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https://api.github.com/repos/huggingface/transformers/issues/1510 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1510/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1510/comments | https://api.github.com/repos/huggingface/transformers/issues/1510/events | https://github.com/huggingface/transformers/issues/1510 | 506,431,939 | MDU6SXNzdWU1MDY0MzE5Mzk= | 1,510 | CalledProcessError | {
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"Hello, does this still crash if you replace `TRAIN_FILE` with `$TRAIN_FILE` and `TEST_FILE` with `$TEST_FILE` in your command ?",
"@LysandreJik - Yes, it does.\r\n```\r\n10/14/2019 16:55:10 - WARNING - __main__ - Process rank: -1, device: cpu, n_gpu: 0, distributed training: False, 16-bits training: False\r\n1... | 1,571 | 1,600 | 1,589 | NONE | null | I'm running
```
python /content/transformers/examples/run_lm_finetuning.py \
--output_dir=output \
--model_type=gpt2 \
--model_name_or_path=gpt2 \
--do_train \
--train_data_file=TRAIN_FILE \
--do_eval \
--eval_data_file=TEST_FILE
```
in my [Colab notebook](https://colab.research.... | {
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https://api.github.com/repos/huggingface/transformers/issues/1509 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1509/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1509/comments | https://api.github.com/repos/huggingface/transformers/issues/1509/events | https://github.com/huggingface/transformers/pull/1509 | 506,395,240 | MDExOlB1bGxSZXF1ZXN0MzI3NjAyNDQw | 1,509 | remove leftover usage of DUMMY_INPUTS | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1509?src=pr&el=h1) Report\n> Merging [#1509](https://codecov.io/gh/huggingface/transformers/pull/1509?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a701c9b32126f1e6974d9fcb3a5c3700527d8559?src=pr&el=desc) will **d... | 1,571 | 1,571 | 1,571 | CONTRIBUTOR | null | Hey @thomwolf
This change https://github.com/huggingface/transformers/commit/da26bae61b8c1e741fdc6735d46c61b43f649561#diff-8ddce309e88e8eb5b4d02228fd8881daL28 removed the constant `DUMMY_INPUTS`, but one usage of that constant remains in the code.
So any call to `load_tf2_checkpoint_in_pytorch_model` is currently... | {
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https://api.github.com/repos/huggingface/transformers/issues/1508 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1508/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1508/comments | https://api.github.com/repos/huggingface/transformers/issues/1508/events | https://github.com/huggingface/transformers/pull/1508 | 506,324,436 | MDExOlB1bGxSZXF1ZXN0MzI3NTU0NzU5 | 1,508 | Added performance enhancements (XLA, AMP) to examples | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1508?src=pr&el=h1) Report\n> Merging [#1508](https://codecov.io/gh/huggingface/transformers/pull/1508?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a701c9b32126f1e6974d9fcb3a5c3700527d8559?src=pr&el=desc) will **n... | 1,570 | 1,571 | 1,571 | CONTRIBUTOR | null | Summary of changes
- Minor enhancements to `run_tf_glue.py` (e.g. calculate train/val steps from number of train/val examples, standardize quotes etc.)
- Added option for mixed precision (Automatic Mixed Precision / AMP) to run models on Tensor Cores (NVIDIA Volta/Turing GPUs) and future hardware
- Added option fo... | {
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https://api.github.com/repos/huggingface/transformers/issues/1507 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1507/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1507/comments | https://api.github.com/repos/huggingface/transformers/issues/1507/events | https://github.com/huggingface/transformers/issues/1507 | 506,221,632 | MDU6SXNzdWU1MDYyMjE2MzI= | 1,507 | GPU Usage? | {
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"the same here.\r\n\r\ninstalling last apex code from repository: git clone https://github.com/NVIDIA/apex\r\n\r\nit says it's apex-0.1 version, but i think it should say apex-1.0",
"I run into this problem while trying to create virtual GPU devices:\r\n\r\n```python\r\nimport tensorflow as tf\r\nimport transform... | 1,570 | 1,578 | 1,578 | CONTRIBUTOR | null | **Question**
> Note the query/issue might not have anything to do with the library as such, just looking for info as to why it will happen. Thanks for understanding.
- Why would the GPU show a usage[verified using `nvidia-smi`] of 420MB/32GB when i import `transformers`?
Note this only happens when i have `tenso... | {
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https://api.github.com/repos/huggingface/transformers/issues/1506 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1506/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1506/comments | https://api.github.com/repos/huggingface/transformers/issues/1506/events | https://github.com/huggingface/transformers/issues/1506 | 506,182,448 | MDU6SXNzdWU1MDYxODI0NDg= | 1,506 | Seq2Seq model with HugginFace | {
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"Hey @juliahane, glad you’re asking: I am currently working on this (See PR #1455) Stay tuned! Closing this as it is not an issue per se.",
"Hi Remi\nthanks a lot for the great work, since I need it for a deadline approaching\nvery soon, I would really appreciate\nif you may know approximately when could be possi... | 1,570 | 1,584 | 1,572 | NONE | null | Hi
I am looking for a Seq2Seq model which is based on HuggingFace BERT model, I know fairseq has some implementation, but they are generally to me not very clean or easy to use, and I am looking for some good implementation based on HuggingFace work, thanks a lot for your help | {
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https://api.github.com/repos/huggingface/transformers/issues/1505 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1505/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1505/comments | https://api.github.com/repos/huggingface/transformers/issues/1505/events | https://github.com/huggingface/transformers/pull/1505 | 506,173,915 | MDExOlB1bGxSZXF1ZXN0MzI3NDU1NjI3 | 1,505 | Fixed the sample code in the title 'Quick tour'. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1505?src=pr&el=h1) Report\n> Merging [#1505](https://codecov.io/gh/huggingface/transformers/pull/1505?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a701c9b32126f1e6974d9fcb3a5c3700527d8559?src=pr&el=desc) will **n... | 1,570 | 1,571 | 1,571 | CONTRIBUTOR | null | The variable pretrained_weights was fixed to 'bert-base-uncased' to be used in each model to experiment. Otherwise, the last value of this variable in the previous loop was unintentionally effective in this loop which was causing throwing error. | {
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https://api.github.com/repos/huggingface/transformers/issues/1504 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1504/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1504/comments | https://api.github.com/repos/huggingface/transformers/issues/1504/events | https://github.com/huggingface/transformers/issues/1504 | 506,152,032 | MDU6SXNzdWU1MDYxNTIwMzI= | 1,504 | Fine-tuning with run_squad.py, Transformers 2.1.1 & PyTorch 1.3.0 Data Parallel Error | {
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"Runs are in a dedicated environment with only the following packages:\r\n\r\npython 3.7.4\r\npytorch 1.3.0, install includes cudatoolkit 10.1\r\ntensorflow_gpu 2.0 and dependencies\r\napex 0.1\r\ntransformers 2.1.1\r\n\r\nComplete terminal output: \r\n\r\n[output_term_ERROR.TXT](https://github.com/huggingface/tra... | 1,570 | 1,593 | 1,593 | CONTRIBUTOR | null | ## 🐛 Bug
Error message when fine-tuning BERT or XLNet on SQuAD1.1 or 2.0 with dual 1080Ti GPUs:
_"RuntimeError: module must have its parameters and buffers on device cuda:0 (device_ids[0]) but found one of them on device: cuda:1"_
Model I am using: BERT & XLNet
Language I am using the model on: English
... | {
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https://api.github.com/repos/huggingface/transformers/issues/1503 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1503/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1503/comments | https://api.github.com/repos/huggingface/transformers/issues/1503/events | https://github.com/huggingface/transformers/issues/1503 | 506,106,852 | MDU6SXNzdWU1MDYxMDY4NTI= | 1,503 | What is the best way to handle sequences > max_len for tasks like abstract summarization? | {
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"Most people truncate the document at 512 tokens.\r\n\r\nMost of the time it is enough. For example on CNNDM dataset, the lead-3 baseline give a pretty strong score, for simply using the first 3 sentences of the article as summary. \r\n\r\nIt indicates that most salient information are located at the beginning of t... | 1,570 | 1,581 | 1,581 | CONTRIBUTOR | null | What is the best way to handle situations where a sequence in your dataset exceeds the max length defined for a model?
