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https://api.github.com/repos/huggingface/transformers/issues/3712 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3712/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3712/comments | https://api.github.com/repos/huggingface/transformers/issues/3712/events | https://github.com/huggingface/transformers/issues/3712 | 597,074,840 | MDU6SXNzdWU1OTcwNzQ4NDA= | 3,712 | Text Generation with XLNet is very Slow | {
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"Reopened the previous issue: https://github.com/huggingface/transformers/issues/789 and will take a look next week :-) ",
"Closing this as well. Reasons are explained in #789."
] | 1,586 | 1,591 | 1,591 | NONE | null | Using the run_generation script to generate text with XLNet is currently extremely slow compared to GPT-2 as mentioned in [this issue](https://github.com/huggingface/transformers/issues/789)
> To generate 100 tokens, XLNet takes **3m22s** while GPT-2 takes **14s**. And it grows exponentially : for 500 tokens, XLNet ... | {
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When called with labels to get the language-modeling loss, `TransfoXLLMHead.forward` computes the NLLLoss of the outputs directly against the labels, rather than against the shifted labels like the documentation indicates (and like the other models). This makes it impossible to train with `lm_labels = input... | {
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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"
] | 1,586 | 1,592 | 1,592 | NONE | null | # π Bug
## Information
Model I am using (Bert, XLNet ...): Bert
Language I am using the model on (English, Chinese ...): Chinese
The problem arises when using: BertTokenizer
I am using the BertModel. When predicting the result, it got inconsistent output (the output differs from time to time). It turns ... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3709?src=pr&el=h1) Report\n> Merging [#3709](https://codecov.io/gh/huggingface/transformers/pull/3709?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6435b9f908e7361330db89e263a65b0a58060d11&el=desc) will **decrease... | 1,586 | 1,587 | 1,587 | CONTRIBUTOR | null | Add model tag to be correctly indexed while working on card description | {
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https://api.github.com/repos/huggingface/transformers/issues/3707 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3707/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3707/comments | https://api.github.com/repos/huggingface/transformers/issues/3707/events | https://github.com/huggingface/transformers/issues/3707 | 596,881,489 | MDU6SXNzdWU1OTY4ODE0ODk= | 3,707 | Distributed training on multiple GPU nodes is slower than on single GPU node | {
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"Are those `p3.16xlarge` is the same AZ? Even in the same AZ, the throughput and latency between machines might be the bottleneck here.",
"i.e. you usually need infiniband between cluster machines. But @mfuntowicz knows about this stuff way better than I do...",
"Thanks for your reply. The two hosts are indeed ... | 1,586 | 1,586 | 1,586 | NONE | null | Our team uses pre-trained model and transformers has been a great help.
While trying to benchmark the training speed of our computing infrastructure, we were running this example:
https://github.com/huggingface/transformers/blob/v2.3.0/examples/run_lm_finetuning.py
We found that on a single p3.16xlarge GPU inst... | {
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"Hi! \r\n\r\n- 1 - casing is the difference between lowercasing and uppercasing. Uncased models do not handle uppercase letters, and therefore lowercase them:\r\n\r\n```py\r\nfrom transformers import AutoTokenizer\r\n\r\nuncased_tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\r\ncased_tokenizer = A... | 1,586 | 1,594 | 1,594 | NONE | null | I have a few questions about the model notation. And also short info about T5 and ELECTRA. I would like to make separate issues but things are not too complex. I mainly working on CV, so sorry if I being so silly.
### 1 Cased or Uncased
What is mean by cased and uncased?
```
bert-base-uncased
bert-base-cased
`... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3703?src=pr&el=h1) Report\n> Merging [#3703](https://codecov.io/gh/huggingface/transformers/pull/3703?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6435b9f908e7361330db89e263a65b0a58060d11&el=desc) will **decrease... | 1,586 | 1,668 | 1,594 | CONTRIBUTOR | null | This is related to the issue #3646 I opened 2 days ago and which was considered interesting by @julien-c
I added the **support for token-level regression in Bert, Roberta, Albert, XLNet, XLM, DistilBert and in the template for adding a new model** when `self.num_labels == 1`, fixing the docstring to match the new c... | {
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https://api.github.com/repos/huggingface/transformers/issues/3702 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3702/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3702/comments | https://api.github.com/repos/huggingface/transformers/issues/3702/events | https://github.com/huggingface/transformers/pull/3702 | 596,734,349 | MDExOlB1bGxSZXF1ZXN0NDAwOTc2Mzgz | 3,702 | Add `run_glue_tpu.py` that trains models on TPUs | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3702?src=pr&el=h1) Report\n> Merging [#3702](https://codecov.io/gh/huggingface/transformers/pull/3702?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f68d22850ced09bb194b30068ff94ca3409f0879&el=desc) will **decrease... | 1,586 | 1,593 | 1,586 | COLLABORATOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/3701 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3701/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3701/comments | https://api.github.com/repos/huggingface/transformers/issues/3701/events | https://github.com/huggingface/transformers/issues/3701 | 596,685,019 | MDU6SXNzdWU1OTY2ODUwMTk= | 3,701 | Problem with https://transformer.huggingface.co/doc/gpt2-xl | {
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"Duplicate of https://github.com/huggingface/transformers/issues/3452.\r\n\r\nWe should just remove the option cc @julien-c ",
"Youβre right, Iβll get on it",
"Also best username ever, @MarxEngelsLeninStalin ",
"Oh, I like that model as someone who has problems with spelling/grammar and fatigue it's useful to... | 1,586 | 1,591 | 1,591 | NONE | null | Hi, when using it, simple switch the button up to xl one. When you try to generate a prediction of words; it will simply never load regardless of what all the other setting are on. Every other gpt2 model size works; expect the xl one. Tried it on Android and Linux same problem and different browsers Firefox and Google ... | {
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https://api.github.com/repos/huggingface/transformers/issues/3700 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3700/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3700/comments | https://api.github.com/repos/huggingface/transformers/issues/3700/events | https://github.com/huggingface/transformers/issues/3700 | 596,618,877 | MDU6SXNzdWU1OTY2MTg4Nzc= | 3,700 | Would the weights for the main body of the pertained GPT2Model and pertained GPT2DoubleHeadsModel be identical? | {
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"Hi, except if you freeze the transformer's (transformer being the base model) weights during the training, these will be modified during the fine-tuning. I believe none of our scripts in the library freeze the transformer's weight; so the weights would not be identical.",
"This issue has been automatically marke... | 1,586 | 1,591 | 1,591 | NONE | null | Hello,
From my understanding, the difference between the GPT2Model and GPT2DoubleHeadsModel is that GPT2Model does not include any output head, whereas GPT2DoubleHeadsModel includes two types of output heads (lmhead and mchead).
