I uploaded the model in my dropbox:\r\nhttps://www.dropbox.com/sh/0e7weo5l6g1uvqi/AADBZN_vuawdR3YOUOzZRo8Pa?dl=0\r\n\r\nIs it possible to download and upload it from the dropbox folder?", "Super I'll take care of it! ", "model is uploaded here: https://huggingface.co/Rostlab/prot_t5_xl_bfd", "Perfect, thanks a lot @patrickvonplaten for your help.\r\nThis solves my issue 😄 \r\n\r\nI will test the model to make sure everything is working as expected.\r\n\r\nShould we close this issue as it solved my current problem, or should we leave it open until the \"transformers-cli\" uploading problem is solved?\r\n\r\nI will leave it to you.", "Let's leave it open :-) ", "Hi! I'm having an issue uploading a model as well. I've tried several different iterations of the CLI command to get it to work. I'm following the instructions from the [model sharing docs](https://huggingface.co/transformers/model_sharing.html). \r\n\r\nHere's the info about my setup:\r\n\r\n- transformers version: 3.3.1\r\n- Platform: Ubuntu (it's a Google Cloud Platform VM)\r\n- Python version: 3.8.5\r\n- PyTorch version (GPU?): 1.4.0 (True)\r\n- Tensorflow version (GPU?): 2.3.1 (True)\r\n- Using GPU in script?: No\r\n- Using distributed or parallel set-up in script?: No\r\n\r\nFirst, I tried `transformers-cli upload distilbert-for-food-extraction`, as it says to do in the docs. This fails because for some reason the directory is not found, even though `ls distilbert-for-food-extraction` confirms that the directory and its files exist in this location.\r\n```\r\n(hf-nlp) charlenechambliss@charlene-gpu:~/.cache/food-ner/models$ transformers-cli upload chambliss/distilbert-for-food-extraction\r\n2020-10-10 21:43:16.899194: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\nTraceback (most recent call last):\r\n File \"/home/charlenechambliss/anaconda3/envs/hf-nlp/bin/transformers-cli\", line 8, in <module>\r\n sys.exit(main())\r\n File \"/home/charlenechambliss/anaconda3/envs/hf-nlp/lib/python3.8/site-packages/transformers/commands/transformers_cli.py\", line 33, in main\r\n service.run()\r\n File \"/home/charlenechambliss/anaconda3/envs/hf-nlp/lib/python3.8/site-packages/transformers/commands/user.py\", line 197, in run\r\n files = self.walk_dir(rel_path)\r\n File \"/home/charlenechambliss/anaconda3/envs/hf-nlp/lib/python3.8/site-packages/transformers/commands/user.py\", line 180, in walk_dir\r\n entries: List[os.DirEntry] = list(os.scandir(rel_path))\r\nFileNotFoundError: [Errno 2] No such file or directory: 'distilbert-for-food-extraction'\r\n```\r\n\r\nThen I tried nesting it under a directory matching my HuggingFace username, so now the path is `chambliss/distilbert-for-food-extraction`. Attempting the upload again seems to result in 3 out of 6 files being uploaded, then the process is aborted. Here is the full output I'm getting:\r\n\r\n```\r\n(hf-nlp) charlenechambliss@charlene-gpu:~/.cache/food-ner/models$ transformers-cli upload chambliss\r\n2020-10-10 21:43:28.932647: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file /home/charlenechambliss/.cache/food-ner/models/chambliss/distilbert-for-food-extraction/special_tokens_map.json to S3 under filename chambliss/distilbert-for-food-extraction/special_tokens_map.json and namespace chambliss\r\nAbout to upload file /home/charlenechambliss/.cache/food-ner/models/chambliss/distilbert-for-food-extraction/vocab.txt to S3 under filename chambliss/distilbert-for-food-extraction/vocab.txt and namespace chambliss\r\nAbout to upload file /home/charlenechambliss/.cache/food-ner/models/chambliss/distilbert-for-food-extraction/pytorch_model.bin to S3 under filename chambliss/distilbert-for-food-extraction/pytorch_model.bin and namespace chambliss\r\nAbout to upload file /home/charlenechambliss/.cache/food-ner/models/chambliss/distilbert-for-food-extraction/config.json to S3 under filename chambliss/distilbert-for-food-extraction/config.json and namespace chambliss\r\nAbout to upload file /home/charlenechambliss/.cache/food-ner/models/chambliss/distilbert-for-food-extraction/tokenizer_config.json to S3 under filename chambliss/distilbert-for-food-extraction/tokenizer_config.json and namespace chambliss\r\nAbout to upload file /home/charlenechambliss/.cache/food-ner/models/chambliss/distilbert-for-food-extraction/tf_model.h5 to S3 under filename chambliss/distilbert-for-food-extraction/tf_model.h5 and namespace chambliss\r\nProceed? [Y/n] Y\r\nUploading... This might take a while if files are large\r\nYour file now lives at: \r\nhttps://s3.amazonaws.com/models.huggingface.co/bert/chambliss/chambliss/distilbert-for-food-extraction/special_tokens_map.json\r\nYour file now lives at: \r\nhttps://s3.amazonaws.com/models.huggingface.co/bert/chambliss/chambliss/distilbert-for-food-extraction/vocab.txt\r\nYour file now lives at: \r\nhttps://s3.amazonaws.com/models.huggingface.co/bert/chambliss/chambliss/distilbert-for-food-extraction/pytorch_model.bin\r\n400 Client Error: Bad Request for url: https://huggingface.co/api/presign\r\nFilename invalid, model must be at exactly one level of nesting, i.e. \"user/model_name\".\r\n```\r\n\r\nIf there is not a fix available for this at the moment, would it be possible to have my model uploaded via Dropbox as well?\r\n\r\nThanks!\r\nCharlene", "Hey @chambliss - it looks like you are uploading the wrong folder. Instead of running \r\n\r\n```\r\n~/.cache/food-ner/models$ transformers-cli upload chambliss\r\n```\r\n\r\nyou should run \r\n\r\n```\r\n~/.cache/food-ner/models/chambliss$ transformers-cli upload distilbert-for-food-extraction\r\n```\r\n\r\nI think", "I'll second that. If `ls distilbert-for-food-extraction` works and shows the correct files, `transformers-cli upload distilbert-for-food-extraction` should work and would be able to find the correct directory.", "@patrickvonplaten @julien-c Thanks for the response guys! I'm not sure why the directory wasn't found the first time, but I tried it again just now (from inside the /chambliss directory, so `~/.cache/food-ner/models/chambliss$ transformers-cli upload distilbert-for-food-extraction`, as suggested) and it worked. \r\n\r\nAs a user, it is a little confusing for a reference to the correct directory not to work, and to have to be exactly one level above the directory in order for the upload to succeed. The example given on the page (`transformers-cli upload path/to/awesome-name-you-picked/`) implies that you can do the upload from anywhere relative to the folder. If that is a constraint, it may be worth updating the docs to reflect it.\r\n\r\nThanks again for the help! ", "no, it is indeed supposed to work as you describe, specifying the dir from any point in your filesystem. \r\n\r\nLet us know if that's not the case.", "Will reopen this for clarity until the fix mentioned in https://github.com/huggingface/transformers/issues/8480#issuecomment-726731046 is deployed", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Ok, closing this for real now! 😎" ]