For example, if I'm working on an abstract summarization task with a Bert model having a `max_position_embeddings=512` and tokenizer with `max_len=512`, how should I handle documents where the token... | {
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https://api.github.com/repos/huggingface/transformers/issues/1502 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1502/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1502/comments | https://api.github.com/repos/huggingface/transformers/issues/1502/events | https://github.com/huggingface/transformers/pull/1502 | 506,102,860 | MDExOlB1bGxSZXF1ZXN0MzI3NDA0MTc2 | 1,502 | the working example code to use BertForQuestionAnswering | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1502?src=pr&el=h1) Report\n> Merging [#1502](https://codecov.io/gh/huggingface/transformers/pull/1502?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a701c9b32126f1e6974d9fcb3a5c3700527d8559?src=pr&el=desc) will **i... | 1,570 | 1,571 | 1,571 | CONTRIBUTOR | null | so we can use the pre-trained and fine-tuned on SQUAD Bert model to get an answer from a text and a question, similar to the way the CoreML model BERTSQUADFP16.mlmodel is used in the iOS example [Finding Answers to Questions in a Text Document](https://developer.apple.com/documentation/coreml/finding_answers_to_questio... | {
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https://api.github.com/repos/huggingface/transformers/issues/1501 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1501/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1501/comments | https://api.github.com/repos/huggingface/transformers/issues/1501/events | https://github.com/huggingface/transformers/issues/1501 | 506,063,864 | MDU6SXNzdWU1MDYwNjM4NjQ= | 1,501 | Issue with XLNet pretrained model | {
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"I think you have the wrong Base-Class imported? [Ref](https://github.com/huggingface/transformers/blob/a701c9b32126f1e6974d9fcb3a5c3700527d8559/transformers/modeling_xlnet.py#L959)\r\n```\r\nfrom transformers.modeling_xlnet import XLNetPreTrainedModel\r\nclass XLNetForSequenceClassification(XLNetPreTrainedModel):\... | 1,570 | 1,571 | 1,571 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): XLNet
Language I am using the model on (English, Chinese....): English
The problem arise when using:
* [ ] the official example scripts: (give details)
* [X] my own modified scripts: (give details)
The tasks I am working on is... | {
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https://api.github.com/repos/huggingface/transformers/issues/1500 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1500/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1500/comments | https://api.github.com/repos/huggingface/transformers/issues/1500/events | https://github.com/huggingface/transformers/issues/1500 | 506,037,656 | MDU6SXNzdWU1MDYwMzc2NTY= | 1,500 | How to load a different domain BERT-based pre-trained model? | {
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"You can use torch to load it, convert the weights using the helper files;\r\nNot sure about your task, but for mine, i was using a BertModel with different pre-trained weights,\r\n```\r\nmodel = BertForSequenceClassification(MODEL_PATH, num_labels=len(np.unique(y_train_torch)))\r\n```\r\n(iirc from_tf is also a pa... | 1,570 | 1,576 | 1,576 | NONE | null | I am trying to load the pre-trained model at pred/FinBERT-Pre2K_128MSL-500K [FinBERT](https://github.com/psnonis/FinBERT) and trying to run the basic task of SST-2 (sentiment classification) using run_glue.py (https://huggingface.co/transformers/examples.html#glue).
But I run into the following error:
OSError: Mo... | {
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https://api.github.com/repos/huggingface/transformers/issues/1499 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1499/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1499/comments | https://api.github.com/repos/huggingface/transformers/issues/1499/events | https://github.com/huggingface/transformers/issues/1499 | 505,968,921 | MDU6SXNzdWU1MDU5Njg5MjE= | 1,499 | model.to(args.device) in run_glue.py taking around 10 minutes. Is this normal? | {
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"This seems weird, I'm looking into this.",
"By running the run_glue.py script as it is right now with your exact parameters, I timed to model.to and it took 6.4 seconds",
"Ok, thanks for looking into that! I'm using my own dataset so I made adjustments to the processor, but I don't think that should be causing... | 1,570 | 1,570 | 1,570 | NONE | null | ## ❓ Questions & Help
Currently line 484 of run_glue.py `model.to(args.device)` is taking close to 10 minutes to complete when loading the bert-base pretrained model. This seems like a long time compared to what I was seeing in pytorch-transformers.
My configuration:
Tesla V100 - Driver 418.87.00
Cuda t... | {
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https://api.github.com/repos/huggingface/transformers/issues/1498 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1498/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1498/comments | https://api.github.com/repos/huggingface/transformers/issues/1498/events | https://github.com/huggingface/transformers/pull/1498 | 505,822,308 | MDExOlB1bGxSZXF1ZXN0MzI3MTc1OTU4 | 1,498 | Merge pull request #1 from huggingface/master | {
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"Can you check your workflow to stop opening/closing these PRs?",
"@thomwolf Yeah. I have checked it. It's really embarrased to opening/closing these PRs."
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https://api.github.com/repos/huggingface/transformers/issues/1494 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1494/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1494/comments | https://api.github.com/repos/huggingface/transformers/issues/1494/events | https://github.com/huggingface/transformers/pull/1494 | 505,795,700 | MDExOlB1bGxSZXF1ZXN0MzI3MTUzODI3 | 1,494 | Merge pull request #1 from huggingface/master | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1494?src=pr&el=h1) Report\n> Merging [#1494](https://codecov.io/gh/huggingface/transformers/pull/1494?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/700331b5ece63381ad1b775fc8661cf3ae4493fd?src=pr&el=desc) will **d... | 1,570 | 1,570 | 1,570 | NONE | null | from 1.0->1.1 | {
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https://api.github.com/repos/huggingface/transformers/issues/1493 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1493/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1493/comments | https://api.github.com/repos/huggingface/transformers/issues/1493/events | https://github.com/huggingface/transformers/issues/1493 | 505,792,746 | MDU6SXNzdWU1MDU3OTI3NDY= | 1,493 | FR: Tokenizer function that can handle arbitrary number of sequences | {
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"Looks like a pretty simple and natural extension, what do you think @LysandreJik?",
"It would be easy to implement indeed, do you use this for dialog (because of the alternating token type ids)?",
"Not particularly, but I can certainly imagine that being a useful use-case.\r\n\r\nI am more interested in adding... | 1,570 | 1,576 | 1,576 | NONE | null | ## 🚀 Feature
Currently Tokenizers only support 1 or 2 sequences being added together, and them being concatenated with the appropriate SEP and CLS tokens for each model. My use case requires more sequences being added together, all separated by SEP tokens and having one CLS token at the start (or end for XLNet) of ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1492 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1492/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1492/comments | https://api.github.com/repos/huggingface/transformers/issues/1492/events | https://github.com/huggingface/transformers/pull/1492 | 505,710,597 | MDExOlB1bGxSZXF1ZXN0MzI3MDg1MzY2 | 1,492 | Add new BERT models for German (cased and uncased) | {
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"Great, ok all the models should be public. Merging this now.\r\nAwesome work @stefan-it!"
