Datasets:

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DanielPFlorian

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Transformers-Github-Issues

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[ "Looking into it right now (cc @Pierrci)", "looks like we were supporting lightweight tags but not annotated ones. ", "Should be fixed: https://huggingface.co/deepset/roberta-base-squad2/tree/v2.0", "That fixed the problem! Thanks very much", "Thanks, and please keep the feedback coming! <3" ]

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[ "feel free to merge this when it's a good time!" ]

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[ "Please don't hesitate to suggest a PR, `run_name` is there just for this reason!", "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", "It will be useful to have this, and it will organize all the experiments better.", "Is there any update on this issue and #12841? I have a very simple one-line solution of passing `args.run_name` (which currently serves for `wandb`) to `mlflow.start_run` that can fix this. I can submit a PR in case of need.", "@HenryMaguire can you send link to your notebook or colab?" ]

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[ "I have tested both \"refactor_t5\" and \"major_t5_refactor\" branches using \"T5ForConditionalGeneration\".\r\n\r\nI didn't re-convert the Tensorflow to Pytorch, since you already told me the conversion process is not the problem.\r\n\r\nDoesn't seem it solved our issue yet, but it gives different rubbish output. Maybe, it is a good step into the right direction.\r\n\r\nThanks Patrick.\r\n\r\n", "Hi @patrickvonplaten, does this PR effects the checkpoint one gets when calling ```AutoModel.from_pretrained(\"t5-3b\")```?\r\n\r\n I am investigating why my results with transformers 3.3.1 and T5 changed and encountered this, according to the date seems like it was merged in version 3.5.0. (edit: I see now it was merged in 4.0.0 https://github.com/huggingface/transformers/releases/tag/v4.0.0)\r\n\r\nI wonder if this changes the T5 weights/checkpoint I get with my version?" ]

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[ "Hi, this is unfortunately a limit of this tokenizer. We try to stay as close as possible to the original implementation, so we will not be able to change this behavior.", "I see. Well, it's not a big deal, as the characters it replaces are quite rare and not really important, at least not for my application. So, I just replace them upfront in the text to make sure that the output of the tokenizer still matches the input.\r\n\r\nThanks for the awesome work you are doing by providing this and other libraries!", "Glad you like it :)" ]

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[ "For fixing the check_code_quality failure, I run `make style` in the repository, but it tries to re-format so many files.\r\nShould I check some settings for `black`?", "I changed the environment and made it work again according to the instructions, and it seems that formatting with black works appropriately. Sorry for the bother you.", "@patrickvonplaten \r\nThank you for reviewing and merging the PR! I'm so happy to read your comment.\r\n\r\nI haven't tried using the full dataset for fine-tuning ProphetNet with the seq2seq trainer, but I think I can try it by adding some modifications to my test code used during my implementation process.\r\nI will try it and would like to post the results on the URL if I can get something interesting!\r\n", "@patrickvonplaten \r\nI've just posted my fine-tuning experiment result on https://discuss.huggingface.co/t/how-can-i-do-text-summarization-using-prophetnet/1661/2.\r\n\r\nI'm sorry it is not the case of using the full dataset.\r\nConsidering the time limitations of the execution environment, I used only about one-tenth of the dataset for now, but I think we could get better results if we used the entire dataset.", "That's already of great help - thank you so much!", "It's my pleasure!" ]

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[ "The authors of the Transformers library wrote a script for this. You can find it under the examples folder -> language modeling [here](https://github.com/huggingface/transformers/tree/master/examples/language-modeling#robertabertdistilbert-and-masked-language-modeling). ", "> The authors of the Transformers library wrote a script for this. You can find it under the examples folder -> language modeling [here](https://github.com/huggingface/transformers/tree/master/examples/language-modeling#robertabertdistilbert-and-masked-language-modeling).\r\n\r\nThank you! I will check it out.", "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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[ "You don't need to do either of those things! You should add tokens to your tokenizer by leveraging the `add_tokens()` method, and then resize your model's embedding matrix.\r\n\r\nThen, you should train your model on a dataset that has those entities so that it understands the meaning of these entities. It seems to be what you're doing here, so just make sure to add the tokens to your tokenizer first. You can see the doc about it [here](https://huggingface.co/transformers/internal/tokenization_utils.html?highlight=add_tokens#transformers.tokenization_utils_base.SpecialTokensMixin.add_tokens).\r\n\r\nAlso, we try to keep the github issues only for bugs and feature requests. Please ask questions/discussions on the [instead](https://discuss.huggingface.co). Thanks!" ]

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[ "You cannot save directly remotely like this (though it could be nice to be able to do this in the future, cc @madlag).\r\n\r\nYou need to first create a repo with `transformers-cli repo create`, or directly on the website.\r\n\r\nThen clone it locally and add your files, then push.\r\n\r\nHopefully #8520 makes it clearer?\r\n", "yes. Thankyou. :)", "Hello, I have the same issue with different outputs. I followed all steps in https://huggingface.co/transformers/model_sharing.html \r\nin order, entered my account with transformers-cli login, created repo, installed lfs, cloned repo, added BERT model via \"git add BERTMODEL\", committed and pushed but always I got the same error. \r\n\r\nremote: \r\nremote: -------------------------------------------------------------------------\r\nremote: Your push was rejected because it contains files larger than 10M.\r\nremote: Please use https://git-lfs.github.com/ to store larger files.\r\nremote: -------------------------------------------------------------------------\r\nremote: \r\nremote: Offending files:\r\nremote: - BERTMODEL (ref: refs/heads/main)\r\nTo https://huggingface.co/Serdar/your-model-name\r\n ! [remote rejected] main -> main (pre-receive hook declined)\r\n\r\nOS: Ubuntu 20.04\r\n\r\nI already used \"git lfs install\" but I could not figure out this problem. I wish someone can help", "@serdarakyol Please make sure you read the Getting started guide at https://git-lfs.github.com/ – in your case I think you didn't lfs-track your actual model file", "@julien-c Thank you so much. fixed the problem" ]

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[ "Hi there! We love contributions, but metrics should now be implemented directly in the datasets library, not inside Transformers. So you should check there if it does not already exist, and if not, suggest a Pr on that repository :-)", "Okay then, I will close it." ]

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[ "Maybe @jplu has an idea!", "Hello @MarsSu0618 \r\n\r\nThe bad news is that it is currently not possible to train an LM from scratch or fine tune it with `.fit()`. The good one is that we are heavily working on it and should be feasible soon.\r\n\r\nSorry for the inconvenience.", "@jplu \r\nSo i can not fine tune Bert MLM model with fit(), right? \r\nBecause i alter pytorch framework(train loop) and it can be work.\r\n\r\nIn addtition, I guess maybe should be divide feature and labels. So I change new tensor as follows:\r\n```python\r\n({'attention_mask': <tf.Tensor: shape=(256,), dtype=int32, numpy=\r\n array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)>,\r\n 'input_ids': <tf.Tensor: shape=(256,), dtype=int32, numpy=\r\n array([ 101, 1962, 102, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0], dtype=int32)>,\r\n 'token_type_ids': <tf.Tensor: shape=(256,), dtype=int32, numpy=\r\n array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)>},\r\n <tf.Tensor: shape=(256,), dtype=int32, numpy=\r\n array([-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\r\n -100, -100, -100], dtype=int32)>)\r\n```\r\n\r\nBut Error message is change\r\n```\r\nWARNING:tensorflow:Gradients do not exist for variables ['tf_bert_for_masked_lm_6/bert/pooler/dense/kernel:0', 'tf_bert_for_masked_lm_6/bert/pooler/dense/bias:0'] when minimizing the loss.\r\nWARNING:tensorflow:Gradients do not exist for variables ['tf_bert_for_masked_lm_6/bert/pooler/dense/kernel:0', 'tf_bert_for_masked_lm_6/bert/pooler/dense/bias:0'] when minimizing the loss.\r\n/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/indexed_slices.py:432: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\r\n \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n<ipython-input-110-48f551163bd4> in <module>()\r\n 4 \r\n 5 \r\n----> 6 model.fit(batched_tfdataset, epochs=1, verbose=1)\r\n\r\n10 frames\r\n/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\r\n 971 except Exception as e: # pylint:disable=broad-except\r\n 972 if hasattr(e, \"ag_error_metadata\"):\r\n--> 973 raise e.ag_error_metadata.to_exception(e)\r\n 974 else:\r\n 975 raise\r\n\r\nTypeError: in user code:\r\n\r\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function *\r\n return step_function(self, iterator)\r\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function **\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run\r\n return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\r\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica\r\n return self._call_for_each_replica(fn, args, kwargs)\r\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica\r\n return fn(*args, **kwargs)\r\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step **\r\n outputs = model.train_step(data)\r\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:759 train_step\r\n self.compiled_metrics.update_state(y, y_pred, sample_weight)\r\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:409 update_state\r\n metric_obj.update_state(y_t, y_p, sample_weight=mask)\r\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/metrics_utils.py:90 decorated\r\n update_op = update_state_fn(*args, **kwargs)\r\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/metrics.py:176 update_state_fn\r\n return ag_update_state(*args, **kwargs)\r\n\r\n TypeError: update_state() got multiple values for argument 'sample_weight'\r\n```\r\n\r\nhow to solve the problem, thanks.", "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 and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread." ]

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[ "Thank you both for your reviews!" ]