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[ "What do you mean it's not working? Could you provide all the information required in the template? What is your environment? What code are you running? What is the error shown? How do you use `from_pretrained`?", "What is meant is how do we utilize the “from_pretrained” functionality after saving the pipeline? I want to load the pipeline back up to start making predictions locally the saved file(s) below:\r\n\r\n\r\nimport transformers\r\n\r\nner_original = pipeline(\"ner\")\r\nner = pipeline(\"ner\",grouped_entities=True)\r\n\r\npath = 'path to folder'\r\n\r\nner.save_pretrained(path)\r\n\r\nI am currently running transformers==2.11.0 and python 3.7.4. I have tried:\r\n\r\npipe = transformers.pipeline(task=\"ner\", model=\"pytorch_model.bin\",\r\ntokenizer=\"tokenizer_config.json\")\r\n\r\nThis gave an error: ValueError: Unrecognized model in tokenizer_config.json. Should have a `model_type` key in its config.json\r\n\r\npipe = transformers.TokenClassificationPipeline(model=\"pytorch_model.bin\",\r\ntokenizer=\"tokenizer_config.json\")\r\n\r\nThis gave an error: AttributeError: 'str' object has no attribute 'config'\r\n\r\nChanging the tokenizer to \"config.json\" yielded the following error:\r\n\r\nUnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 64: invalid start byte\r\n", "This is the code which gives the error\r\n\r\nfrom transformers import AutoModel, AutoTokenizer\r\n\r\ntokenizer = AutoTokenizer.from_pretrained(path)\r\nmodel = AutoModel.from_pretrained(path)\r\n\r\nlabel_list = [\r\n \"O\", # Outside of a named entity\r\n \"B-MISC\", # Beginning of a miscellaneous entity right after another miscellaneous entity\r\n \"I-MISC\", # Miscellaneous entity\r\n \"B-PER\", # Beginning of a person's name right after another person's name\r\n \"I-PER\", # Person's name\r\n \"B-ORG\", # Beginning of an organisation right after another organisation\r\n \"I-ORG\", # Organisation\r\n \"B-LOC\", # Beginning of a location right after another location\r\n \"I-LOC\" # Location\r\n]\r\n\r\nsequence = \"Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very\" \\\r\n \"close to the Manhattan Bridge.\"\r\n\r\n# Bit of a hack to get the tokens with the special tokens\r\ntokens = tokenizer.tokenize(tokenizer.decode(tokenizer.encode(sequence)))\r\ninputs = tokenizer.encode(sequence, return_tensors=\"pt\")\r\n\r\noutputs = model(inputs)[0]\r\npredictions = torch.argmax(outputs, dim=2)\r\n\r\nprint([(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].tolist())])\r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "tiny fix, might as well do this in #7467 and close this one" ]