] | 1,570 | 1,570 | 1,570 | COLLABORATOR | null | Hi,
this PR adds new BERT models for German (both cased and uncased) from @dbmdz.
Details can be found in [this repository](https://github.com/dbmdz/german-bert).
Tasks:
* [x] Models are stored on S3, only permissions need to be adjusted by @julien-c | {
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https://api.github.com/repos/huggingface/transformers/issues/1491 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1491/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1491/comments | https://api.github.com/repos/huggingface/transformers/issues/1491/events | https://github.com/huggingface/transformers/issues/1491 | 505,650,753 | MDU6SXNzdWU1MDU2NTA3NTM= | 1,491 | RuntimeError: unexpected EOF, expected 7491165 more bytes. The file might be corrupted. | {
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"Hi! It seems to me that the file that was downloaded was corrupted, probably because of lacking space or a network error. Could you try using the `from_pretrained` with the `force_download` option ?",
"That worked. Thanks!",
"If you are using Window 10 machine, deleting `vgg16-something` in folder `C:\\Users\\... | 1,570 | 1,692 | 1,571 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I tried a small chunk of code from the Readme.md
```
import torch
from transformers import *
MODELS = [(BertModel, BertTokenizer, 'bert-base-uncased')]
for model_class, tokenizer_class, pretrained_weights in MODELS:... | {
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https://api.github.com/repos/huggingface/transformers/issues/1490 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1490/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1490/comments | https://api.github.com/repos/huggingface/transformers/issues/1490/events | https://github.com/huggingface/transformers/issues/1490 | 505,649,503 | MDU6SXNzdWU1MDU2NDk1MDM= | 1,490 | Is encode_plus supposed to pad to max_length? | {
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"From what I remember (can't check now), padding up to max model seq length is not done and not necessary. The tokenizer will limit longer sequences to the max seq length, but otherwise you can just make sure the batch sizes are equal (so pad up to max _batch_ length, so you can actually create m-dimensional tensor... | 1,570 | 1,571 | 1,571 | NONE | null | ## ❓ Questions & Help
I am using AutoTokenizer and AutoModelForSequenceClassification and `encode_plus` to encode text. I am calling it like this:
`
tokenizer = AutoTokenizer.from_pretrained(self.model_name)
encoded_inputs = tokenizer.encode_plus(text,add_special_tokens=Tru... | {
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https://api.github.com/repos/huggingface/transformers/issues/1489 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1489/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1489/comments | https://api.github.com/repos/huggingface/transformers/issues/1489/events | https://github.com/huggingface/transformers/issues/1489 | 505,581,444 | MDU6SXNzdWU1MDU1ODE0NDQ= | 1,489 | Excessively Long text_b Raises Unnecessary Warnings in `encode_plus` | {
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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"
] | 1,570 | 1,576 | 1,576 | NONE | null | In `encode_plus`, `convert_ids_to_tokens` is called before truncating to `max_len`. However, if either text_a or text_b are longer than `max_len`, `convert_ids_to_tokens` will raise a warning. Since sequences are truncated to the right length afterwards in `encode_plus`, this warning is unnecessary. | {
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https://api.github.com/repos/huggingface/transformers/issues/1488 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1488/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1488/comments | https://api.github.com/repos/huggingface/transformers/issues/1488/events | https://github.com/huggingface/transformers/pull/1488 | 505,561,453 | MDExOlB1bGxSZXF1ZXN0MzI2OTcwNjE5 | 1,488 | GLUE on TPU | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1488?src=pr&el=h1) Report\n> Merging [#1488](https://codecov.io/gh/huggingface/transformers/pull/1488?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f382a8decda82062bb6911f05b646f404eacfdd4?src=pr&el=desc) will **n... | 1,570 | 1,576 | 1,570 | MEMBER | null | This takes advantage of the pytorch 1.3 XLA implementation to fine-tune GLUE on a TPU.
MRPC fine-tuning in 3 epochs + evaluation takes a total of 6 minutes and 30 seconds. | {
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https://api.github.com/repos/huggingface/transformers/issues/1487 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1487/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1487/comments | https://api.github.com/repos/huggingface/transformers/issues/1487/events | https://github.com/huggingface/transformers/pull/1487 | 505,493,229 | MDExOlB1bGxSZXF1ZXN0MzI2OTE1NzYw | 1,487 | convert int to str before adding to a str | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1487?src=pr&el=h1) Report\n> Merging [#1487](https://codecov.io/gh/huggingface/transformers/pull/1487?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6596e3d56626c921b3920e313866b7412633b91a?src=pr&el=desc) will **n... | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/1486 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1486/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1486/comments | https://api.github.com/repos/huggingface/transformers/issues/1486/events | https://github.com/huggingface/transformers/issues/1486 | 505,421,353 | MDU6SXNzdWU1MDU0MjEzNTM= | 1,486 | Can you please share the pre-processed text dump of the bookcorpus and wikipediacorpus? | {
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"Hello @kamalravi \r\n\r\nFor the English Wikipedia data, I followed the scripts in XLM [here](https://github.com/facebookresearch/XLM#train-your-own-monolingual-bert-model). It downloads the latest dump and does the necessary pre-processing.\r\nFor BookCorpus, as you probably know, TBC is not distributed anymore a... | 1,570 | 1,570 | 1,570 | NONE | null | ## ❓ Questions & Help
I am trying to train distilbert with different architecture. If you can share the text dump for the pre-training, it would be great. Thanks!
<!-- A clear and concise description of the question. -->
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https://api.github.com/repos/huggingface/transformers/issues/1485 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1485/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1485/comments | https://api.github.com/repos/huggingface/transformers/issues/1485/events | https://github.com/huggingface/transformers/issues/1485 | 505,337,844 | MDU6SXNzdWU1MDUzMzc4NDQ= | 1,485 | improve final answer extraction in utils_squad.py | {
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"Any update?",
"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"
] | 1,570 | 1,580 | 1,580 | CONTRIBUTOR | null | Shouldn't ` get_final_text` use the specific optionally pre-trained tokenizer instead of generically using `BasicTokenizer` ?