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[ "The problem is that you are aggregating all your predictions on the TPU host, with a big evaluation set. You should use the `eval_accumulation_steps` argument to pass the predictions back to the CPU every, let's say 20 evaluation steps for instance to avoid the OOM.", "Thanks for the response!\r\nI started a version of the workload that uses that flag and I'll update here once it finishes the training loop", "With that flag, I don't get the same OOM error. Instead I see:\r\n```\r\nE 2020-11-13T05:36:28.200219317Z 11/13/2020 05:36:28 - INFO - run_glue - *** Evaluate ***\r\nE 2020-11-13T05:36:28.201262406Z [INFO|trainer.py:388] 2020-11-13 05:36:28,200 >> The following columns in the evaluation set don't have a corresponding argument in `XLNetForSequenceClassification.forward` and have been ignored: premise, hypothesis, idx.\r\nE 2020-11-13T05:36:28.205409874Z [INFO|trainer.py:1387] 2020-11-13 05:36:28,204 >> ***** Running Evaluation *****\r\nE 2020-11-13T05:36:28.205583892Z [INFO|trainer.py:1388] 2020-11-13 05:36:28,205 >> Num examples = 9815\r\nE 2020-11-13T05:36:28.205718259Z [INFO|trainer.py:1389] 2020-11-13 05:36:28,205 >> Batch size = 32\r\nE 2020-11-13T05:43:14.914374736Z \r\n 0%| | 0/39 [00:00<?, ?it/s]\r\n 5%|5 | 2/39 [00:10<03:14, 5.26s/it]\r\n 8%|7 | 3/39 [00:21<04:09, 6.92s/it]\r\n 10%|# | 4/39 [00:31<04:41, 8.04s/it]\r\n 13%|#2 | 5/39 [00:42<05:02, 8.89s/it]\r\n 15%|#5 | 6/39 [00:53<05:13, 9.51s/it]\r\n 18%|#7 | 7/39 [01:05<05:21, 10.04s/it]\r\n 21%|## | 8/39 [01:16<05:20, 10.34s/it]\r\n 23%|##3 | 9/39 [01:27<05:19, 10.64s/it]\r\n 26%|##5 | 10/39 [01:38<05:10, 10.71s/it]Exception in device=TPU:0: tensorflow/compiler/xla/xla_client/mesh_service.cc:364 : Failed to meet rendezvous 'eval_preds_1_0': Sent message larger than max (1342183400 vs. 1073741824) (8)\r\nE 2020-11-13T05:43:14.914454025Z Traceback (most recent call last):\r\nE 2020-11-13T05:43:14.914462893Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py\", line 329, in _mp_start_fn\r\nE 2020-11-13T05:43:14.914469141Z _start_fn(index, pf_cfg, fn, args)\r\nE 2020-11-13T05:43:14.914474634Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py\", line 323, in _start_fn\r\nE 2020-11-13T05:43:14.914481036Z fn(gindex, *args)\r\nE 2020-11-13T05:43:14.914486906Z File \"/transformers/examples/text-classification/run_glue.py\", line 414, in _mp_fn\r\nE 2020-11-13T05:43:14.914495083Z main()\r\nE 2020-11-13T05:43:14.914623679Z File \"/transformers/examples/text-classification/run_glue.py\", line 370, in main\r\nE 2020-11-13T05:43:14.914648860Z eval_result = trainer.evaluate(eval_dataset=eval_dataset)\r\nE 2020-11-13T05:43:14.914657065Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer.py\", line 1313, in evaluate\r\nE 2020-11-13T05:43:14.914667922Z prediction_loss_only=True if self.compute_metrics is None else None,\r\nE 2020-11-13T05:43:14.914675010Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer.py\", line 1431, in prediction_loop\r\nE 2020-11-13T05:43:14.914681724Z preds_gatherer.add_arrays(self._gather_and_numpify(preds_host, \"eval_preds\"))\r\nE 2020-11-13T05:43:14.914712087Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer.py\", line 1474, in _gather_and_numpify\r\nE 2020-11-13T05:43:14.914718679Z tensors = nested_xla_mesh_reduce(tensors, name)\r\nE 2020-11-13T05:43:14.914724791Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer_pt_utils.py\", line 112, in nested_xla_mesh_reduce\r\nE 2020-11-13T05:43:14.914731470Z return type(tensors)(nested_xla_mesh_reduce(t, f\"{name}_{i}\") for i, t in enumerate(tensors))\r\nE 2020-11-13T05:43:14.914737871Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer_pt_utils.py\", line 112, in <genexpr>\r\nE 2020-11-13T05:43:14.914744687Z return type(tensors)(nested_xla_mesh_reduce(t, f\"{name}_{i}\") for i, t in enumerate(tensors))\r\nE 2020-11-13T05:43:14.914751282Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer_pt_utils.py\", line 112, in nested_xla_mesh_reduce\r\nE 2020-11-13T05:43:14.914761474Z return type(tensors)(nested_xla_mesh_reduce(t, f\"{name}_{i}\") for i, t in enumerate(tensors))\r\nE 2020-11-13T05:43:14.914768115Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer_pt_utils.py\", line 112, in <genexpr>\r\nE 2020-11-13T05:43:14.914774306Z return type(tensors)(nested_xla_mesh_reduce(t, f\"{name}_{i}\") for i, t in enumerate(tensors))\r\nE 2020-11-13T05:43:14.914780896Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer_pt_utils.py\", line 113, in nested_xla_mesh_reduce\r\nE 2020-11-13T05:43:14.914788363Z return xm.mesh_reduce(name, tensors, torch.cat)\r\nE 2020-11-13T05:43:14.914794375Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch_xla/core/xla_model.py\", line 909, in mesh_reduce\r\nE 2020-11-13T05:43:14.914801139Z xdata = rendezvous(tag, bio.getvalue())\r\nE 2020-11-13T05:43:14.914806782Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch_xla/core/xla_model.py\", line 861, in rendezvous\r\nE 2020-11-13T05:43:14.914813625Z return torch_xla._XLAC._xla_rendezvous(get_ordinal(), tag, payload, replicas)\r\nE 2020-11-13T05:43:14.914819959Z RuntimeError: tensorflow/compiler/xla/xla_client/mesh_service.cc:364 : Failed to meet rendezvous 'eval_preds_1_0': Sent message larger than max (1342183400 vs. 1073741824) (8)\r\nE 2020-11-13T05:43:15.468075089Z \r\n 26%|##5 | 10/39 [02:11<06:20, 13.12s/it]\r\n```\r\n\r\nI'll try some things on my side. It looks like the accumulation was fine for \"eval_losses\" but then failed on \"eval_preds\". I will just try a more frequent eval accumulation and a smaller batch size and see if that results in a smaller message being sent between TPU/CPU", "It still looks like a problem of memory (from the `Sent message larger than max` in the stack trace). Maybe try a lower `eval_accumulation_step`?\r\n\r\nMaybe we should move those tensors to the CPU before doing the mesh reduce to save a bit of host memory (right now they are reduced on all hosts *then* moved).", "I have a version running now with half the accumulation size and half the eval batch size.\r\n\r\nMemory saving on device is probably always good but in this case it seems to be complaining about the size of the transfer payload. If you don't reduce before moving, probably the size of the transfer would be even bigger", "I tried with `--eval_accumulation_steps 5` instead of 10 and `--per_device_eval_batch_size 16` instead of 32 and ran into:\r\n\r\n`Exception in device=TPU:4: tensorflow/compiler/xla/xla_client/mesh_service.cc:364 : Failed to meet rendezvous 'eval_preds_1_0': Received message larger than max (335550440 vs. 4194304) (8)`\r\n\r\nThe 335550440 number is much less than the previous error message's larger number 1342183400. I will try `--eval_accumulation_steps 1` just in case but I'm wondering if this error means something else than what I was assuming", "`eval_accumulation_steps 1` resulted in the same error:\r\n\r\n```\r\nE 2020-11-17T00:27:24.619933766Z main()\r\nE 2020-11-17T00:27:24.619937169Z File \"/transformers/examples/text-classification/run_glue.py\", line 370, in main\r\nE 2020-11-17T00:27:24.619940804Z eval_result = trainer.evaluate(eval_dataset=eval_dataset)\r\nE 2020-11-17T00:27:24.619944189Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer.py\", line 1313, in evaluate\r\nE 2020-11-17T00:27:24.619947752Z prediction_loss_only=True if self.compute_metrics is None else None,\r\nE 2020-11-17T00:27:24.619951181Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer.py\", line 1431, in prediction_loop\r\nE 2020-11-17T00:27:24.619954905Z preds_gatherer.add_arrays(self._gather_and_numpify(preds_host, \"eval_preds\"))\r\nE 2020-11-17T00:27:24.619958638Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer.py\", line 1474, in _gather_and_numpify\r\nE 2020-11-17T00:27:24.619962458Z tensors = nested_xla_mesh_reduce(tensors, name)\r\nE 2020-11-17T00:27:24.619965855Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer_pt_utils.py\", line 112, in nested_xla_mesh_reduce\r\nE 2020-11-17T00:27:24.619976695Z return type(tensors)(nested_xla_mesh_reduce(t, f\"{name}_{i}\") for i, t in enumerate(tensors))\r\nE 2020-11-17T00:27:24.619980624Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer_pt_utils.py\", line 112, in <genexpr>\r\nE 2020-11-17T00:27:24.619984750Z return type(tensors)(nested_xla_mesh_reduce(t, f\"{name}_{i}\") for i, t in enumerate(tensors))\r\nE 2020-11-17T00:27:24.619988344Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer_pt_utils.py\", line 112, in nested_xla_mesh_reduce\r\nE 2020-11-17T00:27:24.619992533Z return type(tensors)(nested_xla_mesh_reduce(t, f\"{name}_{i}\") for i, t in enumerate(tensors))\r\nE 2020-11-17T00:27:24.619996086Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer_pt_utils.py\", line 112, in <genexpr>\r\nE 2020-11-17T00:27:24.619999738Z return type(tensors)(nested_xla_mesh_reduce(t, f\"{name}_{i}\") for i, t in enumerate(tensors))\r\nE 2020-11-17T00:27:24.620003216Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/transformers/trainer_pt_utils.py\", line 113, in nested_xla_mesh_reduce\r\nE 2020-11-17T00:27:24.620006752Z return xm.mesh_reduce(name, tensors, torch.cat)\r\nE 2020-11-17T00:27:24.620010015Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch_xla/core/xla_model.py\", line 909, in mesh_reduce\r\nE 2020-11-17T00:27:24.620013568Z xdata = rendezvous(tag, bio.getvalue())\r\nE 2020-11-17T00:27:24.620016833Z File \"/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch_xla/core/xla_model.py\", line 861, in rendezvous\r\nE 2020-11-17T00:27:24.620020510Z return torch_xla._XLAC._xla_rendezvous(get_ordinal(), tag, payload, replicas)\r\nE 2020-11-17T00:27:24.620024011Z RuntimeError: tensorflow/compiler/xla/xla_client/mesh_service.cc:364 : Failed to meet rendezvous 'eval_preds_1_0': Received message larger than max (67114984 vs. 4194304) (8)\r\n```", "It may be linked to the issue of XLNet outputing its memories on top of the logits (there is a PR under review to fix that).", "That sounds plausible since this issue is only affecting xlnet and none of our other tests.\r\n\r\nIs this the right PR: https://github.com/huggingface/transformers/pull/8567 ?", "Yes this PR will fix that, but current v4 release candidate should have another fix on the `Trainer` side (which basically ignores some of the keys in the model outputs).", "Looks like #8567 was submitted and now our xlnet test started passing. Thank you!", "Glad to hear it's fixed your issue :-) " ]

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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", "Could this be reopened. I'm facing the same issue.", "@sk- Same. Still getting this with `distilroberta-base`", "Could you solve it? ", "Hi @Bachstelze, if you're still experiencing this problem, could you open a new issue? Since the original issue is old and there have been many changes to the modeling and tokenization code we can't be sure the same thing is being addressed. " ]

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[ "Thanks for reporting !\r\nIndeed the index was renamed recently. I fixed it, it should be good now", "Got it. Upon re-trying the code, it works fine. " ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "I know it's an old issue but I just came across this page.\r\n\r\nThe original PubMedBERT checkpoint didn't have the mask prediction heads, but we updated the checkpoint ~10 months ago \r\nhttps://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract\r\n\r\nAlso, we have a new biomed+clinical domain-specific model if you're interested: https://huggingface.co/microsoft/BiomedVLP-CXR-BERT-general\r\n\r\n@rahuln " ]

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[ "And `git-lfs` is already installed in my local.\r\n```bash\r\n$ git lfs install\r\nUpdated git hooks.\r\nGit LFS initialized.\r\n```\r\n\r\nWhat should I do?", "Well, it's magical. It works if I clone it again. But this time is different from the first time.\r\n\r\n- The first time, the model file seems not complete.\r\n```bash\r\n/data2/wiki_zh ⌚ 1:44:35\r\n$ git clone https://huggingface.co/mymusise/gpt2-medium-chinese\r\nCloning into 'gpt2-medium-chinese'...\r\nremote: Enumerating objects: 15, done.\r\nremote: Counting objects: 100% (15/15), done.\r\nremote: Compressing objects: 100% (14/14), done.\r\nremote: Total 15 (delta 3), reused 0 (delta 0), pack-reused 0\r\nUnpacking objects: 100% (15/15), done.\r\n\r\n/data2/wiki_zh ⌚ 1:44:50\r\n$ cd gpt2-medium-chinese\r\n\r\n/data2/wiki_zh/gpt2-medium-chinese on  main ⌚ 1:44:56\r\n$ ls\r\nconfig.json tf_model.h5 vocab.txt\r\n\r\n/data2/wiki_zh/gpt2-medium-chinese on  main ⌚ 1:44:56\r\n$ ls -lh\r\ntotal 44K\r\n-rw-rw-r-- 1 mymusise mymusise 849 11月 13 01:44 config.json\r\n-rw-rw-r-- 1 mymusise mymusise 135 11月 13 01:44 tf_model.h5\r\n-rw-rw-r-- 1 mymusise mymusise 35K 11月 13 01:44 vocab.txt\r\n```\r\n\r\n- When I clone model repo again, the model file seems complete.\r\n\r\n```bash\r\n/data2/wiki_zh ⌚ 2:40:12\r\n$ rm -rf gpt2-medium-chinese\r\n\r\n/data2/wiki_zh ⌚ 2:40:16\r\n$ git clone https://huggingface.co/mymusise/gpt2-medium-chinese \r\nCloning into 'gpt2-medium-chinese'...\r\nremote: Enumerating objects: 15, done.\r\nremote: Counting objects: 100% (15/15), done.\r\nremote: Compressing objects: 100% (14/14), done.\r\nremote: Total 15 (delta 3), reused 0 (delta 0), pack-reused 0\r\nUnpacking objects: 100% (15/15), done.\r\n\r\n/data2/wiki_zh/gpt2-medium-chinese on  main ⌚ 2:43:08\r\n$ ls -lh \r\ntotal 1.2G\r\n-rw-rw-r-- 1 mymusise mymusise 849 11月 13 02:40 config.json\r\n-rw-rw-r-- 1 mymusise mymusise 1.2G 11月 13 02:42 tf_model.h5\r\n-rw-rw-r-- 1 mymusise mymusise 35K 11月 13 02:40 vocab.txt\r\n```\r\n\r\nThen I can push model successfully when I update the model file.", "I think, the reason why I push fails may be because I haven't added git-lfs before I `git add` the model file for the first time. `git lfs install` may not work after adding a big model file.", "Yes, you need to run `git lfs install` before adding files. I'll make that clearer in the documentation", "alternatively your can use ``` git lfs migrate import --everything ``` even before adding file without lfs. This will reindex files and let you push them using git lfs", "will add this to my upcoming video about `git-lfs` @jqueguiner ❤️ ", "I have to admit I'm lazy sometimes\r\n\r\n", "Maybe this one could help (for future searchers ;) )\r\n```\r\nhuggingface-cli lfs-enable-largefiles\r\n```\r\nI had the same problem and was curious why it's working from Trainer that uses huggingface_hub\r\nand found that need to run \"huggingface-cli lfs-enable-largefiles\"\r\nhttps://github.com/huggingface/huggingface_hub/blob/2e81cf3ec04b0dd5ce2acc92d25f8261a8484f3e/src/huggingface_hub/commands/lfs.py#L45\r\n```\r\nThis should be executed once for each model repo that contains a model file >5GB. It's documented in the error\r\n message you get if you just try to git push a 5GB file without having enabled it before.\r\n```" ]

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[ "Hello!\r\n\r\nYour way of computing the loss is wrong. I suggest you to look at how we compute it [here](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_tf_gpt2.py#L650) and [here](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_tf_utils.py#L125). You should also rewrite your metric the same way.", "I know but changing the loss metric won't fix the ValueError.", "It is. Just look at how we do it. Including in the tests.", "Met the same error before, it works for me with removing the `metrics` params when `model.compile`. But I think it's a bad idea.", "Hi, there. I found it won't raise this error if `batch size` == `n_head`. For example, LysandreJik's [gits](https://gist.github.com/LysandreJik/c958925768eb6a9a72609ea99561d1cb) works only `BATCH_SIZE = 12`", "~~Hey, guys. If the output of other layers was used rarely, can we add a controller to select whether return multi-layer logits or not? (correct me if it's necessary to return multi-layer logits.)~~\r\n\r\n~~If we can do this, this [pull request](https://github.com/huggingface/transformers/pull/8584) may help.~~", "@bjourne Hey guy, I think adding `output_attentions=False` and `output_hidden_states=False` may help:\r\n```\r\nmodel = TFGPT2LMHeadModel(GPT2Config(output_attentions=False, output_hidden_states=False, use_cache=False))\r\n```", "I haven't had the chance to try that. Maybe someone else can? Will the trained network with `output_hidden_states=True` though? You need to set it to True when generating text.", "Hello,\r\n\r\nI got the same error.\r\nWhen I tried to set `output_attentions = False`, it didn't seem to be in GPT2Config.\r\nLooking at the [documentation](https://huggingface.co/transformers/model_doc/gpt2.html#gpt2config), GPT2Config doesn't have that parameter, where should I set it?", "> Hello,\r\n> \r\n> I got the same error.\r\n> When I tried to set `output_attentions = False`, it didn't seem to be in GPT2Config.\r\n> Looking at the [documentation](https://huggingface.co/transformers/model_doc/gpt2.html#gpt2config), GPT2Config doesn't have that parameter, where should I set it?\r\n\r\n`output_attentions` is a Parameter of `PretrainedConfig` which is the SuperClass of `GPT2Config`, [see](https://huggingface.co/transformers/main_classes/configuration.html#transformers.PretrainedConfig) ", "Thank you for your reply.\r\n\r\n> PretrainedConfig which is the SuperClass of GPT2Config\r\n\r\nI see, I understand.\r\nI noticed that the version of the transformer I'm using was out of date :cry: \r\n\r\n> GPT2Config(output_attentions=False, output_hidden_states=False, use_cache=False)\r\n\r\nI used the latest version and it worked without any errors!\r\nYour advice was very helpful, thank you!", "This issue has been stale for 1 month." ]

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[ "Hello!\r\n\r\nUnfortunately this is a known bug we have with few of the TF models. We are currently reworking all the TF models to solve this issue among others.", "@jplu I tried the same thing with Pytorch model also. It is also giving error. Any idea if I can get the attentions with pytorch?", "You get the same error with PyTorch? For PyTorch I will let @patrickvonplaten take the lead to help you, he knows better than me.", "The error is the same instead of list it just says tuple", "Hey @pathikchamaria - is it possible to update your version? 2.11 is very outdated by now. Could you try again with the current version of transformers (3.5) ? ", "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", "@jplu are there any updates on this on the tensorflow side?" ]

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[ "Ah, I guess I figured that out. Does it happen when the training data set of the tokenizer never had this token at the beginning of a word but only inside a word?" ]

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[ "Thanks for the fix!" ]

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[ "I guess the issue is that you're using `anaconda` here. Until version v4.0.0, we're not entirely compatible with anaconda as SentencePiece is not on a conda channel.\r\n\r\nIn the meantime, we recommend installing `transformers` in a pip virtual env:\r\n\r\n```shell-script\r\npython -m venv .env\r\nsource .env/bin/activate\r\npip install -e .\r\n```", "@LysandreJik Thanks for your answer! Actually it was not an `anaconda` issue.\r\n\r\nI found the solution! There're 2 version issues in the install requirements. \r\nSee below the steps - but I had to reinstall Python back to 3.8 as Torch/TorchVision don't support 3.9 yet.\r\n\r\n1. Copy content of GitHub repo in a “transformers” folder: https://github.com/huggingface/transformers\r\n\r\n2. `cd transformers`\r\n\r\n3. Change all the `tokenizers` 0.9.3 reference to 0.9.4 in transformers files\r\n\r\n4. Change all the `sentencepiece` 0.1.91 reference to 0.1.94 in transformers files\r\n\r\n5. `brew install pkgconfig`\r\n\r\n6. `python3.8 setup.py install`\r\n\r\nAnd voila! I hope it helps lots of folks struggling. 👍 \r\n\r\n", "@MoonshotQuest - what are \"transformers files\" in the reply above?" ]