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[ "https://github.com/huggingface/transformers/tree/master/examples/rag#finetuning should help you :-) ", "Thanks" ]

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[ "Thanks!" ]

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[ "You got a little zero-shot BLEU boost it seems!\r\n\r\nThis Branch: 34.433 (on en-de)\r\nMaster: 34.4052\r\n\r\n", "@patrickvonplaten \r\n\r\nIs the docstring still wrong? [T5ForConditionalGeneration](https://huggingface.co/transformers/model_doc/t5.html?highlight=t5forconditional#transformers.T5ForConditionalGeneration), under `decoder_input_ids` says \"if both decoder_input_ids and decoder_inputs_embeds are both unset, decoder_input_ids takes the value of input_ids\"", "> @patrickvonplaten\r\n> \r\n> Is the docstring still wrong? [T5ForConditionalGeneration](https://huggingface.co/transformers/model_doc/t5.html?highlight=t5forconditional#transformers.T5ForConditionalGeneration), under `decoder_input_ids` says \"if both decoder_input_ids and decoder_inputs_embeds are both unset, decoder_input_ids takes the value of input_ids\"\r\n\r\nI can't find this docstring, can you link to it?", "> > @patrickvonplaten\r\n> > Is the docstring still wrong? [T5ForConditionalGeneration](https://huggingface.co/transformers/model_doc/t5.html?highlight=t5forconditional#transformers.T5ForConditionalGeneration), under `decoder_input_ids` says \"if both decoder_input_ids and decoder_inputs_embeds are both unset, decoder_input_ids takes the value of input_ids\"\r\n> \r\n> I can't find this docstring, can you link to it?\r\n\r\nIn T5, it's under method `forward` and argument `decoder_input_ids`. Here's [code link](https://github.com/huggingface/transformers/blob/eb3bd73ce35bfef56eeb722d697f2d39a06a8f8d/src/transformers/modeling_t5.py#L869)", "Oh yeah you're right that was a bad copy & past probably! Do you feel like opening a PR to fix it / delete it? That would be amazing :-) " ]