I am wondering, would the weights used in the pertained GPT2Model identical to the we... | {
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https://api.github.com/repos/huggingface/transformers/issues/3699 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3699/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3699/comments | https://api.github.com/repos/huggingface/transformers/issues/3699/events | https://github.com/huggingface/transformers/issues/3699 | 596,601,173 | MDU6SXNzdWU1OTY2MDExNzM= | 3,699 | Bug in ElectraForTokenClassification | {
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"Oh no, it should be `self.config.num_labels` instead of `self.num_labels` [here](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_electra.py#L665)!",
"My bad, it should be fixed now!"
] | 1,586 | 1,586 | 1,586 | NONE | null | # π Bug
## Information
Model I am using (Bert, XLNet ...): ElectraForTokenClassification
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
* [β] the official example scripts: (give details below)
huggingface.co/transformers/model_doc/electra.html#electrafor... | {
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https://api.github.com/repos/huggingface/transformers/issues/3697 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3697/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3697/comments | https://api.github.com/repos/huggingface/transformers/issues/3697/events | https://github.com/huggingface/transformers/pull/3697 | 596,519,422 | MDExOlB1bGxSZXF1ZXN0NDAwODAxNTM5 | 3,697 | Fix force_download of files on Windows | {
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"Can't test this right now as I don't have access to a Windows machine - does this still work if the model doesn't already exist?",
"> \r\n> \r\n> Can't test this right now as I don't have access to a Windows machine - does this still work if the model doesn't already exist?\r\n\r\nYes, I tested that π. There sh... | 1,586 | 1,586 | 1,586 | CONTRIBUTOR | null | On Windows, `os.rename` gives the error
```
FileExistsError: [WinError 183] Cannot create a file when that file already exists:
```
when trying to re-download a model that already exists in cache using `force_download=True`. | {
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https://api.github.com/repos/huggingface/transformers/issues/3696 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3696/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3696/comments | https://api.github.com/repos/huggingface/transformers/issues/3696/events | https://github.com/huggingface/transformers/issues/3696 | 596,494,132 | MDU6SXNzdWU1OTY0OTQxMzI= | 3,696 | Can not set different token and model dir in `run_glue.py` | {
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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"
] | 1,586 | 1,592 | 1,592 | NONE | null | # π Bug
## Information
the original code as follows, when i use args.tokenizer_name different from model_name_or_path, it still call for the model_name_or_path config file.
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
do_lowe... | {
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https://api.github.com/repos/huggingface/transformers/issues/3695 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3695/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3695/comments | https://api.github.com/repos/huggingface/transformers/issues/3695/events | https://github.com/huggingface/transformers/issues/3695 | 596,475,184 | MDU6SXNzdWU1OTY0NzUxODQ= | 3,695 | Deserialize BERT Sequence Lassifier Quantized Model & Inferencing 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"
] | 1,586 | 1,592 | 1,592 | NONE | null | Dynamic Quantization: Loading Quantized Model issue here:
Currently am looking at below collab link for quantization, where all Linear layers are quantized in corresponding BERTForSequence Classification.
Step:1,
Serialized Quantized Model
Step 2:
Deserialized Using below code:
Screenshot#1:
Linear laye... | {
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https://api.github.com/repos/huggingface/transformers/issues/3694 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3694/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3694/comments | https://api.github.com/repos/huggingface/transformers/issues/3694/events | https://github.com/huggingface/transformers/issues/3694 | 596,446,109 | MDU6SXNzdWU1OTY0NDYxMDk= | 3,694 | Extending XLM Roberta for Question Answering | {
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"I've worked around this problem now by not extending the huggingface framework directly but implementing XLMRobertaForQuestionAnswering externally (using huggingface classes etc.). ",
"Would make sense to have it as a new feature though but that's a different issue type. ;)",
"Could you share the code or make ... | 1,586 | 1,587 | 1,586 | NONE | null | # β Questions & Help
The AutoModelForQuestionAnswering is supported by many models, but not yet by XLM Roberta. In the current implementation I could see that most task-specific classes for XLM-R, e.g. XLMRobertaForSequenceClassification are just inheriting from Roberta. However, when I try to extend the class analo... | {
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https://api.github.com/repos/huggingface/transformers/issues/3693 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3693/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3693/comments | https://api.github.com/repos/huggingface/transformers/issues/3693/events | https://github.com/huggingface/transformers/issues/3693 | 596,428,570 | MDU6SXNzdWU1OTY0Mjg1NzA= | 3,693 | How can I pinpoints Logs directory to Google Drive while finetuning GPT-2 model, which helps in visualizing data via tensorboard? | {
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Hi,
I am facing an issue in retrieving logs, while fine-tuning GPT-2 model by using [Google Colab](https://colab.research.google.com/github/interactive-fiction-class/interactive-fiction-class.github.io/blob/master/homeworks/language-model/hw4_transformer.ipynb).
As the finetunining takes several hours, Google C... | {
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https://api.github.com/repos/huggingface/transformers/issues/3692 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3692/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3692/comments | https://api.github.com/repos/huggingface/transformers/issues/3692/events | https://github.com/huggingface/transformers/issues/3692 | 596,307,174 | MDU6SXNzdWU1OTYzMDcxNzQ= | 3,692 | How to use Huggingface pytorch bert to generate the prediction TSV file from the test set of a GLUE task? | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This was implemented ~1 month ago so closing this issue."
] | 1,586 | 1,591 | 1,591 | NONE | null | # β Questions & Help
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
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"Created another environment from scratch and it got resolved.",
"you need install transformers this way:\r\n\r\n```\r\ngit clone https://github.com/huggingface/transformers\r\ncd transformers\r\npip install .\r\n```",
"> you need install transformers this way:\r\n> \r\n> ```\r\n> git clone https://github.com/h... | 1,586 | 1,630 | 1,586 | NONE | null | # π Bug
## Information
Model I am using (Bert, XLNet ...): Albert
Language I am using the model on (English, Chinese ...): English
27 from typing import List, Optional, Sequence, Tuple, Union
28
---> 29 from tokenizers import AddedToken, Encoding
30 from tokenizers.decoders import Decode... | {
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https://api.github.com/repos/huggingface/transformers/issues/3690 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3690/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3690/comments | https://api.github.com/repos/huggingface/transformers/issues/3690/events | https://github.com/huggingface/transformers/issues/3690 | 596,271,543 | MDU6SXNzdWU1OTYyNzE1NDM= | 3,690 | BertSelfAttention have not Add and Norm layer, why??? | {
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"It's in [`BertSelfOutput`](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_bert.py#L261) which is called right after the `BertSelfAttention` in [`BertAttention`](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_bert.py#L316)."