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[ "Yep, they way I would go about this would be to programmatically check whether the file is text-only (non-binary) and between 100 and 200 bytes. If it is (and we expected a weights file), it's probably a lfs pointer file.", "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", "unstale" ]

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[ "This is really cool @Pogayo, thanks for sharing.\r\n\r\nIf you can, please consider adding sample inputs for the inference widget, either in DefaultWidget.ts (see https://huggingface.co/docs#how-can-i-control-my-models-widgets-example-inputs) or in this model card.\r\n\r\nWill also add Acholi to the list in https://huggingface.co/languages", "I don't know if this is the right place to ask, apologies in advance - I am trying to translate on the model page and getting this error:\r\n\r\nI have not been able to figure out what causes it so if you can guide me, I would really love to see this model accessible for people.\r\n\r\nUnrecognized configuration class for this kind of AutoModel: AutoModelForCausalLM. Model type should be one of CamembertConfig, XLMRobertaConfig, RobertaConfig, BertConfig, OpenAIGPTConfig, GPT2Config, TransfoXLConfig, XLNetConfig, XLMConfig, CTRLConfig, ReformerConfig, BertGenerationConfig, XLMProphetNetConfig, ProphetNetConfig.\r\n\r\n", "Did you change the `pipeline_tag` in the meantime ? It's working now:\r\n\r\nhttps://huggingface.co/Helsinki-NLP/opus-mt-luo-en?text=Ariyo\r\n\r\nThe error seemed to point it wanted to do text-generation with your model which it can't.", "It is still not working @Narsil. I get a different error now, do you know what might be causing it?\r\n\r\n\r\nThe model you referenced is a different one- A Luo -English model- This one is Acholi -English" ]

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[ "I believe @jplu has already used TF Serving. Do you know if it's possible to include tokenization in it?", "Hello!\r\n\r\nUnfortunately it is currently not possible to integrate our tokenizer directly inside a model due to some TensorFlow limitations. Nevertheless, there might be a solution by trying to create your own Tokenization layer such as the one the TF team is [working on](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing/TextVectorization).", "Thanks for response and for the link.\r\n\r\nYa, it's a shame that there is still no way to use plain python in the signature.\r\n\r\nI'll likely just find a different work around e.g. converting to PyTorch and serving with TorchServe. \r\n\r\nI'll close this for now.", "I found a working soltuion that doesn't require any changes to Tensorflow or Transformers.\r\n\r\nCommenting because I came across this trying to do something similar. I actually think the issue here is not tensorflow but the transformer type checking for the tokenizer call which doesn't allow for the tensorflow objects.\r\n\r\nI made the following implementation which appears to be working and doesn't rely on anything due to tensorflow limitations:\r\n\r\n```python\r\n# NOTE: the specific model here will need to be overwritten because AutoModel doesn't work\r\nclass CustomModel(transformers.TFDistilBertForSequenceClassification):\r\n\r\n def call_tokenizer(self, input):\r\n if type(input) == list:\r\n return self.tokenizer([str(x) for x in input], return_tensors='tf')\r\n \r\n else:\r\n return self.tokenizer(str(input), return_tensors='tf')\r\n \r\n\r\n\r\n @tf.function(input_signature=[tf.TensorSpec(shape=(1, ), dtype=tf.string)])\r\n def serving(self, content: str):\r\n batch = self.call_tokenizer(content)\r\n batch = dict(batch)\r\n batch = [batch]\r\n output = self.call(batch)\r\n return self.serving_output(output)\r\n\r\n\r\ntokenizer = transformers.AutoTokenizer.from_pretrained(\r\n model_path,\r\n use_fast=True\r\n)\r\n\r\nconfig = transformers.AutoConfig.from_pretrained(\r\n model_path,\r\n num_labels=2,\r\n from_pt=True\r\n)\r\n\r\nmodel = CustomModel.from_pretrained(\r\n model_path,\r\n config=config,\r\n from_pt=True\r\n)\r\n\r\nmodel.tokenizer = tokenizer\r\nmodel.id2label = config.id2label\r\nmodel.save_pretrained(\"model\", saved_model=True)\r\n```", "Hi @maxzzze \r\n\r\nI was also working on including hf tokenizer into tf model. However, I found that inside call_tokenizer, the results tokenizer return would always be the same despites the text input you passed in. \r\n\r\nHave you also encounter such issue? I am thinking save_pretrained wasn't including the tokenizer appropriately.\r\n\r\n> I found a working soltuion that doesn't require any changes to Tensorflow or Transformers.\r\n> \r\n> Commenting because I came across this trying to do something similar. I actually think the issue here is not tensorflow but the transformer type checking for the tokenizer call which doesn't allow for the tensorflow objects.\r\n> \r\n> I made the following implementation which appears to be working and doesn't rely on anything due to tensorflow limitations:\r\n> \r\n> ```python\r\n> # NOTE: the specific model here will need to be overwritten because AutoModel doesn't work\r\n> class CustomModel(transformers.TFDistilBertForSequenceClassification):\r\n> \r\n> def call_tokenizer(self, input):\r\n> if type(input) == list:\r\n> return self.tokenizer([str(x) for x in input], return_tensors='tf')\r\n> \r\n> else:\r\n> return self.tokenizer(str(input), return_tensors='tf')\r\n> \r\n> \r\n> \r\n> @tf.function(input_signature=[tf.TensorSpec(shape=(1, ), dtype=tf.string)])\r\n> def serving(self, content: str):\r\n> batch = self.call_tokenizer(content)\r\n> batch = dict(batch)\r\n> batch = [batch]\r\n> output = self.call(batch)\r\n> return self.serving_output(output)\r\n> \r\n> \r\n> tokenizer = transformers.AutoTokenizer.from_pretrained(\r\n> model_path,\r\n> use_fast=True\r\n> )\r\n> \r\n> config = transformers.AutoConfig.from_pretrained(\r\n> model_path,\r\n> num_labels=2,\r\n> from_pt=True\r\n> )\r\n> \r\n> model = CustomModel.from_pretrained(\r\n> model_path,\r\n> config=config,\r\n> from_pt=True\r\n> )\r\n> \r\n> model.tokenizer = tokenizer\r\n> model.id2label = config.id2label\r\n> model.save_pretrained(\"model\", saved_model=True)\r\n> ```\r\n\r\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 @Sniper970119 do you mind posting this on the forum rather? It's here: https://discuss.huggingface.co\r\n\r\nWe are trying to focus the issues on bug reports and features/model requests.\r\n\r\nThanks a lot.", "> \r\n> \r\n> Hi @Sniper970119 do you mind posting this on the forum rather? It's here: https://discuss.huggingface.co\r\n> \r\n> We are trying to focus the issues on bug reports and features/model requests.\r\n> \r\n> Thanks a lot.\r\n\r\nok,I just post it on the forum.Thank for your reply." ]

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[ "It doesn't make any change on Linux, and you've tested it on Windows. Could we get someone using MacOS to double-check it doesn't break anything for them before merging?", "I think @LysandreJik is on MacOS?", "I'm actually between Manjaro and EndeavourOS, but I'll check on a Mac." ]

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[ "I have added a lot of comments in the method to make it clearer, I removed a small part of the code that was due to the moment where I was updating to the new names in same time. @LysandreJik @sgugger it should be easier to understand now.", "It's a lot clearer, thanks. There are still unaddressed comments however, and I can't comment on line 259 but it should be removed now (since the dict is create two lines below).", "What is missing now?", "There is Lysandre's comments at line 283 and mine about the loop line 277. Like I said in my previous comments, doing the two functions in one is great, I just don't get the added complexity of the new `model_layers_name_value` variable when we could stick to the previous loop in the function `load_tf_weights` while adding the behavior of `detect_tf_missing_unexpected_layers`.\r\n\r\nThe comments are a great addition, thanks a lot for adding those!", "I have addressed the Lysandre's comment at line 283 and yours for the loop at line 277. Do you see anything else?", "The typos should be fixed now. Sorry for that.", "Good to merge for me too!" ]

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[ "Maybe wrong model config for T5.1.1. For instance, T5.1.1.small should have num_layers=8 and num_heads=6.\r\n\r\nSee https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/models/gin/models/t5.1.1.small.gin", "> Maybe wrong model config for T5.1.1. For instance, T5.1.1.small should have num_layers=8 and num_heads=6.\r\n> \r\n> See https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/models/gin/models/t5.1.1.small.gin\r\n\r\nThanks yeah, I implemented that. \r\n\r\nThe new model structure is now equal to mesh t5 v1.1. \r\n\r\nIf you download the t5v1.1 `t5-small` checkpoint and replace the corresponding path in `check_t5_against_hf.py` you can see that the models are equal. \r\n\r\nThere is still quite some work to do: write more tests, lots of cleaning and better design, and check if mT5 works with it.", "> If you download the t5v1.1 `t5-small` checkpoint and replace the corresponding path in `check_t5_against_hf.py` you can see that the models are equal.\r\n\r\nHi, `check_t5_against_hf.py` still fails if I use a longer input text instead of `Hello there`, like `Hello there. Let's put more words in more languages than I originally thought.`\r\n", "> > If you download the t5v1.1 `t5-small` checkpoint and replace the corresponding path in `check_t5_against_hf.py` you can see that the models are equal.\r\n> \r\n> Hi, `check_t5_against_hf.py` still fails if I use a longer input text instead of `Hello there`, like `Hello there. Let's put more words in more languages than I originally thought.`\r\n\r\nHmm, it works for me - do you experience that for T5v1.1 or mT5?", "> > > If you download the t5v1.1 `t5-small` checkpoint and replace the corresponding path in `check_t5_against_hf.py` you can see that the models are equal.\r\n> > \r\n> > \r\n> > Hi, `check_t5_against_hf.py` still fails if I use a longer input text instead of `Hello there`, like `Hello there. Let's put more words in more languages than I originally thought.`\r\n> \r\n> Hmm, it works for me - do you experience that for T5v1.1 or mT5?\r\n\r\nAha, the checking is OK now. Yesterday I made a mistake that when I changed the test input sentence in the check script, I didn't update the input length for MTF model from 4 to a longer value like 128. So actually the MTF model and PyTorch model received different inputs, and of course got different results.\r\n\r\nBesides, if I add the z-loss to the CE loss at last, it differs from MTF score again. I just found MTF ignores z-loss when not training ([code](https://github.com/tensorflow/mesh/blob/4f82ba1275e4c335348019fee7974d11ac0c9649/mesh_tensorflow/transformer/transformer.py#L781)). So I think MTF model score does not include z-loss, but its training does, which is absent from HF T5 training. Well, this is absolutely not a blocking issue now.\r\n\r\nAppreciate your great work :) ", "closing in favor of https://github.com/huggingface/transformers/pull/8552." ]

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[ "`evaluate` calls `log` which appends the results to `log_history`. So the code is there. Without a reproducer to investigate, there is nothing we can do to help.", "Here is the demo code that shows the bug: https://colab.research.google.com/drive/1dEzkDoMampL-VVrQeO924HQmHXffya0Z?usp=sharing\r\n\r\nThe last line should print all metrics and does that with version 3.4.0 but not with 3.5.0\r\n\r\nOutput of 3.4.0 (which is correct):\r\n```\r\n{'eval_loss': 0.5401068925857544, 'eval_f1_OTHER': 0.8642232403165347, 'eval_f1_OFFENSE': 0.6730190571715146, 'eval_recall_OTHER': 0.9230427046263345, 'eval_recall_OFFENSE': 0.5834782608695652, 'eval_acc': 0.8081224249558564, 'eval_bac': 0.7532604827479499, 'eval_mcc': 0.5547059570919702, 'eval_f1_macro': 0.7686211487440247, 'epoch': 2.0, 'total_flos': 668448673730400, 'step': 628}\r\n```\r\n\r\n\r\nBug in 3.5.0:\r\n```\r\n{'total_flos': 668448673730400, 'epoch': 2.0, 'step': 628}\r\n```", "Ah it's not a bug. In 3.5.0 there is one final log entry for the total_flos (instead of logging them during training as it's only useful at the end). So you can still access all your metrics but with the second-to-last entry (`trainer.state.log_history[-2]`).", "Hi @sgugger ok thanks for the info.\r\nIt might be no bug but honestly. This logging \"API\" is very fragile. What happens if I do a `[-2]` now and in the next release the final log entry for the total_flos is moved to an other list. Then I am getting the result of the 2nd last epoch instead of the last one.\r\n\r\nIMO this logging \"API\" needs a clean and better redesign. Or do I just use it in a wrong way?", "There are plenty of things that could log more info: a callback, some other tweak in training performed at the end. IMO you shouldn't rely on a hard-coded index but loop from the end of the `log_history` until you find a dict with the metric values.", "Ok. Closing this." ]

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[ "Hi @MarktHart do you mind posting this on the forum rather? It's here: https://discuss.huggingface.co\r\n\r\nWe are trying to focus the issues on bug reports and features/model requests.\r\n\r\nThanks a lot.", "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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[ "Please feel free to modify this in any shape or form of course.", "This will cause a regression for code expecting a NumPy output, obviously :/", "I don't have much time, if any, to dedicate to this. If you're not interested by the idea, it's completely fine, I will close.", "I think @jplu is redesigning the TFTrainer. Maybe this should be reopened once that design has been merged in master?", "This won't be compliant anymore because the redisign doesn't use custom loops.", "Do you support different batch lengths in the new one? @jplu ", "It is not on top of the list but, yes for sure, we plan to support it, including for training." ]

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[ "Created pull request https://github.com/huggingface/transformers/pull/8485", "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 @JulesGM \r\nRegarding [#8483](https://github.com/huggingface/transformers/pull/8485)\r\nI went through the code and I found this helpful. But I'm facing issues to convert my training tf.data.Dataset to tf.RaggedTensor format. if possible can you share resources regarding this?" ]