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

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

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[ "Gently pinging @ola13 here, she probably knows best which command to run to reproduce the eval results :-) ", "Hi @acslk, thanks for your post!\r\n\r\nYou should be able to reproduce paper results for the RAG Token model (44.1 EM on NQ) by evaluating `facebook/rag-token-nq` with 20 docs.\r\n\r\nAs for the RAG Sequence model - we have lost some quality when translating the checkpoint from `fairseq` (the experimentation framework we used to obtain the original paper results) to HuggingFace. We are now working on replicating the paper numbers in HF and we'll update the official `facebook/rag-sequence-nq` model weights once we have that so stay tuned!", "Thanks for the response, I tried the command above with RAG Token model and n_docs 20 on NQ test set and can confirm it matches paper results:\r\nINFO:__main__:F1: 51.44\r\nINFO:__main__:EM: 44.10", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Shouldn't we have a properly-typed self.state on the Trainer instance? I always find assigning instance properties within the code a bit messy", "We can put everything in the new `TrainerState`. I just did the same as for `self.epoch` or `self.global_step` (and there are plenty more).", "Yes I think that’d be nice ", "Closing this PR as this will require a bit more work then :-)" ]

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[ "Second this. \r\n\r\nhttps://github.com/deepset-ai/haystack may be useful to you. They leverage huggingface and have an DPR implementation with an end-to-end example. Will not be surprised to see RAG implemented soon.\r\n", "@Weilin37 Thanks. I'm also looking at the Faiss docs now (https://github.com/facebookresearch/faiss/wiki/Faiss-indexes).", "@lhoestq can maybe help here as well", "Yep I'm thinking of adding a script in `examples/rag` that shows how to create an indexed dataset for RAG.\r\nI'll let you know how it goes", "@lhoestq Can you please let me know on how we can index the custom datasets? Appreciate your help on this", "@lhoestq I have a bunch of documents to perform Q&A and currently, in the config it says, \r\ndataset (str, optional, defaults to \"wiki_dpr\") – A dataset identifier of the indexed dataset on HuggingFace AWS bucket (list all available datasets and ids using datasets.list_datasets()). So how can we create an indexed file and input that to the pretrained model for evaluation. ", "> @lhoestq I have a bunch of documents to perform Q&A and currently, in the config it says,\r\n> dataset (str, optional, defaults to \"wiki_dpr\") – A dataset identifier of the indexed dataset on HuggingFace AWS bucket (list all available datasets and ids using datasets.list_datasets()). So how can we create an indexed file and input that to the pretrained model for evaluation.\r\n\r\nYes right... We'll have to edit the `RagRetriever` and the `HfIndex` to accept custom ones.\r\nIf you wanto to give it a try in the meantime, feel free to do so :)", "Any progress on this @lhoestq @patrickvonplaten ? Awesome work guys :)", "@tholor @Timoeller Do you reckon you guys could integrate this work into haystack?", "@aced125 Yep, we will integrate RAG in Haystack soon (https://github.com/deepset-ai/haystack/issues/443).", "> Any progress on this @lhoestq @patrickvonplaten ? Awesome work guys :)\r\n\r\nYou can expect a PR by tomorrow", "Awesome thanks everyone @tholor @lhoestq @patrickvonplaten !!!!", "Thank you @lhoestq . Really appreciate for getting back quickly on this issue.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Hello everyone,\r\nI am interesting in studying how RAG behaves without the DPR retriever.\r\nFor example in the code below\r\n\r\n``from transformers import RagRetriever\r\nfrom transformers import RagTokenizer, RagRetriever, RagTokenForGeneration\r\n\r\nretriever = RagRetriever.from_pretrained('./rag-token-nq', indexed_dataset=dataset)\r\ntokenizer = RagTokenizer.from_pretrained(\"./rag-token-nq\")\r\nmodel = RagTokenForGeneration.from_pretrained(\"./rag-token-nq\", retriever=retriever)\r\n\r\n**input_dict = tokenizer.prepare_seq2seq_batch(\"How many people live in Paris?\", \"In Paris, there are 10 million people.\", return_tensors=\"pt\")**\r\ninput_ids = input_dict[\"input_ids\"]\r\n\r\nmodel = RagTokenForGeneration.from_pretrained(\"facebook/rag-token-nq\", retriever=retriever)\r\n\r\ngenerated_ids = model.generate(input_ids=input_ids, labels=input_dict[\"labels\"])\r\n\r\ngenerated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)\r\nprint(generated_string) ``\r\n\r\nIn the line '' **input_dict = tokenizer.prepare_seq2seq_batch(\"How many people live in Paris?\", \"In Paris, there are 10 million people.\", return_tensors=\"pt\")** ``, I want to use \"How many people live in Paris ?\" as the question and \"In Paris, there are 10 million people.\" the passage / context which should be used to generate the answer.\r\n\r\nKindly let me know how to do this?\r\n\r\nIs my understanding of the code correct and if not, how to go about it?\r\n\r\nThanks,\r\nKrishanu", "For RAG you can pass both your question as `input_ids` and your context as `context_input_ids` to `model.generate`.\r\nYou can provide several contexts for one question.\r\n\r\nYou can find more information in the documentation [here](https://huggingface.co/transformers/model_doc/rag.html#transformers.RagTokenForGeneration.generate)", "@lhoestq Thanks for the reply.\r\nThere is this doc_score parameter in the model.generate function. Is it necessary or optional?", "If you pass the `context_input_ids` you also need to provide the `doc_scores` indeed." ]