] | 1,586 | 1,586 | 1,586 | NONE | null | {
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https://api.github.com/repos/huggingface/transformers/issues/3689 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3689/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3689/comments | https://api.github.com/repos/huggingface/transformers/issues/3689/events | https://github.com/huggingface/transformers/issues/3689 | 596,270,697 | MDU6SXNzdWU1OTYyNzA2OTc= | 3,689 | Can't update the train_batch_size and eval_batch_size for the training image in a docker container | {
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I tried to train a couple multi-label models with the fast-bert libary using the container files to build up the docker image and uploaded to AWS ECR and used the aws helper notebook that's included in the 'sample notebook' folder in the repo. I have trained for 3 models and **regardless I changed to a different... | {
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https://api.github.com/repos/huggingface/transformers/issues/3688 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3688/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3688/comments | https://api.github.com/repos/huggingface/transformers/issues/3688/events | https://github.com/huggingface/transformers/pull/3688 | 596,239,184 | MDExOlB1bGxSZXF1ZXN0NDAwNTczNzk1 | 3,688 | Big cleanup of `glue_convert_examples_to_features` | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3688?src=pr&el=h1) Report\n> Merging [#3688](https://codecov.io/gh/huggingface/transformers/pull/3688?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/715aa5b1356b878cbab7a7415a1c1b03a7777ae2&el=desc) will **increase... | 1,586 | 1,586 | 1,586 | MEMBER | null | {
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https://api.github.com/repos/huggingface/transformers/issues/3687 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3687/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3687/comments | https://api.github.com/repos/huggingface/transformers/issues/3687/events | https://github.com/huggingface/transformers/issues/3687 | 596,210,761 | MDU6SXNzdWU1OTYyMTA3NjE= | 3,687 | Is it possible to use multiprocessing for pipelines? | {
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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 have the same the problem. When you close the issue does it mean that it is fixed? ",
"I have also met this problem, how can I solv... | 1,586 | 1,640 | 1,592 | NONE | null | I am trying to use multiprocessing for pipelines, but it seems that it's not working. I think it's because the pipeline already uses multiprocessing features and so you can't have multiprocessing inception. Anyone able to get it to work? | {
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https://api.github.com/repos/huggingface/transformers/issues/3686 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3686/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3686/comments | https://api.github.com/repos/huggingface/transformers/issues/3686/events | https://github.com/huggingface/transformers/issues/3686 | 596,178,829 | MDU6SXNzdWU1OTYxNzg4Mjk= | 3,686 | Bug in variable name in NER | {
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"I also see the same issue, has there been a fix for this?\r\nAttributeError: 'BertTokenizer' object has no attribute 'num_added_tokens'",
"@minhtuev you can change `num_added_tokens` to `num_special_tokens_to_add`. This made the fix for me"
] | 1,586 | 1,588 | 1,588 | NONE | null | # π Bug
## Information
Model I am using (Bert, XLNet ...): Irrelevant
Language I am using the model on (English, Chinese ...): Irrelevant
The problem arises when using:
* [x] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)
The tasks I am working o... | {
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https://api.github.com/repos/huggingface/transformers/issues/3685 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3685/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3685/comments | https://api.github.com/repos/huggingface/transformers/issues/3685/events | https://github.com/huggingface/transformers/issues/3685 | 596,164,340 | MDU6SXNzdWU1OTYxNjQzNDA= | 3,685 | Requesting model for TFAlbertForQuestionAnswering | {
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"Hi! We don't currently have a `run_tf_squad` script, but we would appreciate a contribution! The preprocessing of SQuAD shouldn't be different between pytorch and tensorflow. We haven't gotten to testing that as we haven't gotten to writing that script yet.\r\n\r\nIf we're to have a TF SQuAD script, we would have ... | 1,586 | 1,588 | 1,588 | CONTRIBUTOR | null | # π New model addition
Is there support for adding a TensorFlow version of the AlbertForQuestionAnswering model? I would be happy to contribute the work. This would also enable the `run_squad.py` example script for TensorFlow.
It also looks like [the preprocessing of SQuAD data is different](https://github.com/h... | {
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```py
encoded_sequence = tokenizer.encode_plus(sequence)
model(encoded_sequence) # for TensorFlow
mode... | {
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https://api.github.com/repos/huggingface/transformers/issues/3683 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3683/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3683/comments | https://api.github.com/repos/huggingface/transformers/issues/3683/events | https://github.com/huggingface/transformers/pull/3683 | 596,086,589 | MDExOlB1bGxSZXF1ZXN0NDAwNDQ3NTQ1 | 3,683 | question-answering pipeline error : too many values to unpack (expected 2) | {
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"hmmm, `mrm8488/bert-uncased-finetuned-qnli` is a sequence classification model, not a QA model.\r\n\r\nYou probably get warnings while loading it in a QA Pipeline.\r\n\r\nDoes this happen with other (QA) models?",
"This issue has been automatically marked as stale because it has not had recent activity. It will ... | 1,586 | 1,594 | 1,594 | NONE | null | For some `question-answering` models the pipleline encounters extra tuples from the `.model()` call, where we must have exactly 2.
> This stub results in the following error:
```python
from transformers.pipelines import pipeline
model_name = "mrm8488/bert-uncased-finetuned-qnli"
nlp = pipeline("question-answ... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3682?src=pr&el=h1) Report\n> Merging [#3682](https://codecov.io/gh/huggingface/transformers/pull/3682?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a594ee9c84dde933a3d0b4e07ff2994a1960574c&el=desc) will **increase... | 1,586 | 1,586 | 1,586 | MEMBER | null | This PR greatly speeds up the autoregressive decoding for T5 by storing past key / value states.
The summarization test: https://github.com/huggingface/transformers/blob/500aa12318ce5acd289d5edb6cb8266b3c3b162e/tests/test_modeling_t5.py#L260 now takes only 44s whereas before it took 311s -> 7.5x Speed up
This will ... | {
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https://api.github.com/repos/huggingface/transformers/issues/3681 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3681/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3681/comments | https://api.github.com/repos/huggingface/transformers/issues/3681/events | https://github.com/huggingface/transformers/pull/3681 | 596,054,277 | MDExOlB1bGxSZXF1ZXN0NDAwNDIxMDA3 | 3,681 | updating to new transformer | {
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https://api.github.com/repos/huggingface/transformers/issues/3680 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3680/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3680/comments | https://api.github.com/repos/huggingface/transformers/issues/3680/events | https://github.com/huggingface/transformers/issues/3680 | 596,043,180 | MDU6SXNzdWU1OTYwNDMxODA= | 3,680 | How to use GPT2DoubleHeadsModel? | {
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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 see that the issue is closed? what is the answer?",
"We are trying to move such questions more and more to the forum because they g... | 1,586 | 1,688 | 1,601 | NONE | null | Hello,
I have a question about the example shown in the GPT2DoubleHeadsModel documentation page:
https://huggingface.co/transformers/model_doc/gpt2.html#gpt2doubleheadsmodel
In the example, the input to the GPT2DoubleHeadsModel is simply a set of choices. But what if the multiple choice question that I want to p... | {
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https://api.github.com/repos/huggingface/transformers/issues/3679 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3679/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3679/comments | https://api.github.com/repos/huggingface/transformers/issues/3679/events | https://github.com/huggingface/transformers/pull/3679 | 596,042,207 | MDExOlB1bGxSZXF1ZXN0NDAwNDExMjU1 | 3,679 | Update doc for {Summarization,Translation}Pipeline and other tweaks | {
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"@sshleifer It's not 100% needed to have model cards but definitely encouraged as they unlock new features e.g. here, discoverability of the models in the model hub, etc.\r\n\r\nYeah, adding an item to the checklist (I guess in `templates/adding_a_new_model`) would be nice, do you want to do it?"