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[ "Thank you! \r\n\r\n❗ This is a preliminary review, I'm not finished with it. 2 important things for now:\r\n\r\n1) I am also testing the Colab demo's with this branch. Currently I'm getting an error when providing `answer_coordinates` and `answer_texts` to the tokenizer: \r\n\r\nSQA: https://colab.research.google.com/drive/1BNxrKkrwpWuE2TthZL5qQlERtcK4ZbIt?usp=sharing\r\nWTQ: https://colab.research.google.com/drive/1K8ZeNQyBqo-A03D8RL8_j34n-Ubggb9U?usp=sharing\r\n\r\nNormally, the `label_ids`, `numeric_values` and `numeric_values_scale` should also be padded when I set padding='max_length'. \r\n\r\n2) I've got an updated version of the creation of the numeric values (they are currently not performed correctly) in a branch named `tapas_v3_up_to_date_with_master`. Either you could incorporate these changes in your branch before making a PR, or I make them after the PR is merged (what you like best - the latter is probably easier). ", "Great, thanks for your great preliminary review. I've fixed a few of the issues, just pushed a commit. There's a few things you mention that definitely need a deeper look. I can do so in the coming days, but I'll let you finish your review first so that I may batch everything. Thank you!", "@LysandreJik I have finished reviewing, I've added more (mostly documentation-related) comments.\r\n\r\nThe most important thing is that when `label_ids`, `answer_coordinates` and `answer_text` are provided to the tokenizer, an error is currently thrown due to the fact that padding is not working. \r\n\r\nBesides this, the other important things are:\r\n* a correct implementation of the creation of the `prev_label_ids` when a batch of table-question pairs is provided\r\n* a correct implementation of `drop_rows_to_fit` and `cell_trim_length`" ]