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[ "Can we see when the config is accessed (in your error message)? `model.config` should be accessed as sparsely as possible in `Trainer` to work with any kind of model and I'll probably remove the requirement entirely soon.", "`Seq2SeqTrainer` uses model.config 8 times. Mostly `pad_token_id` to avoid counting padding in the loss func.", "It should add an assert the model is a `PreTrainedModel` at __init__ just to be clean, then for your specific problem, it should use the function `self._actual_model()` to grab the config to avoid your error (e.g., `self.model.config` -> `self._actual_model().config`).\r\n\r\n`Trainer` is on its way to fully handle models without config, see #7464.", "OK. I reduced scope of this PR to just the `Tensor` -> `tensor`." ]

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[ "@sgugger After that bug fix, the next bug is:\r\n```\r\nRuntimeError: Precision loss when unpacking double\r\n tensorized_scalar = torch.Tensor(scalars).cuda()\r\nRuntimeError: Precision loss when unpacking double\r\nTraceback (most recent call last):\r\n File \"finetune_trainer.py\", line 442, in <module>\r\n main()\r\n File \"finetune_trainer.py\", line 383, in main\r\n model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None\r\n File \"/home/shleifer/transformers_fork/src/transformers/trainer.py\", line 809, in train\r\n self.log(logs)\r\n File \"/home/shleifer/transformers_fork/src/transformers/trainer.py\", line 1031, in log\r\n total_flos = distributed_broadcast_scalars([self.total_flos]).sum().item()\r\n File \"/home/shleifer/transformers_fork/src/transformers/trainer_utils.py\", line 206, in distributed_broadcast_scalars\r\n tensorized_scalar = torch.Tensor(scalars).cuda()\r\nRuntimeError: Precision loss when unpacking double\r\n```\r\n### Env\r\n\r\nApex installed.\r\n```\r\n- `transformers` version: 3.3.1\r\n- Platform: Linux-4.9.0-11-amd64-x86_64-with-debian-9.12\r\n- Python version: 3.7.4\r\n- PyTorch version (GPU?): 1.5.1+cu101 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: <fill in>\r\n- Using distributed or parallel set-up in script?: <fill in>\r\n```", "The next error is on @TevenLeScao ", "I fixed it, will stuff into 1 PR when everything is working." ]