] | 1,586 | 1,586 | 1,586 | MEMBER | null | {
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https://api.github.com/repos/huggingface/transformers/issues/3678 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3678/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3678/comments | https://api.github.com/repos/huggingface/transformers/issues/3678/events | https://github.com/huggingface/transformers/issues/3678 | 596,001,430 | MDU6SXNzdWU1OTYwMDE0MzA= | 3,678 | run_generation.py with empty input | {
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"Hi @r0levrai, \r\n\r\nsorry for responding so late. Thanks for spotting the error. Once the linked PR is merged generation with an empty prompt should be fine! \r\n\r\nAlso note that there is a generation pipeline now which you could use as follows (once the PR is merged):\r\n\r\n```python\r\nfrom transformers imp... | 1,586 | 1,588 | 1,588 | NONE | null | Hi,
I would like to generate text following some context but also from scratch as seen in *Write with Transformer*. Using both
* `python run_generation_callable.py --model_type=gpt2 --model_name_or_path=gpt2` and feeding an empty prompt to the `Model prompt >>> `
* or changing the line
`prompt_text = args.prompt ... | {
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https://api.github.com/repos/huggingface/transformers/issues/3677 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3677/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3677/comments | https://api.github.com/repos/huggingface/transformers/issues/3677/events | https://github.com/huggingface/transformers/issues/3677 | 595,976,672 | MDU6SXNzdWU1OTU5NzY2NzI= | 3,677 | Does anyone have the XLNet (and ALBERT) NER performance on CONLL-2003 | {
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"With ALBERT v1:\r\n\r\nhttps://github.com/huggingface/transformers/pull/1683#issuecomment-556001607\r\n\r\nI got better results using the recent integrated ELECTRA model :)\r\n\r\n",
"@stefan-it j/w but do you know why bert-lg fine-tuned is listed as achieving 92.8 f1 on conll03 in [this paper?](https://paperswi... | 1,586 | 1,599 | 1,589 | CONTRIBUTOR | null | # β Questions & Help
Most transformer models in the library can be fine-tuned for NER tasks.
In https://huggingface.co/transformers/v2.2.0/examples.html#named-entity-recognition, the performances of the roberta, bert, and distilbert have been reported.
However, I did not find performances achieved by other mode... | {
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https://api.github.com/repos/huggingface/transformers/issues/3676 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3676/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3676/comments | https://api.github.com/repos/huggingface/transformers/issues/3676/events | https://github.com/huggingface/transformers/issues/3676 | 595,945,439 | MDU6SXNzdWU1OTU5NDU0Mzk= | 3,676 | gpt2-medium fine-tuned model.generate joins words and sentences together without space or newline | {
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"I can provide a google colab notebook, the fine-tuned model, the training data (or whatever needed) to show the issue. I hope it's a tokenizer issue rather than a training fault on my side, since re-training would cost a lot of cash (the training data is quite big - ~22 million lines)\r\n\r\n ",
"I'm suspecting ... | 1,586 | 1,615 | 1,596 | NONE | null | Hi,
I have successfully fine-tuned and used a gpt2 model to generate text. My training corpus consist of short sentences - 3-5 words and longer ones 10-15 words. All separated by new line character. Sometimes ending with [ . ! ? ] sometimes not
`outputs = model.generate(
input_ids=input_ids, max_l... | {
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https://api.github.com/repos/huggingface/transformers/issues/3675 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3675/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3675/comments | https://api.github.com/repos/huggingface/transformers/issues/3675/events | https://github.com/huggingface/transformers/issues/3675 | 595,921,356 | MDU6SXNzdWU1OTU5MjEzNTY= | 3,675 | Wrong tokenizer configuration in sentiment-analysis pipeline | {
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"I'm having the same issue",
"> I'm having the same issue\r\n\r\nI got this working by using the following code:\r\n```\r\n # Allocate a pipeline for sentiment-analysis\r\n nlp = pipeline(\"sentiment-analysis\")\r\n nlp.tokenizer = transformers.DistilBertTokenizer.from_pretrained(\"**distilbert-base-uncased... | 1,586 | 1,592 | 1,592 | NONE | null | # π Bug
## Information
When following the Pipelines Notebook 03-pipelines.ipynb, Sentiment Analysis tasks gives wrong result ("NEGATIVE") for example 'Such a nice weather outside !'.
```
nlp_sentence_classif = pipeline('sentiment-analysis')
nlp_sentence_classif('Such a nice weather outside !')
[{'label': 'N... | {
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- "results, memory" are renamed to "time, memory"
- all print statements can optionally be saved in a log file
- the CSV file output format is improved
- better naming in general | {
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"looks like it's not able to find vocabulary file. Make sure there is a vocab.txt file for bert. Otherwise, you can simply load it by `tokenizer = BertTokenizer(vocab_file=\"path to vocab\", and configs)`.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be clos... | 1,586 | 1,593 | 1,593 | NONE | null | # π Bug
> TypeError: stat: path should be string, bytes, os.PathLike or integer, not NoneType`
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-33-cd040b700e71> in <module>()
3 from t... | {
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"You can try to move transformer_base file to the same location of run_bart_sum.py.",
"Hi,\r\n1. As written in `README.md`\r\n> \"To use your own data, copy that files format. Each article to be summarized is on its own line.\"\r\n\r\n I think you should insert in cnn_dm folder your files renamed `train.source`,... | 1,586 | 1,587 | 1,587 | NONE | null | # β Questions & Help
## Details
This is actually a two part question. I have noticed that in [1](https://github.com/huggingface/transformers/blob/master/examples/summarization/bart/run_train.sh) instructions have been given to train with the cnn/dm data. How would we train it with our own data? Should the file form... | {
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"Hi! I don't really understand how you obtained what you did, what script did you use, what arguments did you put in? The procedure to convert an ELECTRA checkpoint from the official implementation to our implementation is to do the following (feel free to skip the first steps if you already have your checkpoint):\... | 1,586 | 1,592 | 1,592 | NONE | null | # β Questions & Help
Hello everyone!