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[ "Since `git push` by itself is not so informative, I retried it with more verbose output (sorry for the long output): \r\n```\r\n$ GIT_TRACE=1 GIT_CURL_VERBOSE=1 git push\r\n18:07:20.246678 git.c:440 trace: built-in: git push\r\n18:07:20.247908 run-command.c:663 trace: run_command: GIT_DIR=.git git-remote-https origin https://huggingface.co/allenai/unifiedqa-t5-3b\r\n* Couldn't find host huggingface.co in the .netrc file; using defaults\r\n* Trying 192.99.39.165...\r\n* TCP_NODELAY set\r\n* Connected to huggingface.co (192.99.39.165) port 443 (#0)\r\n* ALPN, offering h2\r\n* ALPN, offering http/1.1\r\n* successfully set certificate verify locations:\r\n* CAfile: /etc/ssl/cert.pem\r\n CApath: none\r\n* SSL connection using TLSv1.2 / ECDHE-RSA-AES256-GCM-SHA384\r\n* ALPN, server accepted to use http/1.1\r\n* Server certificate:\r\n* subject: CN=huggingface.co\r\n* start date: Nov 10 08:05:46 2020 GMT\r\n* expire date: Feb 8 08:05:46 2021 GMT\r\n* subjectAltName: host \"huggingface.co\" matched cert's \"huggingface.co\"\r\n* issuer: C=US; O=Let's Encrypt; CN=Let's Encrypt Authority X3\r\n* SSL certificate verify ok.\r\n> GET /allenai/unifiedqa-t5-3b/info/refs?service=git-receive-pack HTTP/1.1\r\nHost: huggingface.co\r\nUser-Agent: git/2.23.0\r\nAccept: */*\r\nAccept-Encoding: deflate, gzip\r\nAccept-Language: en-US, *;q=0.9\r\nPragma: no-cache\r\n\r\n< HTTP/1.1 401 Unauthorized\r\n< Server: nginx/1.14.2\r\n< Date: Thu, 12 Nov 2020 02:07:21 GMT\r\n< Content-Type: text/plain; charset=utf-8\r\n< Content-Length: 12\r\n< Connection: keep-alive\r\n< X-Powered-By: huggingface-moon\r\n< WWW-Authenticate: Basic realm=\"Authentication required\", charset=\"UTF-8\"\r\n< ETag: W/\"c-dAuDFQrdjS3hezqxDTNgW7AOlYk\"\r\n< \r\n* Connection #0 to host huggingface.co left intact\r\n18:07:20.859025 run-command.c:663 trace: run_command: 'git credential-osxkeychain get'\r\n18:07:20.876285 git.c:703 trace: exec: git-credential-osxkeychain get\r\n18:07:20.877309 run-command.c:663 trace: run_command: git-credential-osxkeychain get\r\n* Found bundle for host huggingface.co: 0x7f89a65048d0 [can pipeline]\r\n* Could pipeline, but not asked to!\r\n* Re-using existing connection! (#0) with host huggingface.co\r\n* Connected to huggingface.co (192.99.39.165) port 443 (#0)\r\n* Server auth using Basic with user 'danyaljj'\r\n> GET /allenai/unifiedqa-t5-3b/info/refs?service=git-receive-pack HTTP/1.1\r\nHost: huggingface.co\r\nAuthorization: Basic ZGFueWFsamo6UmVuZGNyYXp5MQ==\r\nUser-Agent: git/2.23.0\r\nAccept: */*\r\nAccept-Encoding: deflate, gzip\r\nAccept-Language: en-US, *;q=0.9\r\nPragma: no-cache\r\n\r\n< HTTP/1.1 200 OK\r\n< Server: nginx/1.14.2\r\n< Date: Thu, 12 Nov 2020 02:07:21 GMT\r\n< Content-Type: application/x-git-receive-pack-advertisement\r\n< Transfer-Encoding: chunked\r\n< Connection: keep-alive\r\n< X-Powered-By: huggingface-moon\r\n< \r\n* Connection #0 to host huggingface.co left intact\r\n18:07:21.159144 run-command.c:663 trace: run_command: 'git credential-osxkeychain store'\r\n18:07:21.175886 git.c:703 trace: exec: git-credential-osxkeychain store\r\n18:07:21.176867 run-command.c:663 trace: run_command: git-credential-osxkeychain store\r\n18:07:21.240597 run-command.c:663 trace: run_command: .git/hooks/pre-push origin https://huggingface.co/allenai/unifiedqa-t5-3b\r\n18:07:21.258423 git.c:703 trace: exec: git-lfs pre-push origin https://huggingface.co/allenai/unifiedqa-t5-3b\r\n18:07:21.259618 run-command.c:663 trace: run_command: git-lfs pre-push origin https://huggingface.co/allenai/unifiedqa-t5-3b\r\n18:07:21.280328 trace git-lfs: exec: git 'version'\r\n18:07:21.305769 trace git-lfs: exec: git '-c' 'filter.lfs.smudge=' '-c' 'filter.lfs.clean=' '-c' 'filter.lfs.process=' '-c' 'filter.lfs.required=false' 'rev-parse' 'HEAD' '--symbolic-full-name' 'HEAD'\r\n18:07:21.330148 trace git-lfs: exec: git 'config' '-l'\r\n18:07:21.341551 trace git-lfs: pre-push: refs/heads/main 820bb7e936e2e5665ea9c4ac3016456b3ce55bc7 refs/heads/main 4d2dae1e804fc041975dc40c06e3ab902b6c3f38\r\n18:07:21.829857 trace git-lfs: tq: running as batched queue, batch size of 100\r\n18:07:21.830328 trace git-lfs: run_command: git rev-list --stdin --objects --not --remotes=origin --\r\n18:07:21.848139 trace git-lfs: tq: sending batch of size 1 \r\n18:07:21.848726 trace git-lfs: api: batch 1 files\r\n18:07:21.848996 trace git-lfs: creds: git credential fill (\"https\", \"huggingface.co\", \"\")\r\n18:07:21.859568 git.c:440 trace: built-in: git credential fill\r\n18:07:21.861149 run-command.c:663 trace: run_command: 'git credential-osxkeychain get'\r\n18:07:21.877936 git.c:703 trace: exec: git-credential-osxkeychain get\r\n18:07:21.879004 run-command.c:663 trace: run_command: git-credential-osxkeychain get\r\n18:07:21.920056 trace git-lfs: Filled credentials for https://huggingface.co/allenai/unifiedqa-t5-3b\r\n18:07:21.989068 trace git-lfs: HTTP: POST https://huggingface.co/allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch\r\n> POST /allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch HTTP/1.1\r\n> Host: huggingface.co\r\n> Accept: application/vnd.git-lfs+json; charset=utf-8\r\n> Authorization: Basic * * * * *\r\n> Content-Length: 205\r\n> Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n{\"operation\":\"upload\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119}],\"transfers\":[\"lfs-standalone-file\",\"basic\"],\"ref\":{\"name\":\"refs/heads/main\"}}18:07:23.102074 trace git-lfs: HTTP: 200\r\n\r\n\r\n< HTTP/1.1 200 OK\r\n< Content-Length: 578\r\n< Connection: keep-alive\r\n< Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n< Date: Thu, 12 Nov 2020 02:07:23 GMT\r\n< Etag: W/\"242-LFg/omWZFm9SxeMWd5EiIfG1JTM\"\r\n< Server: nginx/1.14.2\r\n< X-Powered-By: huggingface-moon\r\n< \r\n18:07:23.102239 trace git-lfs: creds: git credential approve (\"https\", \"huggingface.co\", \"\")\r\n18:07:23.112995 git.c:440 trace: built-in: git credential approve\r\n18:07:23.114213 run-command.c:663 trace: run_command: 'git credential-osxkeychain store'\r\n18:07:23.129607 git.c:703 trace: exec: git-credential-osxkeychain store\r\n18:07:23.130582 run-command.c:663 trace: run_command: git-credential-osxkeychain store\r\n18:07:23.195094 trace git-lfs: HTTP: {\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020723Z&X-Amz-Expires=900&X-Amz-Signature=2d3c1762e44b21f78c89a7c5a5f41\r\n{\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020723Z&X-Amz-Expires=900&X-Amz-Signature=2d3c1762e44b21f78c89a7c5a5f4118:07:23.195387 trace git-lfs: HTTP: 655333c901abeb466effd6f1d61bd110f6a&X-Amz-SignedHeaders=host\"}}}]}\r\n655333c901abeb466effd6f1d61bd110f6a&X-Amz-SignedHeaders=host\"}}}]}Uploading LFS objects: 0% (0/1), 0 B | 0 B/s 18:07:23.195588 trace git-lfs: tq: starting transfer adapter \"basic\"\r\n18:07:23.195998 trace git-lfs: xfer: adapter \"basic\" Begin() with 8 workers\r\n18:07:23.196062 trace git-lfs: xfer: adapter \"basic\" started\r\n18:07:23.196099 trace git-lfs: xfer: adapter \"basic\" worker 2 starting\r\n18:07:23.196118 trace git-lfs: xfer: adapter \"basic\" worker 0 starting\r\n18:07:23.196169 trace git-lfs: xfer: adapter \"basic\" worker 2 waiting for Auth\r\n18:07:23.196185 trace git-lfs: xfer: adapter \"basic\" worker 1 starting\r\n18:07:23.196151 trace git-lfs: xfer: adapter \"basic\" worker 4 starting\r\n18:07:23.196216 trace git-lfs: xfer: adapter \"basic\" worker 5 starting\r\n18:07:23.196257 trace git-lfs: xfer: adapter \"basic\" worker 5 waiting for Auth\r\n18:07:23.196248 trace git-lfs: xfer: adapter \"basic\" worker 4 waiting for Auth\r\n18:07:23.196288 trace git-lfs: xfer: adapter \"basic\" worker 0 processing job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:07:23.196293 trace git-lfs: xfer: adapter \"basic\" worker 1 waiting for Auth\r\n18:07:23.196255 trace git-lfs: xfer: adapter \"basic\" worker 3 starting\r\n18:07:23.196290 trace git-lfs: xfer: adapter \"basic\" worker 6 starting\r\n18:07:23.196423 trace git-lfs: xfer: adapter \"basic\" worker 6 waiting for Auth\r\n18:07:23.196380 trace git-lfs: xfer: adapter \"basic\" worker 7 starting\r\n18:07:23.196458 trace git-lfs: xfer: adapter \"basic\" worker 7 waiting for Auth\r\n18:07:23.196420 trace git-lfs: xfer: adapter \"basic\" worker 3 waiting for Auth\r\n18:07:23.261193 trace git-lfs: HTTP: PUT https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\r\n> PUT /lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020723Z&X-Amz-Expires=900&X-Amz-Signature=2d3c1762e44b21f78c89a7c5a5f41655333c901abeb466effd6f1d61bd110f6a&X-Amz-SignedHeaders=host HTTP/1.1\r\n> Host: s3.amazonaws.com\r\n> Content-Length: 11406640119\r\n> Content-Type: application/zip\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n18:07:23.499247 trace git-lfs: xfer: adapter \"basic\" worker 4 auth signal received\r\n18:07:23.499298 trace git-lfs: xfer: adapter \"basic\" worker 5 auth signal received\r\n18:07:23.499281 trace git-lfs: xfer: adapter \"basic\" worker 2 auth signal received\r\n18:07:23.499315 trace git-lfs: xfer: adapter \"basic\" worker 6 auth signal received\r\n18:07:23.499324 trace git-lfs: xfer: adapter \"basic\" worker 7 auth signal received\r\n18:07:23.499353 trace git-lfs: xfer: adapter \"basic\" worker 1 auth signal received\r\n18:07:23.499412 trace git-lfs: xfer: adapter \"basic\" worker 3 auth signal received\r\n18:07:34.596626 trace git-lfs: xfer: adapter \"basic\" worker 0 finished job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\" \r\n18:07:34.596706 trace git-lfs: tq: retrying object 7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386: LFS: Put https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020723Z&X-Amz-Expires=900&X-Amz-Signature=2d3c1762e44b21f78c89a7c5a5f41655333c901abeb466effd6f1d61bd110f6a&X-Amz-SignedHeaders=host: write tcp 192.168.0.6:57346->52.216.242.102:443: write: broken pipe\r\n18:07:34.596761 trace git-lfs: tq: enqueue retry #1 for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\" (size: 11406640119)\r\n18:07:34.596823 trace git-lfs: tq: sending batch of size 1\r\n18:07:34.596995 trace git-lfs: api: batch 1 files\r\n18:07:34.597180 trace git-lfs: creds: git credential cache (\"https\", \"huggingface.co\", \"\")\r\n18:07:34.597193 trace git-lfs: Filled credentials for https://huggingface.co/allenai/unifiedqa-t5-3b\r\n18:07:34.597208 trace git-lfs: HTTP: POST https://huggingface.co/allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch\r\n> POST /allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch HTTP/1.1\r\n> Host: huggingface.co\r\n> Accept: application/vnd.git-lfs+json; charset=utf-8\r\n> Authorization: Basic * * * * *\r\n> Content-Length: 205\r\n> Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n{\"operation\":\"upload\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119}],\"transfers\":[\"lfs-standalone-file\",\"basic\"],\"ref\":{\"name\":\"refs/heads/main\"}}18:07:34.925848 trace git-lfs: HTTP: 200\r\n\r\n\r\n< HTTP/1.1 200 OK\r\n< Content-Length: 578\r\n< Connection: keep-alive\r\n< Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n< Date: Thu, 12 Nov 2020 02:07:35 GMT\r\n< Etag: W/\"242-5zNHypYie/0vI3rttL7+btltlmQ\"\r\n< Server: nginx/1.14.2\r\n< X-Powered-By: huggingface-moon\r\n< \r\n18:07:34.926039 trace git-lfs: HTTP: {\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020735Z&X-Amz-Expires=900&X-Amz-Signature=602be765be6206f6363a93f156b88\r\n{\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020735Z&X-Amz-Expires=900&X-Amz-Signature=602be765be6206f6363a93f156b8818:07:34.926220 trace git-lfs: HTTP: 37884d95b5a8f27d14e254dd7108b845cb7&X-Amz-SignedHeaders=host\"}}}]}\r\n37884d95b5a8f27d14e254dd7108b845cb7&X-Amz-SignedHeaders=host\"}}}]}Uploading LFS objects: 0% (0/1), 9.7 MB | 742 KB/s 18:07:34.926411 trace git-lfs: xfer: adapter \"basic\" worker 4 processing job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:07:34.926793 trace git-lfs: HTTP: PUT https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\r\n> PUT /lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020735Z&X-Amz-Expires=900&X-Amz-Signature=602be765be6206f6363a93f156b8837884d95b5a8f27d14e254dd7108b845cb7&X-Amz-SignedHeaders=host HTTP/1.1\r\n> Host: s3.amazonaws.com\r\n> Content-Length: 11406640119\r\n> Content-Type: application/zip\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n18:07:44.835908 trace git-lfs: HTTP: 400 | 752 KB/s \r\n\r\n\r\n< HTTP/1.1 400 Bad Request\r\n< Connection: close\r\n< Transfer-Encoding: chunked\r\n< Content-Type: application/xml\r\n< Date: Thu, 12 Nov 2020 02:07:44 GMT\r\n< Server: AmazonS3\r\n< X-Amz-Id-2: mDWWLDn2SM2srJXwqsVkEIAue+9F8wnupyuGkTAD4lcLKmDSSBa75zgKY7NXUC0X7QEMVwmPSVk=\r\n< X-Amz-Request-Id: B971E21D6254F404\r\n< \r\n18:07:44.836114 trace git-lfs: xfer: adapter \"basic\" worker 4 finished job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\" \r\n18:07:44.836157 trace git-lfs: tq: retrying object 7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386: LFS: Client error: https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020735Z&X-Amz-Expires=900&X-Amz-Signature=602be765be6206f6363a93f156b8837884d95b5a8f27d14e254dd7108b845cb7&X-Amz-SignedHeaders=host\r\n18:07:44.836199 trace git-lfs: tq: enqueue retry #2 for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\" (size: 11406640119)\r\n18:07:44.836238 trace git-lfs: tq: sending batch of size 1\r\n18:07:44.836355 trace git-lfs: api: batch 1 files\r\n18:07:44.836546 trace git-lfs: creds: git credential cache (\"https\", \"huggingface.co\", \"\")\r\n18:07:44.836556 trace git-lfs: Filled credentials for https://huggingface.co/allenai/unifiedqa-t5-3b\r\n18:07:44.836585 trace git-lfs: HTTP: POST https://huggingface.co/allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch\r\n> POST /allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch HTTP/1.1\r\n> Host: huggingface.co\r\n> Accept: application/vnd.git-lfs+json; charset=utf-8\r\n> Authorization: Basic * * * * *\r\n> Content-Length: 205\r\n> Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n{\"operation\":\"upload\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119}],\"transfers\":[\"lfs-standalone-file\",\"basic\"],\"ref\":{\"name\":\"refs/heads/main\"}}18:07:45.158001 trace git-lfs: HTTP: 200\r\n\r\n\r\n< HTTP/1.1 200 OK\r\n< Content-Length: 578\r\n< Connection: keep-alive\r\n< Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n< Date: Thu, 12 Nov 2020 02:07:45 GMT\r\n< Etag: W/\"242-m4CvhzTDqQPlc75+BedrFERvkE0\"\r\n< Server: nginx/1.14.2\r\n< X-Powered-By: huggingface-moon\r\n< \r\n18:07:45.158145 trace git-lfs: HTTP: {\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020745Z&X-Amz-Expires=900&X-Amz-Signature=82592319178cff3f2a02f404e3540\r\n{\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020745Z&X-Amz-Expires=900&X-Amz-Signature=82592319178cff3f2a02f404e354018:07:45.158254 trace git-lfs: HTTP: 89a6ef22b79fe75e6f544e1786faf3e8f5e&X-Amz-SignedHeaders=host\"}}}]}\r\n89a6ef22b79fe75e6f544e1786faf3e8f5e&X-Amz-SignedHeaders=host\"}}}]}Uploading LFS objects: 0% (0/1), 9.7 MB | 752 KB/s 18:07:45.158419 trace git-lfs: xfer: adapter \"basic\" worker 5 processing job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:07:45.158794 trace git-lfs: HTTP: PUT https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\r\n> PUT /lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020745Z&X-Amz-Expires=900&X-Amz-Signature=82592319178cff3f2a02f404e354089a6ef22b79fe75e6f544e1786faf3e8f5e&X-Amz-SignedHeaders=host HTTP/1.1\r\n> Host: s3.amazonaws.com\r\n> Content-Length: 11406640119\r\n> Content-Type: application/zip\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n18:07:55.959066 trace git-lfs: HTTP: 400 | 665 KB/s \r\n\r\n\r\n< HTTP/1.1 400 Bad Request\r\n< Connection: close\r\n< Transfer-Encoding: chunked\r\n< Content-Type: application/xml\r\n< Date: Thu, 12 Nov 2020 02:07:54 GMT\r\n< Server: AmazonS3\r\n< X-Amz-Id-2: YPk1ZSL19/lW1Z7WxE/pTAyDK0Ny2ryDVCi1TZXtuT8Bh6itRmL4qO163dKG+s9yBSl8jyKRD7Y=\r\n< X-Amz-Request-Id: D0E50DEEB73DFA43\r\n< \r\n18:07:55.959368 trace git-lfs: xfer: adapter \"basic\" worker 5 finished job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:07:55.959409 trace git-lfs: tq: retrying object 7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386: LFS: Client error: https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020745Z&X-Amz-Expires=900&X-Amz-Signature=82592319178cff3f2a02f404e354089a6ef22b79fe75e6f544e1786faf3e8f5e&X-Amz-SignedHeaders=host\r\n18:07:55.959458 trace git-lfs: tq: enqueue retry #3 for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\" (size: 11406640119)\r\n18:07:55.959490 trace git-lfs: tq: sending batch of size 1\r\n18:07:55.959582 trace git-lfs: api: batch 1 files\r\n18:07:55.959750 trace git-lfs: creds: git credential cache (\"https\", \"huggingface.co\", \"\")\r\n18:07:55.959768 trace git-lfs: Filled credentials for https://huggingface.co/allenai/unifiedqa-t5-3b\r\n18:07:55.959786 trace git-lfs: HTTP: POST https://huggingface.co/allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch\r\n> POST /allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch HTTP/1.1\r\n> Host: huggingface.co\r\n> Accept: application/vnd.git-lfs+json; charset=utf-8\r\n> Authorization: Basic * * * * *\r\n> Content-Length: 205\r\n> Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n{\"operation\":\"upload\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119}],\"transfers\":[\"basic\",\"lfs-standalone-file\"],\"ref\":{\"name\":\"refs/heads/main\"}}18:07:56.260024 trace git-lfs: HTTP: 200\r\n\r\n\r\n< HTTP/1.1 200 OK\r\n< Content-Length: 578\r\n< Connection: keep-alive\r\n< Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n< Date: Thu, 12 Nov 2020 02:07:56 GMT\r\n< Etag: W/\"242-31cowPk91NvaIaX84tjI/gLbdvo\"\r\n< Server: nginx/1.14.2\r\n< X-Powered-By: huggingface-moon\r\n< \r\n18:07:56.260224 trace git-lfs: HTTP: {\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020756Z&X-Amz-Expires=900&X-Amz-Signature=1c194a7990031e65288f0e7c59507\r\n{\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020756Z&X-Amz-Expires=900&X-Amz-Signature=1c194a7990031e65288f0e7c5950718:07:56.260428 trace git-lfs: HTTP: 8387ee098bd5b3f3708e02085e3e9f6601a&X-Amz-SignedHeaders=host\"}}}]}\r\n8387ee098bd5b3f3708e02085e3e9f6601a&X-Amz-SignedHeaders=host\"}}}]}Uploading LFS objects: 0% (0/1), 9.7 MB | 665 KB/s 18:07:56.260674 trace git-lfs: xfer: adapter \"basic\" worker 2 processing job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:07:56.261037 trace git-lfs: HTTP: PUT https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\r\n> PUT /lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020756Z&X-Amz-Expires=900&X-Amz-Signature=1c194a7990031e65288f0e7c595078387ee098bd5b3f3708e02085e3e9f6601a&X-Amz-SignedHeaders=host HTTP/1.1\r\n> Host: s3.amazonaws.com\r\n> Content-Length: 11406640119\r\n> Content-Type: application/zip\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n18:08:07.099441 trace git-lfs: HTTP: 400 | 567 KB/s \r\n\r\n\r\n< HTTP/1.1 400 Bad Request\r\n< Connection: close\r\n< Transfer-Encoding: chunked\r\n< Content-Type: application/xml\r\n< Date: Thu, 12 Nov 2020 02:08:06 GMT\r\n< Server: AmazonS3\r\n< X-Amz-Id-2: e9blPqVAV5CVfFOylV29AzDODso+WNBEVIhJKKQc6NbEAMDeUCyJ5NKumhuM5P3i67O58fmm31g=\r\n< X-Amz-Request-Id: DFED315EE7523BFE\r\n< \r\n18:08:07.099632 trace git-lfs: xfer: adapter \"basic\" worker 2 finished job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:08:07.099659 trace git-lfs: tq: retrying object 7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386: LFS: Client error: https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020756Z&X-Amz-Expires=900&X-Amz-Signature=1c194a7990031e65288f0e7c595078387ee098bd5b3f3708e02085e3e9f6601a&X-Amz-SignedHeaders=host\r\n18:08:07.099701 trace git-lfs: tq: enqueue retry #4 for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\" (size: 11406640119)\r\n18:08:07.099742 trace git-lfs: tq: sending batch of size 1\r\n18:08:07.099832 trace git-lfs: api: batch 1 files\r\n18:08:07.099999 trace git-lfs: creds: git credential cache (\"https\", \"huggingface.co\", \"\")\r\n18:08:07.100008 trace git-lfs: Filled credentials for https://huggingface.co/allenai/unifiedqa-t5-3b\r\n18:08:07.100024 trace git-lfs: HTTP: POST https://huggingface.co/allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch\r\n> POST /allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch HTTP/1.1\r\n> Host: huggingface.co\r\n> Accept: application/vnd.git-lfs+json; charset=utf-8\r\n> Authorization: Basic * * * * *\r\n> Content-Length: 205\r\n> Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n{\"operation\":\"upload\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119}],\"transfers\":[\"lfs-standalone-file\",\"basic\"],\"ref\":{\"name\":\"refs/heads/main\"}}18:08:07.441913 trace git-lfs: HTTP: 200\r\n\r\n\r\n< HTTP/1.1 200 OK\r\n< Content-Length: 578\r\n< Connection: keep-alive\r\n< Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n< Date: Thu, 12 Nov 2020 02:08:07 GMT\r\n< Etag: W/\"242-aR0wlUnNkp2RbtWgiEkJ7LUjpW0\"\r\n< Server: nginx/1.14.2\r\n< X-Powered-By: huggingface-moon\r\n< \r\n18:08:07.442095 trace git-lfs: HTTP: {\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020807Z&X-Amz-Expires=900&X-Amz-Signature=126779f211c325c10aba6be7bfc4b\r\n{\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020807Z&X-Amz-Expires=900&X-Amz-Signature=126779f211c325c10aba6be7bfc4b18:08:07.442300 trace git-lfs: HTTP: 32fc8b2d03b037c48b35a864f81d0c3f11f&X-Amz-SignedHeaders=host\"}}}]}\r\n32fc8b2d03b037c48b35a864f81d0c3f11f&X-Amz-SignedHeaders=host\"}}}]}Uploading LFS objects: 0% (0/1), 9.7 MB | 673 KB/s 18:08:07.442493 trace git-lfs: xfer: adapter \"basic\" worker 6 processing job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:08:07.442893 trace git-lfs: HTTP: PUT https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\r\n> PUT /lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020807Z&X-Amz-Expires=900&X-Amz-Signature=126779f211c325c10aba6be7bfc4b32fc8b2d03b037c48b35a864f81d0c3f11f&X-Amz-SignedHeaders=host HTTP/1.1\r\n> Host: s3.amazonaws.com\r\n> Content-Length: 11406640119\r\n> Content-Type: application/zip\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n18:08:18.357156 trace git-lfs: HTTP: 400 | 549 KB/s \r\n\r\n\r\n< HTTP/1.1 400 Bad Request\r\n< Connection: close\r\n< Transfer-Encoding: chunked\r\n< Content-Type: application/xml\r\n< Date: Thu, 12 Nov 2020 02:08:17 GMT\r\n< Server: AmazonS3\r\n< X-Amz-Id-2: 18NzY2b209RdCK3nCS9J1AwpWxSPw7jRub8DLEosfO4JcG33iZ00V59ZRf/CwwCpEFS/G7xHPsI=\r\n< X-Amz-Request-Id: 8M6V1WEG2N8R8YAT\r\n< \r\n18:08:18.357367 trace git-lfs: xfer: adapter \"basic\" worker 6 finished job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:08:18.357394 trace git-lfs: tq: retrying object 7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386: LFS: Client error: https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020807Z&X-Amz-Expires=900&X-Amz-Signature=126779f211c325c10aba6be7bfc4b32fc8b2d03b037c48b35a864f81d0c3f11f&X-Amz-SignedHeaders=host\r\n18:08:18.357453 trace git-lfs: tq: enqueue retry #5 for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\" (size: 11406640119)\r\n18:08:18.357489 trace git-lfs: tq: sending batch of size 1\r\n18:08:18.357602 trace git-lfs: api: batch 1 files\r\n18:08:18.357764 trace git-lfs: creds: git credential cache (\"https\", \"huggingface.co\", \"\")\r\n18:08:18.357773 trace git-lfs: Filled credentials for https://huggingface.co/allenai/unifiedqa-t5-3b\r\n18:08:18.357788 trace git-lfs: HTTP: POST https://huggingface.co/allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch\r\n> POST /allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch HTTP/1.1\r\n> Host: huggingface.co\r\n> Accept: application/vnd.git-lfs+json; charset=utf-8\r\n> Authorization: Basic * * * * *\r\n> Content-Length: 205\r\n> Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n{\"operation\":\"upload\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119}],\"transfers\":[\"basic\",\"lfs-standalone-file\"],\"ref\":{\"name\":\"refs/heads/main\"}}18:08:18.659856 trace git-lfs: HTTP: 200\r\n\r\n\r\n< HTTP/1.1 200 OK\r\n< Content-Length: 578\r\n< Connection: keep-alive\r\n< Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n< Date: Thu, 12 Nov 2020 02:08:18 GMT\r\n< Etag: W/\"242-wt34qjjMKH3OaOLKkwsE5YY47Uo\"\r\n< Server: nginx/1.14.2\r\n< X-Powered-By: huggingface-moon\r\n< \r\n18:08:18.659952 trace git-lfs: HTTP: {\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020818Z&X-Amz-Expires=900&X-Amz-Signature=4f4be5d714bb7cb2270c3c9934412\r\n{\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020818Z&X-Amz-Expires=900&X-Amz-Signature=4f4be5d714bb7cb2270c3c993441218:08:18.660061 trace git-lfs: HTTP: cf1f62357f5a3fcde898603508194a423f1&X-Amz-SignedHeaders=host\"}}}]}\r\ncf1f62357f5a3fcde898603508194a423f1&X-Amz-SignedHeaders=host\"}}}]}Uploading