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[ "Thanks! FYI we'll have proper model versioning in ~1 month or so" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/7457?src=pr&el=h1) Report\n> Merging [#7457](https://codecov.io/gh/huggingface/transformers/pull/7457?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/52e8392b7ebd4ebc7b796e8f14b9dae271139f5f?el=desc) will **increase** coverage by `2.09%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/7457?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #7457 +/- ##\n==========================================\n+ Coverage 77.07% 79.17% +2.09% \n==========================================\n Files 181 181 \n Lines 35858 35858 \n==========================================\n+ Hits 27638 28391 +753 \n+ Misses 8220 7467 -753 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/7457?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/7457/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fbWFyaWFuLnB5) | `32.75% <0.00%> (-66.38%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/7457/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9ncHQyLnB5) | `65.38% <0.00%> (-29.59%)` | :arrow_down: |\n| [src/transformers/tokenization\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/7457/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdDUucHk=) | `66.34% <0.00%> (-28.85%)` | :arrow_down: |\n| [src/transformers/modeling\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/7457/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ194bG5ldC5weQ==) | `60.70% <0.00%> (-22.68%)` | :arrow_down: |\n| [src/transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/7457/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `81.51% <0.00%> (-15.11%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/7457/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `85.71% <0.00%> (-0.51%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_xlm.py](https://codecov.io/gh/huggingface/transformers/pull/7457/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl94bG0ucHk=) | `58.52% <0.00%> (+39.78%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/7457/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mbGF1YmVydC5weQ==) | `89.67% <0.00%> (+68.14%)` | :arrow_up: |\n| [src/transformers/modeling\\_reformer.py](https://codecov.io/gh/huggingface/transformers/pull/7457/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yZWZvcm1lci5weQ==) | `94.60% <0.00%> (+77.88%)` | :arrow_up: |\n| [src/transformers/configuration\\_reformer.py](https://codecov.io/gh/huggingface/transformers/pull/7457/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3JlZm9ybWVyLnB5) | `100.00% <0.00%> (+78.37%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/7457?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/7457?src=pr&el=footer). Last update [52e8392...f4761cf](https://codecov.io/gh/huggingface/transformers/pull/7457?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/7456?src=pr&el=h1) Report\n> Merging [#7456](https://codecov.io/gh/huggingface/transformers/pull/7456?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/52e8392b7ebd4ebc7b796e8f14b9dae271139f5f?el=desc) will **decrease** coverage by `0.23%`.\n> The diff coverage is `75.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/7456?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #7456 +/- ##\n==========================================\n- Coverage 77.07% 76.84% -0.24% \n==========================================\n Files 181 181 \n Lines 35858 35860 +2 \n==========================================\n- Hits 27638 27555 -83 \n- Misses 8220 8305 +85 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/7456?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/7456/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `82.97% <75.00%> (-0.40%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_funnel.py](https://codecov.io/gh/huggingface/transformers/pull/7456/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mdW5uZWwucHk=) | `18.53% <0.00%> (-75.51%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/7456/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `24.25% <0.00%> (-73.56%)` | :arrow_down: |\n| [src/transformers/modeling\\_layoutlm.py](https://codecov.io/gh/huggingface/transformers/pull/7456/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19sYXlvdXRsbS5weQ==) | `25.06% <0.00%> (-69.40%)` | :arrow_down: |\n| [src/transformers/activations\\_tf.py](https://codecov.io/gh/huggingface/transformers/pull/7456/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9uc190Zi5weQ==) | `54.16% <0.00%> (-20.84%)` | :arrow_down: |\n| [src/transformers/configuration\\_layoutlm.py](https://codecov.io/gh/huggingface/transformers/pull/7456/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2xheW91dGxtLnB5) | `80.00% <0.00%> (-20.00%)` | :arrow_down: |\n| [src/transformers/tokenization\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/7456/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYXV0by5weQ==) | `83.58% <0.00%> (-8.96%)` | :arrow_down: |\n| [src/transformers/configuration\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/7456/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2JhcnQucHk=) | `90.00% <0.00%> (-4.00%)` | :arrow_down: |\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/7456/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `96.68% <0.00%> (-0.67%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/7456/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `86.90% <0.00%> (-0.32%)` | :arrow_down: |\n| ... and [10 more](https://codecov.io/gh/huggingface/transformers/pull/7456/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/7456?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/7456?src=pr&el=footer). Last update [52e8392...c20be03](https://codecov.io/gh/huggingface/transformers/pull/7456?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]