I have been struggling with HuggingFace interface for loading ELECTRA model via transformers.TFElectraModel class. Since TF version of ElectraModel didn't manage to help me restore the checkpoint from the official Google Research implementation (they save only .ckpl files) due... | {
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https://api.github.com/repos/huggingface/transformers/issues/3670 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3670/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3670/comments | https://api.github.com/repos/huggingface/transformers/issues/3670/events | https://github.com/huggingface/transformers/issues/3670 | 595,644,434 | MDU6SXNzdWU1OTU2NDQ0MzQ= | 3,670 | Has anyone used the run_language_modeling.py to train a gpt2 in a different language? is it possible? | {
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"Hi! Maybe you can have a look at to the issue #1560 . ",
"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"
] | 1,586 | 1,591 | 1,591 | NONE | null | # β Questions & Help
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3669?src=pr&el=h1) Report\n> Merging [#3669](https://codecov.io/gh/huggingface/transformers/pull/3669?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/0a9d09b42a9c7c1ccc00da48486a1188078e8594&el=desc) will **increase... | 1,586 | 1,586 | 1,586 | MEMBER | null | {
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"Hi @Colanim,\r\n\r\nThat's indeed a very good question! The only reason why we add these hacks here is because that's the way Fairseq implemented it and you get better results on summarization using Bart this way. We measured the differences in performance when leaving out those \"force token\" hacks and it was qu... | 1,586 | 1,635 | 1,586 | CONTRIBUTOR | null | # β Questions & Help
In the generation method, the method `prepare_scores_for_generation` is called :
https://github.com/huggingface/transformers/blob/0a9d09b42a9c7c1ccc00da48486a1188078e8594/src/transformers/modeling_utils.py#L1208
And in this method, if it's the first decoding step, BOS token is forced :
http... | {
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"CTRL is a very large model so generating in bulk would require a lot of RAM. \r\nAlso generating with padded batches is not really supported yet, see: https://github.com/huggingface/transformers/issues/3021",
"This issue has been automatically marked as stale because it has not had recent activity. It will be cl... | 1,586 | 1,591 | 1,591 | NONE | null | # β Questions & Help
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"Couldn't we achieve the same result by specifying `add_special_tokens=False` instead of that? This isn't robust to different models, as GPT-2 (which doesn't have special tokens) would get some tokens removed.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be ... | 1,586 | 1,594 | 1,594 | CONTRIBUTOR | null | The way the texts is being split up into blocks right now
```
tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))
for i in range(0, len(tokenized_text) - block_size + 1, block_size): # Truncate in block of block_size
self.examples.append(tokenizer.build_inputs_with_special_tokens(tokeniz... | {
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"do you solve it ?",
"> do you solve it ?\r\n\r\nNot yet. Let me know if you are able to find the fix.",
"yes, I have the same issue. I inspected the tensorboard graph and there is no config operation or anything of that sort. I also tried to save it with a manually defined signature, which didn't work either.\... | 1,586 | 1,602 | 1,591 | NONE | null | # π Bug
I am getting an error while trying to serialize/save TF2.0 RobertaSequenceClassification Keras model to saved model format. I do not see this issue with Bert or Albert model architecture. Please see below for my test script that can be used to reproduce this issue.
## Information
Model I am using (Bert, X... | {
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"Non slow test speed before change:\r\n\r\n",
"There is a tiny TF T5 model now as well via: \r\n`model = TFAutoModelWithLMHead.from_pretrained(\"patrickvonplaten/t5-tiny-random\")`",
"# [Codecov](https:/... | 1,586 | 1,586 | 1,586 | CONTRIBUTOR | null | Speedup torch summarization tests by using small models that are faster to download and instantiate. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3662?src=pr&el=h1) Report\n> Merging [#3662](https://codecov.io/gh/huggingface/transformers/pull/3662?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/261c4ff4e297e919ba993e1214a805e988bc9e79&el=desc) will **decrease... | 1,586 | 1,586 | 1,586 | CONTRIBUTOR | null | {
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"Did you find the solution to the problem?",
"@jhashekhar it seems like pytorch xla has some memory issue itself, xla team is working on it, tf tpu is much better at this moment so i am not using pytorch tpu anymore,probably later this year torch xla team will solve all the performance issues they are having ... | 1,586 | 1,629 | 1,596 | NONE | null | # β Questions & Help
i was using this notebook : https://www.kaggle.com/theoviel/bert-pytorch-huggingface-with-tpu-multiprocessing
to finetune huggingfaceβs xlm roberta base model on jigsaw multilingual (ongoing kaggle competition)
this is my first time with torch xla and TPU multiprocessingβ¦!
the code i a... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3659?src=pr&el=h1) Report\n> Merging [#3659](https://codecov.io/gh/huggingface/transformers/pull/3659?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/39a34cc375ed79d18888464289b83713fc20f7d4&el=desc) will **not chan... | 1,586 | 1,586 | 1,586 | CONTRIBUTOR | null | {
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"You can pass `summarizer(data, min_length=10, max_length=20)` to get a summary whose length is between 10 and 20 tokens. By default, summaries will be between 56 and 142 tokens. ",
"Thanks @sshleifer, interestingly now by having a max_length the summary is just arbitrarily cut, which is not great either. Is ther... | 1,586 | 1,594 | 1,594 | NONE | null | # π Bug
## Information
Summarization task is returning an unexpected results. For an input of
> "We have a telephony partner who is very interested in this program and may be able to help identify pilot customers."
The results is
> [{'summary_text': '"We have a telephony partner who is very interested in... | {
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"[**Model page**](https://huggingface.co/ktrapeznikov/biobert_v1.1_pubmed_squad_v2)"
] | 1,586 | 1,586 | 1,586 | CONTRIBUTOR | null | {
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"docs are pretty nice imho;",
"If I had found what I looking for in the documentation then why would I open an issue and waste someone else's time? I know other approaches to this problem but one seemed to be more time saving so just checking for implementation available. But now it seems that people are more con... | 1,586 | 1,586 | 1,586 | NONE | null | ## Details
Hi, I am working with DistilBERT multilingual model for sequence classification tasks where I need to add some additional languages apart from mentioned [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages). And for that, I am struggling to find the correct way to upd... | {
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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"
] | 1,586 | 1,591 | 1,591 | NONE | null | # β Questions & Help
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
new models and benchmarks, and migration questions. For all other questions,
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Tensorflow enthusiast can help you out. Make s... | {
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https://api.github.com/repos/huggingface/transformers/issues/3649 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3649/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3649/comments | https://api.github.com/repos/huggingface/transformers/issues/3649/events | https://github.com/huggingface/transformers/pull/3649 | 595,029,879 | MDExOlB1bGxSZXF1ZXN0Mzk5NTczMTAx | 3,649 | Add model card for BERTeus | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3649?src=pr&el=h1) Report\n> Merging [#3649](https://codecov.io/gh/huggingface/transformers/pull/3649?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/2ee410560e45ae3c619dc1e0b0fc4d257c48e18a&el=desc) will **increase... | 1,586 | 1,586 | 1,586 | CONTRIBUTOR | null | This PR includes the model card for the BERTeus model which has been recently uploaded to the huggingface repository. | {
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"You might want to check this, It has a very nice example. [https://huggingface.co/transformers/model_doc/bert.html#bertforquestionanswering](https://huggingface.co/transformers/model_doc/bert.html#bertforquestionanswering) ",
"> You might want to check this, It has a very nice example. https://huggingface.co/tra... | 1,586 | 1,592 | 1,592 | NONE | null | Is there a way we can have a chatbot specifically which can answer questions on a given text corpus?