LFS objects: 0% (0/1), 9.7 MB | 549 KB/s 18:08:18.660225 trace git-lfs: xfer: adapter \"basic\" worker 7 processing job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:08:18.660511 trace git-lfs: HTTP: PUT https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\r\n> PUT /lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020818Z&X-Amz-Expires=900&X-Amz-Signature=4f4be5d714bb7cb2270c3c9934412cf1f62357f5a3fcde898603508194a423f1&X-Amz-SignedHeaders=host HTTP/1.1\r\n> Host: s3.amazonaws.com\r\n> Content-Length: 11406640119\r\n> Content-Type: application/zip\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n18:08:31.284958 trace git-lfs: HTTP: 400 | 415 KB/s \r\n\r\n\r\n< HTTP/1.1 400 Bad Request\r\n< Connection: close\r\n< Transfer-Encoding: chunked\r\n< Content-Type: application/xml\r\n< Date: Thu, 12 Nov 2020 02:08:30 GMT\r\n< Server: AmazonS3\r\n< X-Amz-Id-2: EilC4w16RhqwexN8CgO2pXC5Vf5T7PUWS5lsntHalCkp603MmhbpjBtHiITw8NIYifaMK5cuY6U=\r\n< X-Amz-Request-Id: A0585EE068BDEB73\r\n< \r\n18:08:31.285190 trace git-lfs: xfer: adapter \"basic\" worker 7 finished job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:08:31.285198 trace git-lfs: tq: retrying object 7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386: LFS: Client error: https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020818Z&X-Amz-Expires=900&X-Amz-Signature=4f4be5d714bb7cb2270c3c9934412cf1f62357f5a3fcde898603508194a423f1&X-Amz-SignedHeaders=host\r\n18:08:31.285250 trace git-lfs: tq: enqueue retry #6 for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\" (size: 11406640119)\r\n18:08:31.285284 trace git-lfs: tq: sending batch of size 1\r\n18:08:31.285391 trace git-lfs: api: batch 1 files\r\n18:08:31.285539 trace git-lfs: creds: git credential cache (\"https\", \"huggingface.co\", \"\")\r\n18:08:31.285549 trace git-lfs: Filled credentials for https://huggingface.co/allenai/unifiedqa-t5-3b\r\n18:08:31.285566 trace git-lfs: HTTP: POST https://huggingface.co/allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch\r\n> POST /allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch HTTP/1.1\r\n> Host: huggingface.co\r\n> Accept: application/vnd.git-lfs+json; charset=utf-8\r\n> Authorization: Basic * * * * *\r\n> Content-Length: 205\r\n> Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n{\"operation\":\"upload\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119}],\"transfers\":[\"lfs-standalone-file\",\"basic\"],\"ref\":{\"name\":\"refs/heads/main\"}}18:08:31.638814 trace git-lfs: HTTP: 200\r\n\r\n\r\n< HTTP/1.1 200 OK\r\n< Content-Length: 578\r\n< Connection: keep-alive\r\n< Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n< Date: Thu, 12 Nov 2020 02:08:31 GMT\r\n< Etag: W/\"242-7CB890z2UIC8LfHHmFvE0XNO8co\"\r\n< Server: nginx/1.14.2\r\n< X-Powered-By: huggingface-moon\r\n< \r\n18:08:31.639032 trace git-lfs: HTTP: {\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020831Z&X-Amz-Expires=900&X-Amz-Signature=577f945c7c793130dc45581c51367\r\n{\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020831Z&X-Amz-Expires=900&X-Amz-Signature=577f945c7c793130dc45581c5136718:08:31.639183 trace git-lfs: HTTP: 2755ab7636ad209ef6755d542a332673930&X-Amz-SignedHeaders=host\"}}}]}\r\n2755ab7636ad209ef6755d542a332673930&X-Amz-SignedHeaders=host\"}}}]}Uploading LFS objects: 0% (0/1), 9.7 MB | 415 KB/s 18:08:31.639442 trace git-lfs: xfer: adapter \"basic\" worker 1 processing job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:08:31.639795 trace git-lfs: HTTP: PUT https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\r\n> PUT /lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020831Z&X-Amz-Expires=900&X-Amz-Signature=577f945c7c793130dc45581c513672755ab7636ad209ef6755d542a332673930&X-Amz-SignedHeaders=host HTTP/1.1\r\n> Host: s3.amazonaws.com\r\n> Content-Length: 11406640119\r\n> Content-Type: application/zip\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n18:08:35.670792 trace git-lfs: HTTP: 400 | 442 KB/s \r\n\r\n\r\n< HTTP/1.1 400 Bad Request\r\n< Connection: close\r\n< Transfer-Encoding: chunked\r\n< Content-Type: application/xml\r\n< Date: Thu, 12 Nov 2020 02:08:34 GMT\r\n< Server: AmazonS3\r\n< X-Amz-Id-2: 37wPu8zcJ6igY2DAtJ27Oaf5vcLhzCJStEw6bBpHK4QIwUFxcriAuVDuPgfYsUp5mOIqpGXYd5g=\r\n< X-Amz-Request-Id: 4040905E813EF937\r\n< \r\n18:08:35.670992 trace git-lfs: xfer: adapter \"basic\" worker 1 finished job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:08:35.671009 trace git-lfs: tq: retrying object 7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386: LFS: Client error: https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020831Z&X-Amz-Expires=900&X-Amz-Signature=577f945c7c793130dc45581c513672755ab7636ad209ef6755d542a332673930&X-Amz-SignedHeaders=host\r\n18:08:35.671057 trace git-lfs: tq: enqueue retry #7 for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\" (size: 11406640119)\r\n18:08:35.671169 trace git-lfs: tq: sending batch of size 1\r\n18:08:35.671270 trace git-lfs: api: batch 1 files\r\n18:08:35.671422 trace git-lfs: creds: git credential cache (\"https\", \"huggingface.co\", \"\")\r\n18:08:35.671434 trace git-lfs: Filled credentials for https://huggingface.co/allenai/unifiedqa-t5-3b\r\n18:08:35.671449 trace git-lfs: HTTP: POST https://huggingface.co/allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch\r\n> POST /allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch HTTP/1.1\r\n> Host: huggingface.co\r\n> Accept: application/vnd.git-lfs+json; charset=utf-8\r\n> Authorization: Basic * * * * *\r\n> Content-Length: 205\r\n> Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n{\"operation\":\"upload\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119}],\"transfers\":[\"lfs-standalone-file\",\"basic\"],\"ref\":{\"name\":\"refs/heads/main\"}}18:08:35.978219 trace git-lfs: HTTP: 200\r\n\r\n\r\n< HTTP/1.1 200 OK\r\n< Content-Length: 578\r\n< Connection: keep-alive\r\n< Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n< Date: Thu, 12 Nov 2020 02:08:36 GMT\r\n< Etag: W/\"242-aBj4kp6nW/vZfASETDB6DUEmP80\"\r\n< Server: nginx/1.14.2\r\n< X-Powered-By: huggingface-moon\r\n< \r\n18:08:35.978365 trace git-lfs: HTTP: {\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020836Z&X-Amz-Expires=900&X-Amz-Signature=25e0ceea23657b4711a66397bf0e4\r\n{\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020836Z&X-Amz-Expires=900&X-Amz-Signature=25e0ceea23657b4711a66397bf0e418:08:35.978471 trace git-lfs: HTTP: 274d53934c669794d8f31e5be4472d27493&X-Amz-SignedHeaders=host\"}}}]}\r\n274d53934c669794d8f31e5be4472d27493&X-Amz-SignedHeaders=host\"}}}]}Uploading LFS objects: 0% (0/1), 9.7 MB | 442 KB/s 18:08:35.978651 trace git-lfs: xfer: adapter \"basic\" worker 3 processing job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:08:35.978961 trace git-lfs: HTTP: PUT https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\r\n> PUT /lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020836Z&X-Amz-Expires=900&X-Amz-Signature=25e0ceea23657b4711a66397bf0e4274d53934c669794d8f31e5be4472d27493&X-Amz-SignedHeaders=host HTTP/1.1\r\n> Host: s3.amazonaws.com\r\n> Content-Length: 11406640119\r\n> Content-Type: application/zip\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n18:08:47.217142 trace git-lfs: HTTP: 400 | 382 KB/s \r\n\r\n\r\n< HTTP/1.1 400 Bad Request\r\n< Connection: close\r\n< Transfer-Encoding: chunked\r\n< Content-Type: application/xml\r\n< Date: Thu, 12 Nov 2020 02:08:46 GMT\r\n< Server: AmazonS3\r\n< X-Amz-Id-2: 5AEyU9ANTZA6eG2d4Y1XW5KAQ5XX9TsO5IKpThwbwvYh2x2neejx+SxYlt7ysbZ5ZZKRtOQhp0k=\r\n< X-Amz-Request-Id: CF55ABCF55095CE9\r\n< \r\n18:08:47.217330 trace git-lfs: xfer: adapter \"basic\" worker 3 finished job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:08:47.217349 trace git-lfs: tq: retrying object 7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386: LFS: Client error: https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020836Z&X-Amz-Expires=900&X-Amz-Signature=25e0ceea23657b4711a66397bf0e4274d53934c669794d8f31e5be4472d27493&X-Amz-SignedHeaders=host\r\n18:08:47.217399 trace git-lfs: tq: enqueue retry #8 for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\" (size: 11406640119)\r\n18:08:47.217432 trace git-lfs: tq: sending batch of size 1\r\n18:08:47.217524 trace git-lfs: api: batch 1 files\r\n18:08:47.217666 trace git-lfs: creds: git credential cache (\"https\", \"huggingface.co\", \"\")\r\n18:08:47.217675 trace git-lfs: Filled credentials for https://huggingface.co/allenai/unifiedqa-t5-3b\r\n18:08:47.217689 trace git-lfs: HTTP: POST https://huggingface.co/allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch\r\n> POST /allenai/unifiedqa-t5-3b.git/info/lfs/objects/batch HTTP/1.1\r\n> Host: huggingface.co\r\n> Accept: application/vnd.git-lfs+json; charset=utf-8\r\n> Authorization: Basic * * * * *\r\n> Content-Length: 205\r\n> Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n{\"operation\":\"upload\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119}],\"transfers\":[\"lfs-standalone-file\",\"basic\"],\"ref\":{\"name\":\"refs/heads/main\"}}18:08:47.576518 trace git-lfs: HTTP: 200\r\n\r\n\r\n< HTTP/1.1 200 OK\r\n< Content-Length: 578\r\n< Connection: keep-alive\r\n< Content-Type: application/vnd.git-lfs+json; charset=utf-8\r\n< Date: Thu, 12 Nov 2020 02:08:47 GMT\r\n< Etag: W/\"242-I6sTx/9B2Dp11gS7wtbjrP1c3lQ\"\r\n< Server: nginx/1.14.2\r\n< X-Powered-By: huggingface-moon\r\n< \r\n18:08:47.576645 trace git-lfs: HTTP: {\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020847Z&X-Amz-Expires=900&X-Amz-Signature=81eab82a86449f39b5dc223fbdf2b\r\n{\"transfer\":\"basic\",\"objects\":[{\"oid\":\"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\",\"size\":11406640119,\"authenticated\":true,\"actions\":{\"upload\":{\"href\":\"https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020847Z&X-Amz-Expires=900&X-Amz-Signature=81eab82a86449f39b5dc223fbdf2b18:08:47.576740 trace git-lfs: HTTP: d23ecd4834899033d6a896742ea480a1985&X-Amz-SignedHeaders=host\"}}}]}\r\nd23ecd4834899033d6a896742ea480a1985&X-Amz-SignedHeaders=host\"}}}]}Uploading LFS objects: 0% (0/1), 9.7 MB | 382 KB/s 18:08:47.576910 trace git-lfs: xfer: adapter \"basic\" worker 0 processing job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:08:47.577223 trace git-lfs: HTTP: PUT https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\r\n> PUT /lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020847Z&X-Amz-Expires=900&X-Amz-Signature=81eab82a86449f39b5dc223fbdf2bd23ecd4834899033d6a896742ea480a1985&X-Amz-SignedHeaders=host HTTP/1.1\r\n> Host: s3.amazonaws.com\r\n> Content-Length: 11406640119\r\n> Content-Type: application/zip\r\n> User-Agent: git-lfs/2.10.0 (GitHub; darwin amd64; go 1.13.6)\r\n> \r\n18:08:50.864302 trace git-lfs: HTTP: 400 | 278 KB/s \r\n\r\n\r\n< HTTP/1.1 400 Bad Request\r\n< Connection: close\r\n< Transfer-Encoding: chunked\r\n< Content-Type: application/xml\r\n< Date: Thu, 12 Nov 2020 02:08:49 GMT\r\n< Server: AmazonS3\r\n< X-Amz-Id-2: hV+PVm+Jl6JpvptNirGJM1ZhxunLPQcDUc0z0Ea053vMhwpgNMGs57y/qnEQFaL5ffAzrTmcfOI=\r\n< X-Amz-Request-Id: 7Z1MEY0MAV4T5NCY\r\n< \r\n18:08:50.864739 trace git-lfs: xfer: adapter \"basic\" worker 0 finished job for \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\"\r\n18:08:50.864774 trace git-lfs: tq: refusing to retry \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\", too many retries (8)\r\n18:08:50.864842 trace git-lfs: tq: refusing to retry \"7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386\", too many retries (8)\r\n18:08:50.864891 trace git-lfs: xfer: adapter \"basic\" End()\r\n18:08:50.864903 trace git-lfs: xfer: adapter \"basic\" worker 4 stopping\r\n18:08:50.864910 trace git-lfs: xfer: adapter \"basic\" worker 0 stopping\r\n18:08:50.864929 trace git-lfs: xfer: adapter \"basic\" worker 3 stopping\r\n18:08:50.864935 trace git-lfs: xfer: adapter \"basic\" worker 1 stopping\r\n18:08:50.864940 trace git-lfs: xfer: adapter \"basic\" worker 7 stopping\r\n18:08:50.864946 trace git-lfs: xfer: adapter \"basic\" worker 6 stopping\r\n18:08:50.864954 trace git-lfs: xfer: adapter \"basic\" worker 2 stopping\r\n18:08:50.864956 trace git-lfs: xfer: adapter \"basic\" worker 5 stopping\r\n18:08:50.865017 trace git-lfs: xfer: adapter \"basic\" stopped\r\nLFS: Client error: https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020847Z&X-Amz-Expires=900&X-Amz-Signature=81eab82a86449f39b5dc223fbdf2bd23ecd4834899033d6a896742ea480a1985&X-Amz-SignedHeaders=host\r\nUploading LFS objects: 0% (0/1), 9.7 MB | 278 KB/s, done.\r\nerror: failed to push some refs to 'https://huggingface.co/allenai/unifiedqa-t5-3b'\r\n* Closing connection 0\r\n```\r\n\r\nI see lines like this that contain error messages, but not sure what they mean: \r\n```\r\n18:08:47.217349 trace git-lfs: tq: retrying object 7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386: LFS: Client error: https://s3.amazonaws.com/lfs.huggingface.co/allenai/unifiedqa-t5-3b/7e295e01528dc6a361211884f82daac33c422089644d0f5b2ddb2d96166aa386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20201112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201112T020836Z&X-Amz-Expires=900&X-Amz-Signature=25e0ceea23657b4711a66397bf0e4274d53934c669794d8f31e5be4472d27493&X-Amz-SignedHeaders=host\r\n```\r\n\r\nI also tried tweaking the git config parameters a bit, just in case they matter; but did not help. \r\n```\r\n$ git config --global lfs.transfer.maxretries 10\r\n$ git config --global lfs.dialtimeout 600000\r\n```\r\n", "Very weird, and unfortunately error messages from S3 aren't very informative (we're generating presigned upload urls to S3, in your log git-lfs actually tries to upload to S3).\r\n\r\nWhat kind of upload bandwidth do you have? (cc @pierrci) Can you share your pytorch_model.bin somewhere so that I can try pushing it in a clone of your model?\r\n\r\nThe one thing I'm thinking about is maybe if S3 presigned URLs expire while the upload is still underway, does it reject the download.", "@julien-c The model files are here: https://console.cloud.google.com/storage/browser/unifiedqa/tmp;tab=objects \r\n\r\n> What kind of upload bandwidth do you have?\r\n\r\nI am actually not sure how to answer this question. But my internet is quite reliable; never had any major issues with download/uploads. \r\n", "Ok @danyaljj, we can reproduce and will be working on a fix in the coming weeks.\r\n\r\nIn the meantime, do you want me to upload your models manually?", "> In the meantime, do you want me to upload your models manually? \r\n\r\nThat would be great! 🙏 ", "⚠️⚠️ For anyone else in the Hugging Face team (@patrickvonplaten notably) who might have to upload large models before we improve native support for large files (ETA = about 2 weeks), here's the current workaround (Reminder: previous workaround was simply `aws s3 cp` as the `transformers-cli` already had the same issue):\r\n\r\n- compute sha256 of large file with e.g. `sha256sum` (takes 3 mins on a beefy machine for 42GB t5-11b checkpoint)\r\n- copy the file to our lfs bucket, named with the sha256: `aws s3 cp pytorch_model.bin s3://lfs.huggingface.co/{model_id}/{sha256_from_above}`\r\n- clone the model repo you want to push to (with `GIT_LFS_SKIP_SMUDGE=1`) and write an LFS pointer file manually at the file's place, replacing the sha256 and the file size: example for t5-3b is https://huggingface.co/allenai/unifiedqa-t5-3b/blob/main/pytorch_model.bin\r\n- commit and push\r\n\r\nYou can then check that it worked, with (with lfs installed):\r\n\r\n```\r\ngit clone https://huggingface.co/{model_id}\r\n```\r\n\r\ncc @Pierrci @Narsil @thomwolf ", "Ok @danyaljj thanks for your patience 😄 \r\n\r\nFiles are uploaded at \r\nhttps://huggingface.co/allenai/unifiedqa-t5-11b/commits/main\r\nand\r\nhttps://huggingface.co/allenai/unifiedqa-t5-3b/commits/main\r\n\r\nI've checked that git clones work, though the clone takes a pretty long time for the 11b model :)\r\n\r\nLet me know if any issue.", "Appreciate the help, @julien-c 🙏 ", "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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[ "LGTM, but will break CI. no idea why it's not running.\r\nYou will need to update the tests. I bet `pytest examples/seq2seq/test_seq2seq_examples.py` will fail ( you should fix that).\r\n", "Yeah, here is the error I am seeing. \r\n\r\n```\r\n$ pytest examples/seq2seq/test_seq2seq_examples.py\r\ncomet_ml is installed but `COMET_API_KEY` is not set.\r\nTraceback (most recent call last):\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/_pytest/config/__init__.py\", line 430, in _importconftest\r\n return self._conftestpath2mod[conftestpath]\r\nKeyError: local('/Users/danielk/ideaProjects/transformers/examples/conftest.py')\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/Users/danielk/opt/anaconda3/bin/pytest\", line 10, in <module>\r\n sys.exit(main())\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/_pytest/config/__init__.py\", line 58, in main\r\n config = _prepareconfig(args, plugins)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/_pytest/config/__init__.py\", line 196, in _prepareconfig\r\n pluginmanager=pluginmanager, args=args\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/hooks.py\", line 286, in __call__\r\n return self._hookexec(self, self.get_hookimpls(), kwargs)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/manager.py\", line 92, in _hookexec\r\n return self._inner_hookexec(hook, methods, kwargs)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/manager.py\", line 86, in <lambda>\r\n firstresult=hook.spec.opts.get(\"firstresult\") if hook.spec else False,\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/callers.py\", line 203, in _multicall\r\n gen.send(outcome)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/_pytest/helpconfig.py\", line 93, in pytest_cmdline_parse\r\n config = outcome.get_result()\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/callers.py\", line 80, in get_result\r\n raise ex[1].with_traceback(ex[2])\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/callers.py\", line 187, in _multicall\r\n res = hook_impl.function(*args)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/_pytest/config/__init__.py\", line 675, in pytest_cmdline_parse\r\n self.parse(args)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/_pytest/config/__init__.py\", line 845, in parse\r\n self._preparse(args, addopts=addopts)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/_pytest/config/__init__.py\", line 809, in _preparse\r\n early_config=self, args=args, parser=self._parser\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/hooks.py\", line 286, in __call__\r\n return self._hookexec(self, self.get_hookimpls(), kwargs)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/manager.py\", line 92, in _hookexec\r\n return self._inner_hookexec(hook, methods, kwargs)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/manager.py\", line 86, in <lambda>\r\n firstresult=hook.spec.opts.get(\"firstresult\") if hook.spec else False,\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/callers.py\", line 208, in _multicall\r\n return outcome.get_result()\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/callers.py\", line 80, in get_result\r\n raise ex[1].with_traceback(ex[2])\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/callers.py\", line 187, in _multicall\r\n res = hook_impl.function(*args)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/_pytest/config/__init__.py\", line 719, in pytest_load_initial_conftests\r\n self.pluginmanager._set_initial_conftests(early_config.known_args_namespace)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/_pytest/config/__init__.py\", line 379, in _set_initial_conftests\r\n self._try_load_conftest(current)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/_pytest/config/__init__.py\", line 382, in _try_load_conftest\r\n self._getconftestmodules(anchor)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/_pytest/config/__init__.py\", line 414, in _getconftestmodules\r\n mod = self._importconftest(conftestpath)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/_pytest/config/__init__.py\", line 464, in _importconftest\r\n self.consider_conftest(mod)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/_pytest/config/__init__.py\", line 492, in consider_conftest\r\n self.register(conftestmodule, name=conftestmodule.__file__)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/_pytest/config/__init__.py\", line 306, in register\r\n ret = super(PytestPluginManager, self).register(plugin, name)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/manager.py\", line 126, in register\r\n hook._maybe_apply_history(hookimpl)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/hooks.py\", line 333, in _maybe_apply_history\r\n res = self._hookexec(self, [method], kwargs)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/manager.py\", line 92, in _hookexec\r\n return self._inner_hookexec(hook, methods, kwargs)\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/manager.py\", line 86, in <lambda>\r\n firstresult=hook.spec.opts.get(\"firstresult\") if hook.spec else False,\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/callers.py\", line 208, in _multicall\r\n return outcome.get_result()\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/callers.py\", line 80, in get_result\r\n raise ex[1].with_traceback(ex[2])\r\n File \"/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/pluggy/callers.py\", line 187, in _multicall\r\n res = hook_impl.function(*args)\r\n File \"/Users/danielk/ideaProjects/transformers/examples/conftest.py\", line 20, in pytest_addoption\r\n from transformers.testing_utils import pytest_addoption_shared\r\n File \"/Users/danielk/ideaProjects/transformers/src/transformers/__init__.py\", line 135, in <module>\r\n from .pipelines import (\r\n File \"/Users/danielk/ideaProjects/transformers/src/transformers/pipelines.py\", line 38, in <module>\r\n from .tokenization_auto import AutoTokenizer\r\n File \"/Users/danielk/ideaProjects/transformers/src/transformers/tokenization_auto.py\", line 119, in <module>\r\n from .tokenization_albert_fast import AlbertTokenizerFast\r\n File \"/Users/danielk/ideaProjects/transformers/src/transformers/tokenization_albert_fast.py\", line 23, in <module>\r\n from .tokenization_utils_fast import PreTrainedTokenizerFast\r\n File \"/Users/danielk/ideaProjects/transformers/src/transformers/tokenization_utils_fast.py\", line 30, in <module>\r\n from .convert_slow_tokenizer import convert_slow_tokenizer\r\n File \"/Users/danielk/ideaProjects/transformers/src/transformers/convert_slow_tokenizer.py\", line 25, in <module>\r\n from tokenizers.models import BPE, Unigram, WordPiece\r\nImportError: cannot import name 'Unigram' from 'tokenizers.models' (/Users/danielk/opt/anaconda3/lib/python3.7/site-packages/tokenizers/models/__init__.py)\r\n```\r\n\r\nLikely caused by previous changes? \r\n", "The above error comes from a wrong version of `tokenizers` being installed.\r\n\r\nCould you \r\n```\r\npip install --upgrade tokenizers\r\n```\r\n\r\nand re-run the tests? ", "Have updated the branch now and I *think* the previous error has gone away. ", "Looks good to me. @patil-suraj can you take a final look and merge if you want?", "Something is wrong with this PR, \r\n\r\n1) didn't go through autoformatters\r\n2) failing CI on current master. e.g. see: https://app.circleci.com/pipelines/github/huggingface/transformers/16336/workflows/09f5b053-9f0e-4f70-aae8-3b31c79227f0/jobs/125984 (from unrelated recent PR https://github.com/huggingface/transformers/pull/8798)\r\n3) It looks like CI has never passed on this PR yet was merged - odd\r\n", "Hey @danyaljj - sorry we merged your PR too early without exactly checking whether everything was fine or not. A couple of tests were actually failing on master due to the merge of this PR and I just reverted the PR. \r\n\r\nCould you maybe open a new PR and we'll all make sure this time that all tests pass? :-) \r\n\r\nSorry for the inconvenience! The mistake is definitely on us here!", "post mortem - for some reason this PR had no indication of CI pass/fail - one can only see its status in the merge https://github.com/huggingface/transformers/commit/5aa361f3e56de0f65720f291bb3975bfc98f2837, which fails 3 CIs. \r\n\r\nSo this was definitely an odd situation and probably some bug in CI software itself.", "The tests are failing because in s2s tests the `args` are directly passed to `finetune.py`'s `main` function and the newly added `eval_min_gen_length` is not included in it. \r\n\r\nTwo changes to make the tests pass\r\n1. add `eval_min_gen_length` in `CHEAP_ARGS` `dict` here https://github.com/huggingface/transformers/blob/master/examples/seq2seq/test_seq2seq_examples.py#L30\r\n2. run `make style`" ]