[examples/utils_squad.py L911](https://github.com/huggingface/transformers/blob/6596e3d56626c921b3920e313866b7412633b91a/examples/utils_squad.py#L911) | {
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https://api.github.com/repos/huggingface/transformers/issues/1484 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1484/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1484/comments | https://api.github.com/repos/huggingface/transformers/issues/1484/events | https://github.com/huggingface/transformers/issues/1484 | 505,320,982 | MDU6SXNzdWU1MDUzMjA5ODI= | 1,484 | Error while fine-tuning model for GPT2 | {
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"Hi! It seems like you have taken the example from the latest version but that your library is not up to date. Could you tell me the version of your `transformers` library ?",
"Hi! \r\nI am using the version 2.0.0.",
"If you´re using the version 2.0.0 you should use the [script that was used in this version](ht... | 1,570 | 1,570 | 1,570 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using GPT2:
Language I am using the model on English:
The problem arise when using:
* [ ] the official example scripts: I run the run_lm_finetuning.py script from the examples folder
The tasks I am working on is:
* [ ] my own task or dataset: The Writ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1483 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1483/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1483/comments | https://api.github.com/repos/huggingface/transformers/issues/1483/events | https://github.com/huggingface/transformers/pull/1483 | 505,233,146 | MDExOlB1bGxSZXF1ZXN0MzI2NzA1NTQx | 1,483 | Create new | {
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"Hi @saksham7778. In order to keep the repository clean we would prefer that people open pull requests once a substantial amount of work has been done. Closing for now."
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https://api.github.com/repos/huggingface/transformers/issues/1482 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1482/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1482/comments | https://api.github.com/repos/huggingface/transformers/issues/1482/events | https://github.com/huggingface/transformers/pull/1482 | 505,203,874 | MDExOlB1bGxSZXF1ZXN0MzI2NjgyMDY1 | 1,482 | Integration of TF 2.0 models with other Keras modules | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1482?src=pr&el=h1) Report\n> Merging [#1482](https://codecov.io/gh/huggingface/transformers/pull/1482?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6596e3d56626c921b3920e313866b7412633b91a?src=pr&el=desc) will **i... | 1,570 | 1,578 | 1,570 | MEMBER | null | Add tests that TF 2.0 models can be integrated with other Keras modules.
Add more serialization tests for TF 2.0 and PyTorch models.
Fix TFSequenceSummary head and RoBERTa. | {
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https://api.github.com/repos/huggingface/transformers/issues/1481 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1481/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1481/comments | https://api.github.com/repos/huggingface/transformers/issues/1481/events | https://github.com/huggingface/transformers/issues/1481 | 505,162,649 | MDU6SXNzdWU1MDUxNjI2NDk= | 1,481 | Does run_lm_finetuning.py finetune the entire BERT / Xlnet architecture | {
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"1) Yes, the entire model is fine-tuned.\r\n\r\n2) We follow the fine-tuning that takes place in the BERT paper:\r\n > All of the parameters of BERT and W are fine-tuned jointly to maximize the log-probability of the correct label.",
"Thanks.\n",
"Why would _The correct way to do this is to first finetune th... | 1,570 | 1,576 | 1,576 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
1. When finetuning on data without a task, i.e. **unsupervised finetuning** by running the **run_lm_finetuning.py** script, does the code finetune all the weight layers of the model or just finetunes by adding an extra layer over the to... | {
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https://api.github.com/repos/huggingface/transformers/issues/1480 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1480/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1480/comments | https://api.github.com/repos/huggingface/transformers/issues/1480/events | https://github.com/huggingface/transformers/pull/1480 | 505,110,490 | MDExOlB1bGxSZXF1ZXN0MzI2NjA2Nzcw | 1,480 | Fixing CTRL tokenizer - Update error messages - XLM-MLM in run_generation | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1480?src=pr&el=h1) Report\n> Merging [#1480](https://codecov.io/gh/huggingface/transformers/pull/1480?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/036483fae538faff62f78448b38787f3adb94f97?src=pr&el=desc) will **i... | 1,570 | 1,576 | 1,570 | MEMBER | null | # CTRL tokenizer
We are trying to find a good full-python replacement for the fastBPE tokenizer originally used for CTRL.
We don't really want to depend on fastBPE, even though it's fast, because it's a cython package which means we may then have installation issues on specific platforms like Windows.
Current ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1479 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1479/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1479/comments | https://api.github.com/repos/huggingface/transformers/issues/1479/events | https://github.com/huggingface/transformers/issues/1479 | 505,094,850 | MDU6SXNzdWU1MDUwOTQ4NTA= | 1,479 | How can I get the transformers' parameters? | {
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"The models we use inherit directly from `torch.nn.Module` for our pytorch models and `tf.keras.layers.Layer` for tensorflow modules. You can therefore get the total number of parameters as you would do with any other pytorch/tensorflow modules:\r\n\r\n`sum(p.numel() for p in model.parameters() if p.requires_grad)`... | 1,570 | 1,570 | 1,570 | NONE | null | ## ❓ Questions & Help
Hi, I am new to transformers. Does this library offer an interface to compute the total number of different model's parameters?
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https://api.github.com/repos/huggingface/transformers/issues/1478 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1478/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1478/comments | https://api.github.com/repos/huggingface/transformers/issues/1478/events | https://github.com/huggingface/transformers/issues/1478 | 505,064,651 | MDU6SXNzdWU1MDUwNjQ2NTE= | 1,478 | bert-large-uncased-whole-word-masking-finetuned-squad or BertForQuestionAnswering? | {
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"Hey @jeffxtang in your last line you are asking for 3 outputs, but only index from [:2]. You need to change it to \r\n```\r\nloss, start_scores, end_scores = outputs[:3]\r\n```\r\nThe documentation is off in that example. As for your last question, I don't entirely understand it; however, BertForQuestionAnswering ... | 1,570 | 1,621 | 1,577 | CONTRIBUTOR | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I'm trying to use the pre-trained model bert-large-uncased-whole-word-masking-finetuned-squad to get answer to a question from a text, and I'm able to run:
```
model = BertModel.from_pretrained('bert-large-uncased-whole-word-masking-f... | {
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https://api.github.com/repos/huggingface/transformers/issues/1477 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1477/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1477/comments | https://api.github.com/repos/huggingface/transformers/issues/1477/events | https://github.com/huggingface/transformers/issues/1477 | 505,032,772 | MDU6SXNzdWU1MDUwMzI3NzI= | 1,477 | Much slower for inference, even when traced? | {
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"Can you fix this sentence? It seems some error slipped in there\r\n\r\n> One of the primary differences I can think of is that now I am padding all up to max-seq length, and it does increase performance a lot decrease this.\r\n\r\nAs far as I know, you don't have to pad up to the max sequence length manually, and ... | 1,570 | 1,575 | 1,570 | NONE | null | ## ❓ Questions & Help
When running inference using BERT-large on a T4 GPU using bert-as-a-service, I could get well over 100/s on sentence pair classification. (I am aware that this utilized TF's graph freezing and pruning)
When running inference with Roberta-large on a T4 GPU using native pytorch and fairseq, I ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1476 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1476/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1476/comments | https://api.github.com/repos/huggingface/transformers/issues/1476/events | https://github.com/huggingface/transformers/issues/1476 | 504,985,121 | MDU6SXNzdWU1MDQ5ODUxMjE= | 1,476 | RuntimeError: Error(s) in loading state_dict for BertModel: | {
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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",
"try:\r\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\nmodel_path = None\r\nmodel = my_model.load_state_d... | 1,570 | 1,619 | 1,576 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
Hello, I need to use "py torch_model.bin" in a model. I used your "convert_bert_original_tf_checkpoint_to_pytorch.py" to generate bin file, but I used "model_bert.load_state_dict (torch.load (init_checkpoint, map_location='cpu') to load... | {
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https://api.github.com/repos/huggingface/transformers/issues/1475 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1475/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1475/comments | https://api.github.com/repos/huggingface/transformers/issues/1475/events | https://github.com/huggingface/transformers/issues/1475 | 504,895,655 | MDU6SXNzdWU1MDQ4OTU2NTU= | 1,475 | data loader for varying length input | {
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<!-- A clear and concise description of the question. -->
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https://api.github.com/repos/huggingface/transformers/issues/1474 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1474/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1474/comments | https://api.github.com/repos/huggingface/transformers/issues/1474/events | https://github.com/huggingface/transformers/issues/1474 | 504,861,135 | MDU6SXNzdWU1MDQ4NjExMzU= | 1,474 | 'LayerNorm' object has no attribute 'cls' | {
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"Hi @chiyuzhang94 . What do you get if you remove everything related to `cls` in\r\n\r\n```\r\n pointer = getattr(pointer, 'cls')\r\n pointer = getattr(pointer, 'bias')\r\n elif l[0] == 'output_weights':\r\n pointer = getattr(pointer, 'cls')\r\n ... | 1,570 | 1,577 | 1,577 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I try to use `load_tf_weights_in_bert` to convert my fine-tuned tf classification model in Pytorch. I original trained the model by tensorflow BERT.