Is there any transformer model which we can train for this and how? | {
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https://api.github.com/repos/huggingface/transformers/issues/3647 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3647/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3647/comments | https://api.github.com/repos/huggingface/transformers/issues/3647/events | https://github.com/huggingface/transformers/issues/3647 | 594,984,123 | MDU6SXNzdWU1OTQ5ODQxMjM= | 3,647 | Bertabs metrics lower than paper | {
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"bump. i have the same question. paper says 5 BeamSize and better accuracy for bert-base. ",
"same here",
"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"
] | 1,586 | 1,598 | 1,598 | NONE | null | I tested abstractive summarization pre-trained model using the source under transformers/examples/summarization/bertabs/...
My dataset are CNN & Daily mail, which are 30 thousands of docs.
However, the result of rouge score is as follows
****** ROUGE SCORES ******
** ROUGE 1
F1 >> 0.275
Precisi... | {
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"I think that's a reasonable feature β Thoughts @LysandreJik @thomwolf?",
"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"
] | 1,586 | 1,591 | 1,591 | CONTRIBUTOR | null | # π Feature request
Current `ForTokenClassification` class implementation for all models does not support regression. I propose to adapt the current implementation for all the `ForTokenClassification` models in order to enable out-of-the-box token regression when `num_labels == 1`, similarly to what is currently av... | {
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https://api.github.com/repos/huggingface/transformers/issues/3645 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3645/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3645/comments | https://api.github.com/repos/huggingface/transformers/issues/3645/events | https://github.com/huggingface/transformers/issues/3645 | 594,933,513 | MDU6SXNzdWU1OTQ5MzM1MTM= | 3,645 | β How to run pipeline (summarization) in FP16 mode ? | {
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"@sshleifer - do you have more info on that? ",
"You can't without editing the code, unfortunately.\r\n",
"Running a pipeline in FP16 mode would be really useful for optimizing the GPU RAM usage. Can this be turned into a feature request?\r\n\r\n**Edit:** I just found out that the following works:\r\n\r\n```\r\... | 1,586 | 1,653 | 1,586 | CONTRIBUTOR | null | # β Questions & Help
I couldn't find on the documentation any parameter that allow running a pipeline in FP16 mode. **Did I miss it or it's not a feature yet ?** | {
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"[run_language_modeling.py](https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py) allow you to track you training performance (validation perplexity, training loss and learning rate) using tensorboad. You need to have tensorboad installed. And then you can just run `tensorboard ... | 1,586 | 1,586 | 1,586 | NONE | null | Hi,
I am new in using Transformer HugginFace library.
I am using [Google Colab](https://colab.research.google.com/github/interactive-fiction-class/interactive-fiction-class.github.io/blob/master/homeworks/language-model/hw4_transformer.ipynb) to fine tune GPT-2 model. Google Colab only displays the output of las... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3643?src=pr&el=h1) Report\n> Merging [#3643](https://codecov.io/gh/huggingface/transformers/pull/3643?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/1789c7daf1b8013006b0aef6cb1b8f80573031c5&el=desc) will **increase... | 1,586 | 1,586 | 1,586 | CONTRIBUTOR | null | This PR includes the model card for Biomed-roberta base, which @kyleclo recently uploaded to allenai's huggingface model repository. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3642?src=pr&el=h1) Report\n> Merging [#3642](https://codecov.io/gh/huggingface/transformers/pull/3642?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/1789c7daf1b8013006b0aef6cb1b8f80573031c5&el=desc) will **increase... | 1,586 | 1,586 | 1,586 | CONTRIBUTOR | null | After #2521 and #2958, this script stopped working. We need to set the bias on the new `decoder` Linear directly.
cc @LysandreJik | {
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"Another comment, I have looked at the cache that the model outputs, and it has a bunch of question marks for all of the cache files(train,dev,test). That makes sense as it is cached, but still I am thinking that I might be doing something wrong. If someone knows where I might have made a mistake, please let me kno... | 1,586 | 1,587 | 1,587 | NONE | null | # π Bug: Can't evaluate official TensorFlow NER model
## Information
Model I am using (Bert, XLNet ...): I am using bert-base-multilingual-cased
Language I am using the model on (English, Chinese ...): German
The problem arises when using:
* [X] the official example scripts: I was using the official scrip... | {
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https://api.github.com/repos/huggingface/transformers/issues/3640 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3640/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3640/comments | https://api.github.com/repos/huggingface/transformers/issues/3640/events | https://github.com/huggingface/transformers/issues/3640 | 594,557,277 | MDU6SXNzdWU1OTQ1NTcyNzc= | 3,640 | Wrong Mask LM prediction with BertForMaskedLM | {
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"This is probably because you're not using special tokens at all. When using BERT you should add the `[CLS]` and `[SEP]` tokens at the appropriate places. Modifying your code to include these generates the correct answer:\r\n\r\n```py\r\n# Tokenized input\r\ntext = \"Who was Jim Henson ? Jim Henson was a puppeteer\... | 1,586 | 1,587 | 1,587 | NONE | null | # π Migration
## Information
<!-- Important information -->
Model I am using (Bert, XLNet ...):
Bert, Electra
Language I am using the model on (English, Chinese ...):
English
The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give ... | {
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https://api.github.com/repos/huggingface/transformers/issues/3639 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3639/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3639/comments | https://api.github.com/repos/huggingface/transformers/issues/3639/events | https://github.com/huggingface/transformers/issues/3639 | 594,543,059 | MDU6SXNzdWU1OTQ1NDMwNTk= | 3,639 | Summarization pipeline - Couldn't reach server at 'https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-large-cnn/modelcard.json' | {
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"@julien-c This is issue has been closed, but I continue to get the same exact error. Can you reopen it? Or do I have to open a new issue?",
"Did you update from master. \r\n\r\nCan you paste the output of `transformers-cli env`",
"I just upgraded to the latest version:\r\n```\r\n- `transformers` version: 2.8.0... | 1,586 | 1,586 | 1,586 | NONE | null | # π Bug
## Information
Loading the summarization pipeline will result in below assertion:
> Couldn't reach server at 'https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-large-cnn/modelcard.json' to download model card file.
> Creating an empty model card.
The problem arises when using:
* ... | {
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https://api.github.com/repos/huggingface/transformers/issues/3638 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3638/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3638/comments | https://api.github.com/repos/huggingface/transformers/issues/3638/events | https://github.com/huggingface/transformers/issues/3638 | 594,479,175 | MDU6SXNzdWU1OTQ0NzkxNzU= | 3,638 | Translation pipeline bug after 398 characters | {
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"Hi @MoritzLaurer, \r\n\r\nI played around with your example a bit and I don't get a good translation either! I think one of the main problems is that T5 was pretrained on a per sentence level - not on whole texts. \r\nTherefore you get quite good results when you split your text into sentences and translate each s... | 1,586 | 1,586 | 1,586 | NONE | null | # π Bug
The translation pipeline with T5 does not seem to allow longer translations than 400~ character. It either automatically stops at 398, or if I play with the min/max_length parameters, it produces gibberish after 400~ characters.