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[ "Take a look at https://github.com/huggingface/transformers/issues/5164", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "I'm not sure what the bug is: by requiring the complete predictions for your `compute_metrics` function, you are asking for an array of 4,057 by 200 by vocab_size (which for the base CamemBERT model is 30,522 I believe). This does not fit easily in RAM.\r\n", "Is there another way to compute the metrics (or an estimation) without having to build such a huge vector ?", "You haven't shared what metric you are using so I have no idea.", "This the function i'm using:\r\n\r\n```python\r\nfrom sklearn.metrics import precision_recall_fscore_support\r\ndef compute_metrics(p: EvalPrediction) -> Dict:\r\n #print('raw_predictions: ', p.predictions, '\\n')\r\n #print('labels: ', p.label_ids,'\\n')\r\n preds = np.argmax(p.predictions, axis=-1)\r\n #print('shape:', preds.shape, '\\n')\r\n precision, recall, f1, _ = precision_recall_fscore_support(p.label_ids.flatten(), preds.flatten(), average='weighted', zero_division=0)\r\n return {\r\n 'accuracy': (preds == p.label_ids).mean(),\r\n 'f1': f1,\r\n 'precision': precision,\r\n 'recall': recall\r\n }\r\n```", "I guess you could write your custom loop to store the predictions after the argmax together, this won't blow up memory the same way.", "Great, thanks a lot for the tip !\r\n\r\nI ll mark the issue as closed.", "@soufianeelalami Did you come up with a solution for this issue? Our team has run into the same issue with `nested_conat` while evaluating on a fairly large dataset.", "@gphillips-ema Hello, basically what you need to do is create your trainer class (which inherits from the trainer class) then override the ```prediction_loop```method to change one particular behavior:\r\n```python\r\nif logits is not None:\r\n #preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index=-100)\r\n logits_reduced = np.argmax(logits, axis=-1)\r\n preds_host = logits_reduced if preds_host is None else nested_concat(preds_host, logits_reduced, padding_index=-100)\r\n```\r\nYou need to do a ```np.argmax(logits, axis=-1)``` to reduce the dimension of the output logit vector. \r\n\r\nIf you are using accumulation, then you need to do the same thing in that part of the code (always in the ```prediction_loop```method).\r\n\r\nPlease let me know if this solves your problem or if you need any help.", "I was facing a related issues with `nested_concat` that caused GPU memory errors. Using the `Seq2SeqTrainer` instead of the default Trainer solved the issue for me, since does not rely on concatenating the logits over the vocabulary. ", "Same issue, I got an A5000 gpu for training, but I can't even eval with batch_size=8." ]