I used this code:
```import torch
from transformers.modeling_bert import BertConfig,... | {
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https://api.github.com/repos/huggingface/transformers/issues/1473 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1473/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1473/comments | https://api.github.com/repos/huggingface/transformers/issues/1473/events | https://github.com/huggingface/transformers/issues/1473 | 504,825,783 | MDU6SXNzdWU1MDQ4MjU3ODM= | 1,473 | Bug in CTRL generation | {
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"Yes we have observed there was a difference in tokenization. We've temporarily fixed in 036483f, could you install from source and tell us if you manage to have good generations?\r\n\r\nBy following the recommended specs (temperature=0.2, top_k=5 and repetition_penalty=1.2), with the following input sentence: `Rev... | 1,570 | 1,570 | 1,570 | NONE | null | ## 🐛 Bug
Model: CTRL
Language: English
The problem arises when using:
* [x] Official example script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py)
The tasks I am working on is:
* [x] Generating text with the CTRL model.
## To Reproduce
Step... | {
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https://api.github.com/repos/huggingface/transformers/issues/1472 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1472/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1472/comments | https://api.github.com/repos/huggingface/transformers/issues/1472/events | https://github.com/huggingface/transformers/issues/1472 | 504,788,193 | MDU6SXNzdWU1MDQ3ODgxOTM= | 1,472 | Bug when finetuning model on Squad | {
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"https://github.com/huggingface/transformers/issues/940",
"@ahotrod you have any fix for this bug?",
"> @ahotrod you have any fix for this bug?\r\n\r\n@a-maci no unfortunately not, still searching. I'm considering rolling back to Transformers 2.0.0 or even pytorch-transformers 1.2.0, one or both of which didn'... | 1,570 | 1,591 | 1,579 | NONE | null | ## 🐛 Bug
Model: Bert (bert-large-uncased-whole-word-masking)
The problem arises when using:
The official example script for finetuning on squad data:
```
python -m torch.distributed.launch --nproc_per_node=8 run_squad.py \
--model_type bert \
--model_name_or_path bert-large-uncased-whole-word-ma... | {
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https://api.github.com/repos/huggingface/transformers/issues/1471 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1471/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1471/comments | https://api.github.com/repos/huggingface/transformers/issues/1471/events | https://github.com/huggingface/transformers/issues/1471 | 504,654,185 | MDU6SXNzdWU1MDQ2NTQxODU= | 1,471 | Write with Transformer: Changing settings on Mobile? | {
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"You're right, the interface isn't well suited to mess with settings on mobile. It's on our roadmap!",
"awesome! I found a dumb workaround; saved a copy of the page, changed the default values, then put it in my dropbox!",
"Haha that’s a great hack!\r\n\r\nClosing this for now, thanks"
] | 1,570 | 1,570 | 1,570 | NONE | null | ## ❓ Questions & Help
It's great to see new features and options, in particular the Max Time option to generate longer outputs. However, none of the Model Settings are available on mobile...?
In order to change the model settings on mobile, I had to download Firefox, mess with the CSS settings in about://config t... | {
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https://api.github.com/repos/huggingface/transformers/issues/1470 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1470/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1470/comments | https://api.github.com/repos/huggingface/transformers/issues/1470/events | https://github.com/huggingface/transformers/issues/1470 | 504,653,823 | MDU6SXNzdWU1MDQ2NTM4MjM= | 1,470 | Plan for Albert? | {
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"Duplicate of #1370"
] | 1,570 | 1,570 | 1,570 | NONE | null | ## 🚀 Feature
<!-- A clear and concise description of the feature proposal. Please provide a link to the paper and code in case they exist. -->
I think Albert is popular enough to not say anything more. The link to the paper is below.
https://arxiv.org/pdf/1909.11942v1.pdf
## Motivation
<!-- Please outline... | {
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https://api.github.com/repos/huggingface/transformers/issues/1469 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1469/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1469/comments | https://api.github.com/repos/huggingface/transformers/issues/1469/events | https://github.com/huggingface/transformers/issues/1469 | 504,593,677 | MDU6SXNzdWU1MDQ1OTM2Nzc= | 1,469 | How much GPU memory is needed to run run_squad.py | {
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"With 4 GB you're bound to issues with a batch size of 12. You could figure out the total memory usage of the model + calculate the memory footprints of tensors to determine the biggest batch size that would fit on your GPU. \r\n\r\nSpecifying a smaller batch size (like 1 or 2) would let you run the script, though.... | 1,570 | 1,570 | 1,570 | NONE | null | ## ❓ Questions & Help
How much GPU memory is needed to run `run_squad.py`, I tried on `GTX 1050ti (4gb)` with the following setting and I am getting out of memory error
```
$ python3 examples/run_squad.py \
--model_type bert \
--model_name_or_path bert-base-cased \
--do_train \
--do_eval \
--do_lo... | {
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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"
] | 1,570 | 1,576 | 1,576 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
The output of BertForNextSentencePrediction are not Interpretable.
What is seq_relationship_score?
Input example and their respective output is defined below.
1:
text = "[CLS] How old are you? [SEP] I am 193 years old [SEP]"
output... | {
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https://api.github.com/repos/huggingface/transformers/issues/1467 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1467/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1467/comments | https://api.github.com/repos/huggingface/transformers/issues/1467/events | https://github.com/huggingface/transformers/pull/1467 | 504,479,890 | MDExOlB1bGxSZXF1ZXN0MzI2MTE4MjMy | 1,467 | Hf master | {
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https://api.github.com/repos/huggingface/transformers/issues/1466 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1466/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1466/comments | https://api.github.com/repos/huggingface/transformers/issues/1466/events | https://github.com/huggingface/transformers/issues/1466 | 504,442,696 | MDU6SXNzdWU1MDQ0NDI2OTY= | 1,466 | RuntimeError: storage has wrong size: expected -1451456236095606723 got 1024 | {
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"Hi, what do you mean by different CPU systems? Do you mean that you tried it with different CPU architectures like ARM/x86? On which CPU did it fail?",
"Thank you so much for your reply sir.\n** Different CPUs means** - normal CPU system it is not working. I tested two more CPU system for inferencing own model. ... | 1,570 | 1,576 | 1,576 | NONE | null | ## 🐛 Bug
## RuntimeError: storage has wrong size: expected -1451456236095606723 got 1024
<!-- Important information -->
Model I am using GPT-2:
Language I am using the model on English:
The problem arise when using:
* [ ] i was trained and build GPT-2 model with my own corpus.