## Information
I am using the translation pipeline (T5)
I tried both:
t... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3637?src=pr&el=h1) Report\n> Merging [#3637](https://codecov.io/gh/huggingface/transformers/pull/3637?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/4ab8ab4f50baf391612cbc78cfa3f09b7ad0c3ac&el=desc) will **increase... | 1,586 | 1,586 | 1,586 | MEMBER | null | Wrong argument order of function. Thanks @dmytyar ! | {
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https://api.github.com/repos/huggingface/transformers/issues/3636 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3636/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3636/comments | https://api.github.com/repos/huggingface/transformers/issues/3636/events | https://github.com/huggingface/transformers/pull/3636 | 594,373,202 | MDExOlB1bGxSZXF1ZXN0Mzk5MDA2Njg5 | 3,636 | [Docs, T5] Fix TF T5 examples docstring | {
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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"
] | 1,586 | 1,591 | 1,591 | NONE | null | Hello,
I have a question regarding re-initialising the encoder layers in BERT. What happens if I call the __init__() method of a BERT layer, is the the layer re-initialised using the pre-trained BERT weights or does it get completely new weights ?
`model.bert.encoder.layer[0].__init__(config)` | {
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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"
] | 1,586 | 1,591 | 1,591 | CONTRIBUTOR | null | Currenty all input is padded to `max_seq_length` but in most cases the longest sequence in a batch is shorter than that, sometimes by a significant amount. If there is a custom collate function that pads only to the longest sequence in a batch, that will probably save quite some memory and time. Is that feasible? | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3632?src=pr&el=h1) Report\n> Merging [#3632](https://codecov.io/gh/huggingface/transformers/pull/3632?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/4ab8ab4f50baf391612cbc78cfa3f09b7ad0c3ac&el=desc) will **increase... | 1,586 | 1,586 | 1,586 | CONTRIBUTOR | null | - Rename generation_mode -> `use_cache`
### Benefits
- Avoid confusion (see linked issues)
- allow unit tests to instantiate once, then test `forward` and `generate`. Avoiding extra 10 second init cost.
- Never accidentally have slow generation
### Costs
- If a developer is changing something and wants to tur... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3631?src=pr&el=h1) Report\n> Merging [#3631](https://codecov.io/gh/huggingface/transformers/pull/3631?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/243e687be6cd701722cce050005a2181e78a08a8&el=desc) will **decrease... | 1,586 | 1,586 | 1,586 | CONTRIBUTOR | null | `convert_examples_to_fes atures` sets `pad_token=0` by default, which is correct for BERT but incorrect for RoBERTa (`pad_token=1`) and XLNet (`pad_token=5`). I think the other arguments to `convert_examples_to_features` are correct, but it might be helpful if someone checked who is more familiar with this part of the ... | {
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"I have written a function to calculate Top-k accuracy and MRR of a model, which is trained by using GPT-2. However, the function gives me very low values of Top-k accuracy and MRR. \r\n\r\nKindly let me know anything wrong in this function?\r\n\r\n```\r\ndef calculateModelPerformance(model,tokenizer):\r\n testD... | 1,586 | 1,591 | 1,591 | NONE | null | # β Questions & Help
Dear All,
I am a newcomer to build language model with GPT-2.
I have [fine-tuned](https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py) a language model by using GPT-2.
Now, I would like to calculate Top-k accuracy and Mean Reciprocal Rank (MRR) of ... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3629?src=pr&el=h1) Report\n> Merging [#3629](https://codecov.io/gh/huggingface/transformers/pull/3629?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/243e687be6cd701722cce050005a2181e78a08a8&el=desc) will **decrease... | 1,586 | 1,586 | 1,586 | CONTRIBUTOR | null | {
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ktrapeznikov/albert-xlarge-v2-squad-v2 | {
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"Facing a similar issue with tf 2.2.0-rc3.\r\n```\r\ndef get_model(lr=0.00001):\r\n inp_bert = tf.keras.layers.Input(shape=(512), dtype=\"int32\")\r\n bert = TFBertModel.from_pretrained('bert-base-multilingual-cased')(inp_bert)[0]\r\n doc_encodings = tf.squeeze(bert[:, 0:1, :], axis=1)\r\n out = tf.kera... | 1,586 | 1,695 | 1,610 | NONE | null | TF version: 2.2.0-rc1
transformers version: 2.7.0
`import tensorflow as tf`
`import transformers`
`print(tf.__version__)`
`print(transformers.__version__)`
`MAX_LEN = 10`
`model_path = 'saved_model/temp_model'`
`ids = tf.keras.layers.Input((MAX_LEN,), dtype=tf.int32)`
`mask = tf.keras.layers.Input((MAX_LEN... | {
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https://api.github.com/repos/huggingface/transformers/issues/3626 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3626/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3626/comments | https://api.github.com/repos/huggingface/transformers/issues/3626/events | https://github.com/huggingface/transformers/issues/3626 | 593,785,903 | MDU6SXNzdWU1OTM3ODU5MDM= | 3,626 | ValueError: You have to specify either input_ids or inputs_embeds! | {
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"Hi @innat, \r\n\r\nT5 is an encoder-decoder model so you will have to provide both `input_ids` and `decoder_input_ids` to the model. Maybe taking a look at the [T5 docs](https://huggingface.co/transformers/model_doc/t5.html#transformers.T5Model.forward) (especially the \"Examples\") can help you :-) \r\n",
"Just... | 1,585 | 1,689 | 1,586 | NONE | null | ## Details
I'm quite new to NLP task. However, I was trying to train the T5-large model and set things as follows. But unfortunately, I've got an error.
```python
def build_model(transformer, max_len=512):
input_word_ids = Input(shape=(max_len,), dtype=tf.int32, name="input_word_ids")
sequence_output =... | {
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https://api.github.com/repos/huggingface/transformers/issues/3625 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3625/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3625/comments | https://api.github.com/repos/huggingface/transformers/issues/3625/events | https://github.com/huggingface/transformers/issues/3625 | 593,771,876 | MDU6SXNzdWU1OTM3NzE4NzY= | 3,625 | how can i run gpt2 model on tf serving ? | {
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"This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md)... | 1,585 | 1,619 | 1,619 | NONE | null | {
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https://api.github.com/repos/huggingface/transformers/issues/3624 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3624/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3624/comments | https://api.github.com/repos/huggingface/transformers/issues/3624/events | https://github.com/huggingface/transformers/issues/3624 | 593,671,282 | MDU6SXNzdWU1OTM2NzEyODI= | 3,624 | Add code to pretrain T5 model from scratch | {
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"@patrickvonplaten Can we pre-train T5 from scratch on any task? I want to use it for Question Answering.",
"Many notebooks for T5 are now added to the community notebooks :-) ",
"@patrickvonplaten can you share the notebook which show T5 pre-training if it is available ?",
"@patrickvonplaten as of today, I d... | 1,585 | 1,624 | 1,591 | NONE | null | # π Feature request
The T5 model can significantly improve NLP task accuracies. However, the existing pretrained models are all in English. I'd like to pretrain T5 model on different language datasets from scratch.
Can you add code on pretraining T5 model? Thanks.