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[ "Great fix, thanks!" ]

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[ "I imagine this only happens when the `generate()` method is used under the hood? ", "Hi there! It's true you can't just do `model.half()` for generation. There is nothing in Trainer/Seq2SeqTrainer right now for FP16-inference, only training, but we're looking at it right now through #8403. So stay tuned!", "Thanks for the input @sgugger. Good to know we're not missing something here and it's actually unsupported somehow. ", "Hi,\r\nI've noticed the same issue of the model randomly generating junk when using autocast within a custom generate() method with the only change below (fp16 is a boolean). From the above comments I thought this approach should've worked.\r\n```\r\nif fp16:\r\n\twith torch.cuda.amp.autocast():\r\n\t\toutputs = self(**model_inputs)\r\nelse:\r\n\toutputs = self(**model_inputs)\r\n```\r\n\r\nThe current model I've tested it on is a huggingface gpt2 model finetuned on a personal dataset. Without fp16 the generate works perfectly. The dataset is very specific and the model is supposed to generate symbols+numbers, so it's clear when it starts spitting out words during fp16 inference.\r\n", "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread.", "has this been solved?" ]

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[ "@g-karthik ", "Was able to reproduce this as well.\r\n\r\nFrom the observation, I suspect the random weights being initialized for the added tokens in the final `Linear` and/or `SequenceSummary` head are such that no matter what hidden state is sent in, a special token gets the highest final scalar score. Haven't dived in to check how the random initialization is done, but if it's done from a standard unit Gaussian, I would imagine this cannot happen at every single time-step.", "You should fine-tune your model on a dataset containing your added tokens, otherwise the model will very probably generate gibberish.", "@LysandreJik The model is only generating special tokens. It does not generate any of the original tokens in the pre-trained model's vocabulary. I'd understand if there were special tokens generated occasionally, but that's not the case.", "I understand. By adding new tokens, you're resizing the token embedding layer with some *randomly initialized values*. These values can be of an entirely different dimension to the ones currently initialized in your token embedding layer, which can lead to these tokens being overly generated.\r\n\r\nAs I said before: you should fine-tune your model on a dataset containing your added tokens, otherwise the model will very probably generate gibberish.", "I'm aware that fine-tuning on a dataset containing the added tokens will bring those \"entirely different dimension\" (as you call them) values back to the \"same dimension\". But that's besides the point here. We're talking about expected behavior.\r\n\r\n> By adding new tokens, you're resizing the token embedding layer with some randomly initialized values\r\n\r\nIt's not just the token embedding layer that'll get resized though, right? There's the final `Linear` that outputs the distribution over the vocabulary as well that would have to be adjusted to account for the new vocabulary size.\r\n\r\n> These values can be of an entirely different dimension to the ones currently initialized in your token embedding layer\r\n\r\nCan you please point me to the code that does this \"entirely different dimension\" random initialization for the added tokens? If the argument you're making is that a pre-trained model *should* generate *only-special-tokens* gibberish if special tokens were added to its vocabulary and it were resized accordingly, then I disagree with it. I would expect a mix of both, and if the random initialization can be altered to ensure the pre-trained model's behavior with and without added special tokens is *mostly* similar, that would be the best outcome for consistency. That's why I brought up the point about random initialization from a unit Gaussian earlier in this issue.", "> It's not just the token embedding layer that'll get resized though, right? There's the final Linear that outputs the distribution over the vocabulary as well that would have to be adjusted to account for the new vocabulary size.\r\n\r\nYes, you are right. However, the embedding layer and output linear layer are shared. They have the same values, as they are tied. Resizing the embedding layer resizes the output layer at the same time.\r\nThis is done in the [_tie_or_clone_weights method](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_utils.py#L571), which is called by the [init_weights method](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_gpt2.py#L705).\r\n\r\n> Can you please point me to the code that does this \"entirely different dimension\" random initialization for the added tokens?\r\n\r\n[Here it is](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_gpt2.py#L341-L351).\r\n\r\n> If the argument you're making is that a pre-trained model should generate only-special-tokens gibberish if special tokens were added to its vocabulary and it were resized accordingly, then I disagree with it.\r\n\r\nI am not making this argument. The argument I'm making is that if you're adding *any* token to an embedding matrix **without** training the model afterwards, you will obtain gibberish.", "> The argument I'm making is that if you're adding any token to an embedding matrix without training the model afterwards, you will obtain gibberish.\r\n\r\nWhat @al3xpapangelis and I are saying is that a pre-trained model's behavior *before* and *after* adding *any* new token, with no *further* training done beyond the original pre-training, should be the same *on average* for the same input. We're talking about expected behavior vs. actual behavior, and what can be done to make them the same. Your argument makes sense to me, but only in the scenario where the model's not been trained originally at all.\r\n\r\nThanks for the pointers! Looks like the random initialization for `Linear` is from a 0-centered, 0.02 std. deviation Gaussian. I'll do some analysis to see how vectors from this distribution \"vary\" on average from a pre-trained embedding for a regular token.", "Hey @g-karthik and hey @al3xpapangelis (cool to see you here again :-) ), \r\n\r\nThe reason for this behavior is mainly because `lm_head` is tied to the word_embedding matrix and therefore the softmax over the output logit vectors seems to give very high values to the randomly init tokens. So this seems to suggest the distribution of trained logit vectors is very different from the rnd init ones.\r\n\r\nI'd also suggest to play around with changing the init scheme for new tokens or just setting the newly added tokens manually to some better value:\r\n\r\n```python\r\nmodel.lm_head.weight[:, -2] = # good init vector for <USER>\r\nmodel.lm_head.weight[:, -1] = # good init vector for <SYSTEM>\r\n```\r\n\r\nIf you do this for example, the tokens won't be generated.\r\n\r\n```python\r\nimport torch\r\nimport torch.nn.functional as F\r\nfrom transformers import GPT2Tokenizer, GPT2LMHeadModel\r\n\r\ntokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')\r\ntokenizer.add_special_tokens(\r\n\t{'additional_special_tokens': ['<USER>', '<SYSTEM>']}\r\n)\r\n\r\nmodel = GPT2LMHeadModel.from_pretrained('distilgpt2')\r\nmodel.resize_token_embeddings(len(tokenizer))\r\ninp_tok_ids = tokenizer.encode('I want a pepperoni pizza with mushroom')\r\ninp_tensor = torch.LongTensor(inp_tok_ids).unsqueeze(0)\r\nmodel.eval()\r\n\r\nmodel.lm_head.weight[-2, :] = (torch.zeros((768,)) - 10000.0) \r\nmodel.lm_head.weight[-1, :] = (torch.zeros((768,)) - 10000.0) \r\n\r\nwith torch.no_grad():\r\n\tfor i in range(10):\r\n\t\toutputs = model(inp_tensor)\r\n\t\tlogits = outputs[0][:, -1, :]\r\n\t\tprobs = F.softmax(logits, dim=-1)\r\n\t\tnext_token = torch.multinomial(probs, num_samples=1).squeeze(1)\r\n\t\tinp_tensor = torch.cat([inp_tensor, next_token.unsqueeze(-1)], dim=-1)\r\n\r\nprint(tokenizer.decode(inp_tensor[0]))\r\n```", "@patrickvonplaten yes, I was thinking I'll try and estimate the mean and covariance of the set of values in GPT-2's pre-trained embeddings (across all of its 4 model sizes), assuming a Gaussian distribution. And then update the random initialization's mean and std. dev. accordingly in the model's [`_init_weights()`](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_gpt2.py#L346). That way, the random initialization comes from a distribution that's effectively \"similar\" to that of the pre-trained vectors, and hence decoding sequences would result in a mixture of the original tokens and added tokens.", "Thanks @LysandreJik and @patrickvonplaten! I like @g-karthik suggestion, it would be nice for this bevahiour to happen automatically", "i found the same problem while i was trying to fine tune gpt2 , and here is how i have solved it: \r\n\r\n```\r\n# special tokens are defined\r\nadd_spe_tokens = ['<|ANS|>']\r\n\r\nspecial_tokens_dict = {'eos_token': '<|EOS|>', \r\n 'bos_token': '<|SOS|>', \r\n 'pad_token': '<|PAD|>', \r\n 'additional_special_tokens':add_spe_tokens}\r\n\r\n# the new token is added to the tokenizer\r\ntokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\nnum_added_toks = tokenizer.add_special_tokens(special_tokens_dict)\r\nprint(f\"bos_token_id: {tokenizer.bos_token_id}\")\r\nprint(f\"eos_token_id: {tokenizer.eos_token_id}\")\r\nprint(f\"pad_token_id: {tokenizer.pad_token_id}\")\r\n\r\n\r\n# model configuration to which we add the special tokens\r\nconfig = AutoConfig.from_pretrained('gpt2', \r\n bos_token_id=tokenizer.bos_token_id,\r\n eos_token_id=tokenizer.eos_token_id,\r\n pad_token_id=tokenizer.pad_token_id,\r\n output_hidden_states=False)\r\n\r\n# we load the pre-trained model with custom settings\r\nmodel = GPT2LMHeadModel.from_pretrained('gpt2', config=config)\r\n\r\n# model embeding resizing\r\nprint(\"before : \", model.lm_head.weight.shape)\r\nmodel.resize_token_embeddings(len(tokenizer))\r\n\r\nnew_weights = torch.cat([model.lm_head.weight[:-4, :], torch.zeros(4, model.lm_head.weight.shape[1]) -10000])\r\nmodel.lm_head.weight = torch.nn.Parameter(new_weights) \r\n\r\n\r\nprint(\"after : \", model.lm_head.weight.shape)\r\n```\r\n\r\n```\r\ninput_text = \"Once upon a time,\"\r\n\r\ninput_ids = tokenizer.encode(input_text, return_tensors=\"pt\")\r\nprint(input_ids)\r\noutput = model.generate(input_ids, max_length=50, num_return_sequences=1)\r\n\r\nprint(output)\r\n# Decode and print the generated text\r\n# generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\r\n# print(generated_text)\r\n```", "but i am not sure if this will ruin the pre-trained weights while fine tuning or not " ]

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

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[ "maybe @julien-c or @Pierrci can answer this question better", "This seems very environment-specific. @wehriam in case paid support is an option, we have a technical support program in beta at [email protected] (cc @clmnt @jeffboudier)", "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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[ "Hello, thanks for opening an issue! We try to keep the github issues for bugs/feature requests.\r\nCould you ask your question on the [forum](https://discuss.huggingface.co) instead?\r\n\r\nThanks!" ]

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[ "I also got different results 🤦‍", "I have figured out the problem, but I don't have the solution.\r\nWhen you use a single sample per batch it works correctly, but when you use more than one sample per batch, the results are totally different.", "@mfuntowicz and @LysandreJik , it will be great if you could show us an example for how to correctly use batch processing for onnx inference.", "Hey @agemagician! I have exactly the same problem, great inference time with onnx when batch size = 1 but when batch size is increased, raw pytorch wins over onnx. Did you find any solution for the issue?", "unfortunately, not.", "That's unfortunate..", "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread." ]

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[ "really cool, thanks for sharing" ]

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[ "cc @patrickvonplaten ", "No, 2 character language codes are not covered. However `>>.{2}<<|>>.{3}<<|>>.{3}\\_.{4}<<` can be used.\r\n\r\nI can try writing tests. Is these somewhere where I could find an exhaustive set of language codes? I had written the regex to cover the 194 language code in `'Helsinki-NLP/opus-mt-en-mul'`.\r\n\r\n```python\r\ndef test_language_codes(self):\r\n tok = MarianTokenizer.from_pretrained(f\"{ORG_NAME}opus-mt-en-mul\")\r\n batch = tok.prepare_seq2seq_batch([\">>hin<< I am a small frog\", \">>zlm_Latn<< I am a small frog\", \">>fr<< I am a small frog\"], return_tensors=FRAMEWORK)\r\n \r\n expected = [[[888, 21, 437, 9, 2613, 37565, 0], [770, 21, 437, 9, 2613, 37565, 0], [1, 21, 437, 9, 2613, 37565, 0]]\r\n for i in range(3):\r\n self.assertListEqual(expected[i], batch.input_ids[i])\r\n```\r\n\r\nThe above will need some changes since `>>fr<<` is not in the vocabulary and the sentences also do not have another `<<` in them (maybe a different test for that?).", "> Is there somewhere where I could find an exhaustive set of language codes?\r\n\r\nNot easily, you can find the supported codes for any model by looking for `tgt_languages` or `tgt_constituents` on a model card e.g. https://huggingface.co/Helsinki-NLP/opus-mt-en-ROMANCE or https://huggingface.co/Helsinki-NLP/opus-mt-en-roa?text=%3E%3Efra%3C%3C+My+name+is+Sarah+and+I+live+in+London\r\n\r\n\r\nHere are some more:\r\n\r\n```python\r\ntgt_constituents= {'ita', 'cat', 'roh', 'spa', 'pap', 'lmo', 'mwl', 'lij', 'lad_Latn', 'ext', 'ron', 'ast', 'glg', 'pms', 'zsm_Latn', 'gcf_Latn', 'lld_Latn', 'min', 'tmw_Latn', 'cos', 'wln', 'zlm_Latn', 'por', 'egl', 'oci', 'vec', 'arg', 'ind', 'fra', 'hat', 'lad', 'max_Latn', 'frm_Latn', 'scn', 'mfe'}\r\ntgt_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}\r\n```\r\n\r\n\r\nI think we should accept any number of alphabet characters or '_' inside the `>>` or at least up to 8.\r\n\r\n\r\n\r\n", "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread." ]

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[ "will report this in datasets repo. " ]

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[ "Hi! That sounds good, do you want to open a PR?", "Yes, I am on it. Thanks!\r\n", "+1\r\nany updates?", "@iremnasir can you please let me know how exactly you managed to solve it? I am having the same issue. \r\n\r\n", "Hi, you can see my answer above for the solution in Expected Behavior section", "Got it. Thanks\r\n\r\nAs got the error for AutoTokenizer: `NameError: name 'AutoTokenizer' is not defined `\r\nJust imported all:\r\n`from transformers import * ` earlier I was only importing `pipeline` from transformers", "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread.", "Thanks @iremnasir, it worked. Why hasn't this been merged yet?" ]

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[ "The five examples scripts mentioned above all already use the huggingface logger, and already set it to the correct verbosity level. Seeing as the example scripts are *examples*, they really should be as straightforward as possible, and should not repeat unnecessary statements.\r\n\r\nPlease update the following files: `run_clm.py`, `run_mlm.py`, `run_glue.py`, `run_mlm_wwm.py`, `run_plm.py` so that they do not have useless statements.\r\n\r\n@sgugger pointed you towards the correct area, you should be able to find it if you look for \"logging\".", "I don't see the import neither the usage. Can you link it please?", "https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_clm.py#L170", "Ok got it I was looking at my own updated file ahah.", "It is also in your file, below https://github.com/huggingface/transformers/blob/d410b83111238f7b949b7c9c6a4c3f689d29519b/examples/language-modeling/run_clm.py#L176", "Ok I have removed the duplicate import and add only the two missing calls.", "Already done the changes 👍 ", "No, the changes are not done:\r\n1. in the scripts like the new `run_glue.py`, the code for logging is duplicated and executed once for every process, then once for the main process.\r\n2. in all the other scripts, the code is executed on all processes.\r\n\r\nThis is also added in util files or test files where it's just not necessary.", "I really don't get what you mean, sorry. What are the exact changes I have to make for each file?", "@sgugger Please check the last commit and let me know if it was what you meant. Otherwise, please, can you be more specific in what you want me to change." ]

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[ "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread." ]

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[ "change other questions.", "How did u solve this issue?", "How did u solve this problem? I got same problem like yours" ]