* [ ] when i `*tested in di... | {
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https://api.github.com/repos/huggingface/transformers/issues/1465 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1465/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1465/comments | https://api.github.com/repos/huggingface/transformers/issues/1465/events | https://github.com/huggingface/transformers/issues/1465 | 504,322,157 | MDU6SXNzdWU1MDQzMjIxNTc= | 1,465 | Multilabel Classification with TFBertForSequenceClassification | {
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"Have you figured out what the problem is? I'm facing the same thing....",
"Have you figured out what the problem is? I'm facing the same thing....",
"Hello, did you figure out the solution here?",
"Hey guys, sorry for the late update. Here's my solution: I set a lower learning rate and the problem is fixed. ... | 1,570 | 1,615 | 1,585 | NONE | null | I'm currently trying to train a multi label classifier, but in my trained model I'm get the same output no matter the input that I put in.
I've modified the TFBertForSequenceClassification class to include a sigmoid activation output layer as shown below:
```
class TFBertForMultilabelClassification(TFBertPreTra... | {
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https://api.github.com/repos/huggingface/transformers/issues/1464 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1464/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1464/comments | https://api.github.com/repos/huggingface/transformers/issues/1464/events | https://github.com/huggingface/transformers/issues/1464 | 504,296,407 | MDU6SXNzdWU1MDQyOTY0MDc= | 1,464 | How is it possible to furthur tune gpt-2(or gpt) in a seq2seq manner? | {
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"Hi, this is on our mid-term roadmap (seq2seq models).",
"@Hannabrahman In the original GPT2 paper (section 3.7 Translation) the authors used the format \"english sentence = french sentence\" to produce translations. You can definitely fine tune the model using this format to produce translations using the existi... | 1,570 | 1,687 | 1,576 | NONE | null | Hi,
Can we futhur funetue gpt-2 pretrained model in a sequence 2 sequence manner, where we want to minimize the loss of log p(y|x).
In other words, our dataset has both source and target and we want to generate target given source.
But I want to start from using gpt-2 weights and then tune it.
| {
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https://api.github.com/repos/huggingface/transformers/issues/1463 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1463/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1463/comments | https://api.github.com/repos/huggingface/transformers/issues/1463/events | https://github.com/huggingface/transformers/issues/1463 | 504,290,517 | MDU6SXNzdWU1MDQyOTA1MTc= | 1,463 | bert ids | {
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"Hello, you should take a look at the `encode` and `decode` methods in the [documentation](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.PreTrainedTokenizer.decode).",
"Thank you so much that was very helpful",
"Glad I could help!",
"Hi \r\nI am trying to use the code in this li... | 1,570 | 1,571 | 1,570 | NONE | null | ## ❓ Questions & Help
after I Use the BERT tokenizer to convert the tokens to their index numbers in the BERT vocabulary
input_ids = [tokenizer.convert_tokens_to_ids(x) for x in tokenized_texts]
How I can convert them back to the original sentence???
Thank you in advance | {
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https://api.github.com/repos/huggingface/transformers/issues/1462 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1462/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1462/comments | https://api.github.com/repos/huggingface/transformers/issues/1462/events | https://github.com/huggingface/transformers/issues/1462 | 504,287,293 | MDU6SXNzdWU1MDQyODcyOTM= | 1,462 | Visualizing the Inner Workings of Attention | {
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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"
] | 1,570 | 1,576 | 1,576 | NONE | null | ## ❓ Questions & Help
what should I DO to plot my model by BertViz tool??
i am useing config = BertConfig.from_pretrained(“bert-base-uncased”,output_attentions=True,output_hidden_states=True, num_labels=2)
model = BertForSequenceClassification.from_pretrained(“bert-base-uncased”, config= config)
Thank you in ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1461 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1461/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1461/comments | https://api.github.com/repos/huggingface/transformers/issues/1461/events | https://github.com/huggingface/transformers/issues/1461 | 504,267,669 | MDU6SXNzdWU1MDQyNjc2Njk= | 1,461 | How can I use a TensorFlow 2.0 model for Named-Entity-Recognition (NER)? (using TFBertForTokenClassification ) | {
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"Exactly the same question here! Can someone please provide us with a small tutorial or even some general guidelines?",
"Any response here? I was looking for something similar",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity oc... | 1,570 | 1,576 | 1,576 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
How can I use a TensorFlow 2.0 model for Named-Entity-Recognition (NER)? (using TFBertForTokenClassification ) | {
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https://api.github.com/repos/huggingface/transformers/issues/1460 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1460/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1460/comments | https://api.github.com/repos/huggingface/transformers/issues/1460/events | https://github.com/huggingface/transformers/issues/1460 | 504,240,411 | MDU6SXNzdWU1MDQyNDA0MTE= | 1,460 | `decoder` without bias in BertLMPredictionHead | {
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"Hi, I believe that means that the decoder is a linear layer that has the same weights as the word embedding matrix. However, that embedding matrix does not have a bias, whereas the decoder does have a bias. \r\n\r\nIt is initialized to a vector of zeros here, but it can update its weights during training and has a... | 1,570 | 1,621 | 1,570 | CONTRIBUTOR | null | ## ❓ Questions & Help
What does this comment mean?
https://github.com/huggingface/transformers/blob/80bf868a268fa445926bc93f7fe15960853e828e/transformers/modeling_bert.py#L394-L407 | {
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https://api.github.com/repos/huggingface/transformers/issues/1459 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1459/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1459/comments | https://api.github.com/repos/huggingface/transformers/issues/1459/events | https://github.com/huggingface/transformers/issues/1459 | 504,211,872 | MDU6SXNzdWU1MDQyMTE4NzI= | 1,459 | Imports for Roberta conversion appear to be outdated | {
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"Getting the same error here",
"Will investigate, thanks for reporting. (And Hi, @louismartin :)",
"I had a similar issue trying to load BioBERT and I figured out what was going on in my case, sharing just in case that's what's going on in your case. In my case I converted TF BioBERT checkpoint to pytorch model... | 1,570 | 1,576 | 1,576 | NONE | null | ## 🐛 Bug
<!-- Important information -->
I'm trying to convert a custom Roberta model (from fairseq checkpoints) to a Tensorflow model.