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https://api.github.com/repos/huggingface/transformers/issues/3623 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3623/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3623/comments | https://api.github.com/repos/huggingface/transformers/issues/3623/events | https://github.com/huggingface/transformers/pull/3623 | 593,667,082 | MDExOlB1bGxSZXF1ZXN0Mzk4NDA4MjQw | 3,623 | Create model card | {
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"I forgot to add the language in the header:\r\n```\r\n---\r\nlanguage: english\r\nthumbnail: \r\n---\r\n```",
"Looks quite cool! Also cc'ing @lvwerra\r\n\r\nThanks for sharing π",
"Very nice - never tested it for negative feedback :)"
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"Hi \r\n\r\nthe default value of weight_decay is \"0\" for run_language_modeling.py , why is that ?\r\n\r\nshouldn't it be 0.01 according to original paper of BERT ?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Th... | 1,585 | 1,591 | 1,591 | NONE | null | # β Questions & Help
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
new models and benchmarks, and migration questions. For all other questions,
we direct you to Stack Overflow (SO) where a whole community of PyTorch and
Tensorflow enthusiast can help you out. Make s... | {
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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"
] | 1,585 | 1,594 | 1,594 | CONTRIBUTOR | null | fix the corner case break run_glue.py with QQP task mentioned in #3608 | {
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https://api.github.com/repos/huggingface/transformers/issues/3620 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3620/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3620/comments | https://api.github.com/repos/huggingface/transformers/issues/3620/events | https://github.com/huggingface/transformers/pull/3620 | 593,558,391 | MDExOlB1bGxSZXF1ZXN0Mzk4MzE4NDQ5 | 3,620 | Update notebooks | {
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https://api.github.com/repos/huggingface/transformers/issues/3619 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3619/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3619/comments | https://api.github.com/repos/huggingface/transformers/issues/3619/events | https://github.com/huggingface/transformers/issues/3619 | 593,437,072 | MDU6SXNzdWU1OTM0MzcwNzI= | 3,619 | Feature Request: Fill Mask more than 1 token | {
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"Duplicate of #3609 \r\n\r\nNot currently supported but we welcome a PR"
] | 1,585 | 1,585 | 1,585 | NONE | null | At the moment you can use hugging face's mask filling pipeline to predict 1 masked token in a sentence using the below:
```
!pip install -q transformers
from __future__ import print_function
import ipywidgets as widgets
from transformers import pipeline
nlp_fill = pipeline('fill-mask')
nlp_fill("I am going t... | {
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"@Timoeller @tholor LGTM and thanks for linking to the discussion on the FARM repo. π",
"cherry-picked only the relevant change in 4ab8ab4f50baf391612cbc78cfa3f09b7ad0c3ac"
] | 1,585 | 1,586 | 1,586 | CONTRIBUTOR | null | We changed the vocab to work with run_split_on_punc tokenization.
Now there are much less [UNK] punctuation tokens.
For more details see deepset-ai/FARM/issues/60 | {
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https://api.github.com/repos/huggingface/transformers/issues/3617 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3617/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3617/comments | https://api.github.com/repos/huggingface/transformers/issues/3617/events | https://github.com/huggingface/transformers/issues/3617 | 593,373,968 | MDU6SXNzdWU1OTMzNzM5Njg= | 3,617 | Choosing between adding frequent out of vocab words and doing further pretraining. | {
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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"
] | 1,585 | 1,591 | 1,591 | NONE | null | I have a dutch medical dataset (for Namen Entity Recognition) which contains a lot of domain-specific words. The dutch BERT tokenizer therefor outputs a lot of [UNK] tokens when it tokenizes.
Given that I dispose over a corpus of 60k labelled tokens, and right now I have also a relatively small annotated corpus of 185... | {
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https://api.github.com/repos/huggingface/transformers/issues/3616 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3616/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3616/comments | https://api.github.com/repos/huggingface/transformers/issues/3616/events | https://github.com/huggingface/transformers/issues/3616 | 593,314,716 | MDU6SXNzdWU1OTMzMTQ3MTY= | 3,616 | Out of memory error while training GPT2-large on 8x32GB Nvidia Volta | {
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"Someone's help, please?\r\n\r\nA block_size of 900 works, but I need to use 1024. Is there gradient checkpointing maybe?",
"I managed to train on block_size of 950 using the latest build of pytorch supported by NVIDIA: https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel_20-03.html#rel_20-03... | 1,585 | 1,596 | 1,596 | NONE | null | # π Bug
I'm getting an `out-of-memory error` while trianing `gpt2-large` using `batch_size=1`. I'm using the [examples/run_language_modeling.py](https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py) script. I'm using a custom dataset with varied length examples, maximum `block_s... | {
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https://api.github.com/repos/huggingface/transformers/issues/3615 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3615/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3615/comments | https://api.github.com/repos/huggingface/transformers/issues/3615/events | https://github.com/huggingface/transformers/issues/3615 | 593,303,129 | MDU6SXNzdWU1OTMzMDMxMjk= | 3,615 | Mismatch of loss shape in document with output of TransfoXL | {
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"The output shape looks correct to me \r\n\"loss (:obj:`torch.FloatTensor` of shape `(batch_size, sequence_length)`\" means that \r\n`outputs[0]` should be of shape ` [batch_size, sequence_length] ` and it is ` [1, 2] `, so that is correct no?",
"yes, since the document was updated via #3661"
] | 1,585 | 1,586 | 1,586 | CONTRIBUTOR | null | # π Bug
## Information
Model: Transformer-XL
Language: English
The problem arises when using: a small demo of transformer-xl output
## To reproduce
Steps to reproduce the behavior:
```python3
model_name = 'transfo-xl-wt103'
model = AutoModelWithLMHead.from_pretrained(model_name)
tokenizer = AutoT... | {
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https://api.github.com/repos/huggingface/transformers/issues/3614 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3614/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3614/comments | https://api.github.com/repos/huggingface/transformers/issues/3614/events | https://github.com/huggingface/transformers/issues/3614 | 593,295,506 | MDU6SXNzdWU1OTMyOTU1MDY= | 3,614 | The tensorflow implementation of T5ForConditionalGeneration runs much slower than the pytorch one. GPU utilization is 30% | {
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"I want to take a closer look in a week or so at this. This issue seems to be related: https://github.com/huggingface/transformers/issues/4634",
"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 contributi... | 1,585 | 1,602 | 1,602 | NONE | null | # π Bug
When I am running the official example in examples/summarization/t5/example on PyTorch, I have much better performance than the Tensorflow one. When running on PyTorch it needs 4s per iteration and uses 100% of the GPU. When running the TensorFlow model it needs 30s per iteration and the GPU utilization is 1... | {
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https://api.github.com/repos/huggingface/transformers/issues/3613 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3613/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3613/comments | https://api.github.com/repos/huggingface/transformers/issues/3613/events | https://github.com/huggingface/transformers/pull/3613 | 593,290,070 | MDExOlB1bGxSZXF1ZXN0Mzk4MDk4NzQw | 3,613 | Added albert-base-bahasa-cased README and fixed tiny-bert-bahasa-cased README | {
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