The problem arise when using:
* [x] the official example scripts: (give details)
* [ ] my own modified scripts: (give details)
The tasks I am working on is:
* [ ] an off... | {
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https://api.github.com/repos/huggingface/transformers/issues/1458 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1458/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1458/comments | https://api.github.com/repos/huggingface/transformers/issues/1458/events | https://github.com/huggingface/transformers/issues/1458 | 504,131,414 | MDU6SXNzdWU1MDQxMzE0MTQ= | 1,458 | how to get word embedding vector in GPT-2 | {
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"Hi, indeed GPT-2 has a slightly different implementation than BERT. In order to have access to the embeddings, you would have to do the following:\r\n\r\n```py\r\nfrom transformers import GPT2LMHeadModel\r\n\r\nmodel = GPT2LMHeadModel.from_pretrained('gpt2') # or any other checkpoint\r\nword_embeddings = model.tr... | 1,570 | 1,703 | 1,570 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
How can we get the word embedding vector in gpt-2? I follow the guidance in bert(model.embeddings.word_embeddings.weight). But it shows that ''GPT2LMHeadModel' object has no attribute 'embeddings''.
Please help me with that. Thank ... | {
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"when i use \r\nexport SQUAD_DIR=/path/to/SQUAD \r\npython run_squad.py \\ --model_type bert \\ --model_name_or_path bert-base-cased \\ --do_train \\ --do_eval \\ --do_lower_case \\ --train_file $SQUAD_DIR/train-v1.1.json \\ --predict_file $SQUAD_DIR/dev-v1.1.json \\ --per_gpu_train_batch_size 12 ... | 1,570 | 1,572 | 1,570 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
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https://api.github.com/repos/huggingface/transformers/issues/1456 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1456/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1456/comments | https://api.github.com/repos/huggingface/transformers/issues/1456/events | https://github.com/huggingface/transformers/issues/1456 | 504,101,820 | MDU6SXNzdWU1MDQxMDE4MjA= | 1,456 | questions on checkpoint and 'training_args.bin' in run_lm_finetuning.py | {
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"The model saved can be loaded by using the `model.from_pretrained(directory)` method. The training arguments are saved so that they can be re-used later. You can load them using the `torch.load(directory/training_args.bin)` method.",
"thanks for reply. ",
"This issue has been automatically marked as stale beca... | 1,570 | 1,576 | 1,576 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
2 questions:
1. there is a **checkpoint** save logical, but don't see any logical to load this checkpoint. nothe load method in code
1. there is '**training_args.bin**' has been store with checkpoint together. no load code to suppor... | {
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https://api.github.com/repos/huggingface/transformers/issues/1455 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1455/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1455/comments | https://api.github.com/repos/huggingface/transformers/issues/1455/events | https://github.com/huggingface/transformers/pull/1455 | 504,077,124 | MDExOlB1bGxSZXF1ZXN0MzI1Nzk4NTUw | 1,455 | [WIP] Add PretrainedEncoderDecoder class | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1455?src=pr&el=h1) Report\n> Merging [#1455](https://codecov.io/gh/huggingface/transformers/pull/1455?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/ae1d03fc51bb22ed59517ee6f92c560417fdb049?src=pr&el=desc) will **d... | 1,570 | 1,586 | 1,572 | CONTRIBUTOR | null | In this PR we add the possibility to define encoder-decoder architectures. We:
- Added a `PreTrainedEncoderDecoder` class that can be initialized from pre-trained models;
- Modified the BERT model so it can behave as a decoder;
- Added a `Model2Model`class that simplifies the definition of an encoder-decoder when ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1454 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1454/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1454/comments | https://api.github.com/repos/huggingface/transformers/issues/1454/events | https://github.com/huggingface/transformers/pull/1454 | 504,046,949 | MDExOlB1bGxSZXF1ZXN0MzI1Nzc0MTQ1 | 1,454 | Change tensorboard imports to use built-in tensorboard if available | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1454?src=pr&el=h1) Report\n> Merging [#1454](https://codecov.io/gh/huggingface/transformers/pull/1454?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d688af19e5ce92c1395820a89e3f3b635eacc2ba?src=pr&el=desc) will **n... | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | Related issue: #1427 | {
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https://api.github.com/repos/huggingface/transformers/issues/1453 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1453/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1453/comments | https://api.github.com/repos/huggingface/transformers/issues/1453/events | https://github.com/huggingface/transformers/issues/1453 | 503,983,953 | MDU6SXNzdWU1MDM5ODM5NTM= | 1,453 | DistilBert for Tensorflow doesn't work | {
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"I have been experiencing the same issue #1378.",
"Fixed on master with 23b7138, thanks.\r\nWill be in this week's new release 2.1",
"thanks a lot"
] | 1,570 | 1,570 | 1,570 | NONE | null | Model: TFDistilBertForSequenceClassification
Language: English
Task: multi-label classification
Environment: google colab
When trying to use TF Distil Bert I get the below error after I have loaded the model and try to run model.fit() :
> TypeError: in converted code:
> relative to /usr/local/lib/pytho... | {
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https://api.github.com/repos/huggingface/transformers/issues/1452 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1452/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1452/comments | https://api.github.com/repos/huggingface/transformers/issues/1452/events | https://github.com/huggingface/transformers/issues/1452 | 503,909,252 | MDU6SXNzdWU1MDM5MDkyNTI= | 1,452 | xlm-mlm-100-1280 model is not available for download | {
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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"
] | 1,570 | 1,576 | 1,576 | NONE | null | xlm-mlm-100-1280 model is not available for download, see:
https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-100-1280-tf_model.h5
The model for pytorch is available
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https://api.github.com/repos/huggingface/transformers/issues/1451 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1451/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1451/comments | https://api.github.com/repos/huggingface/transformers/issues/1451/events | https://github.com/huggingface/transformers/issues/1451 | 503,755,079 | MDU6SXNzdWU1MDM3NTUwNzk= | 1,451 | nn.Transformer | {
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"Even though I am in favour of using as many built-ins as possible, I wonder whether it is not too early to do this. You will end up with a lot of pseudo-duplicate code: for those who are on 1.0 (no transformer-like support), 1.1 (only nn.*Attention), and 1.2 (full transformer). I don't know any statistics about pe... | 1,570 | 1,680 | 1,576 | CONTRIBUTOR | null | ## 🚀
Use Pytorch's own attention and transformer modules.
## Motivation
Pytorch now offers modules like [nn.MultiheadAttention](https://pytorch.org/docs/stable/nn.html?highlight=attention#torch.nn.MultiheadAttention) and [nn.Transformer](https://pytorch.org/docs/stable/nn.html#transformer-layers). It would b... | {
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https://api.github.com/repos/huggingface/transformers/issues/1450 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1450/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1450/comments | https://api.github.com/repos/huggingface/transformers/issues/1450/events | https://github.com/huggingface/transformers/issues/1450 | 503,747,817 | MDU6SXNzdWU1MDM3NDc4MTc= | 1,450 | Installation example #2 fails: cannot import name 'glue_compute_metrics' | {
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" @evanweissburg Hi, i got the same error..have you found any solution???\r\n ",
"Hello! I believe you must have sklearn installed in order to pass these tests. Please let me know if it doesn't work while having sklearn installed.",
"yeah got it .... i guess we need to run pip install -r ./examples/requirement... | 1,570 | 1,580 | 1,570 | NONE | null | ## 🐛 Bug
<!-- Important information -->
I am having issues with the official installation procedure, where running `python -m pytest -sv ./examples` fails with an opaque error message (below).
## To Reproduce
Steps to reproduce the behavior:
1. Create virtualenv
2. Install Pytorch (`pip install torch==... | {
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https://api.github.com/repos/huggingface/transformers/issues/1449 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1449/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1449/comments | https://api.github.com/repos/huggingface/transformers/issues/1449/events | https://github.com/huggingface/transformers/issues/1449 | 503,719,492 | MDU6SXNzdWU1MDM3MTk0OTI= | 1,449 | Can't replicate Language Model finetuning | {
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"Hello, this language model fine-tuning was community-maintained and is now deprecated. The example script to fine-tune on language modeling is now `run_lm_finetuning.py`."
] | 1,570 | 1,570 | 1,570 | NONE | null | I cannot replicate BioBERT results by using finetune_on_pregenerated.py with data generated using pregenerate_training_data.py.
I've noticed that the LM code has been removed from the repo in that last couple versions. Does this mean there were known issues with this process?
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1448?src=pr&el=h1) Report\n> Merging [#1448](https://codecov.io/gh/huggingface/transformers/pull/1448?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/8fcc6507ce9d0922ddb60f4a31d4b9a839de1270?src=pr&el=desc) will **n... | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | Here is a first draft to serve as a basis for discussion around contribution guidelines. Please mention anything that seems relevant to you / that you care about. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1447?src=pr&el=h1) Report\n> Merging [#1447](https://codecov.io/gh/huggingface/transformers/pull/1447?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/1615360c71f75da7b8aefd14c5d8a461486f865b?src=pr&el=desc) will **n... | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | This PR adds the list of requirements needed to run the tests to the repo. Makes it easier for newcomers to contribute. | {
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"Hello! You should specify `add_prefix_space=True` in your encode method to obtain that behavior.",
"Thank you! That works!"
] | 1,570 | 1,570 | 1,570 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I use GPT-2 transformers model. Tokenizer.encode(' man') = 805, tokenizer.encode('man') = 805. But when in the sentence(e.g. Tokenizer.encode(' the man is a teacher') = [1169, 582, 318, 257, 4701], here the integer representing 'man' ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1445 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1445/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1445/comments | https://api.github.com/repos/huggingface/transformers/issues/1445/events | https://github.com/huggingface/transformers/issues/1445 | 503,531,754 | MDU6SXNzdWU1MDM1MzE3NTQ= | 1,445 | Performance degradation with new version of this library (inference) | {
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"I'm so sorry, I haven't replaced all occurrences of `pytorch-tranformers` to `tranformers`. That was the source of the problem.",
"Glad to hear that!"
] | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): GPT-2
Language I am using the model on (English, Chinese....): Russian
The problem arise when using:
* [ ] the official example scripts: (give details)
* [ x] my own modified scripts: (give details)
I do inference with a bit m... | {
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"No this means the layer rate is smaller deeper in the network, what is called \"discriminative learning\" in ULMFiT.\r\nCheck our NAACL Tutorial on Transfer Learning for more details, in particular, Hands-on n°5 slide 163 here: https://docs.google.com/presentation/d/1fIhGikFPnb7G5kr58OvYC3GN4io7MznnM0aAgadvJfc/edi... | 1,570 | 1,570 | 1,570 | NONE | null | ## ❓ Questions & Help
I'm trying to finetuning a XLNet using run_glue.py, but i haven't seen any references about **Layer-wise lr decay**, that were commented by the authors in the paper.
- Where can I set this parameter on finetuning optimizer?
- The *linear learning rate decay* commented in the paper is related... | {
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"What GPU do you have?",
"Thanks for your reply and support sir:)\n\nNVIDIA TITAN RTX: 4 × 24 GB GPUs",
"Looks like your batch size may be too big?",
"Thank you so much for your support sir.\r\n\r\nI given batch size = 1. May be the latest branch any issues will be present. I will check out previous master an... | 1,570 | 1,630 | 1,586 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using Bert:
Language I am using the model on English:
The tasks I am working on is:
* [ ] Finetuned bert model with my own dataset.
* [ ] run_lm_finetuning.py
## To Reproduce
Steps to reproduce the behavior:
1. I was followesd this issue https:... | {
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https://api.github.com/repos/huggingface/transformers/issues/1442 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1442/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1442/comments | https://api.github.com/repos/huggingface/transformers/issues/1442/events | https://github.com/huggingface/transformers/issues/1442 | 503,394,451 | MDU6SXNzdWU1MDMzOTQ0NTE= | 1,442 | TFBertForSequenceClassification - Feeding List of InputExamples | {
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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",
"Same problem here. My workaround:\r\n\r\n```\r\ndef my_workaround(data):\r\n '''Takes list of InputFeatures, returns arrays.'''\r\n ... | 1,570 | 1,579 | 1,576 | NONE | null | ## ❓ Questions & Help
I used the "glue_convert_examples_to_features" function on my own InputExamples to get a List of InputFeatures. I want to do a Multi-Label Classification but I can not figure out how i need to feed the List of InputFeatures to the TFBertForSequenceClassification model.
train_dataset = gl... | {
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https://api.github.com/repos/huggingface/transformers/issues/1441 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1441/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1441/comments | https://api.github.com/repos/huggingface/transformers/issues/1441/events | https://github.com/huggingface/transformers/issues/1441 | 503,354,798 | MDU6SXNzdWU1MDMzNTQ3OTg= | 1,441 | TF2 Mixed Precision, XLA, Distribution | {
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"Hi @tlkh, thank you for your work on the benchmarks!\r\n\r\nWe're planning to release some in-depths benchmarks by the end of the week/early next week. We'll add your work to it and we'll notify you once we have set-up an easier way to contribute benchmarks/examples!",
"This is really great @tlkh.\r\nDo you thin... | 1,570 | 1,571 | 1,571 | CONTRIBUTOR | null | ## 🚀 Feature
Hi there, I have benchmarked TF2 with the Transformers library. There are very positive results to be gained from the various TensorFlow 2.0 features:
- Automatic Mixed Precision (AMP)
- XLA compiler
- Distribution strategies (multi-GPU)
Here are the benefits (tested on CoLA, MRPC, SST-2):
-... | {
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https://api.github.com/repos/huggingface/transformers/issues/1440 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1440/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1440/comments | https://api.github.com/repos/huggingface/transformers/issues/1440/events | https://github.com/huggingface/transformers/pull/1440 | 503,352,528 | MDExOlB1bGxSZXF1ZXN0MzI1MjIyMTcy | 1,440 | BLUE 2 | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1440?src=pr&el=h1) Report\n> Merging [#1440](https://codecov.io/gh/huggingface/transformers/pull/1440?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/1615360c71f75da7b8aefd14c5d8a461486f865b?src=pr&el=desc) will **d... | 1,570 | 1,576 | 1,576 | NONE | null | this PR seemed to be out of date due to being late considered (https://github.com/huggingface/transformers/pull/1238).
So I updated the code to be able to merge with the latest version.
In this PR:
- I add BertForMultiLabelClassification, RobertaForTokenClassification, RobertaForMultiLabelClassification.
... | {
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https://api.github.com/repos/huggingface/transformers/issues/1439 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1439/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1439/comments | https://api.github.com/repos/huggingface/transformers/issues/1439/events | https://github.com/huggingface/transformers/issues/1439 | 503,337,064 | MDU6SXNzdWU1MDMzMzcwNjQ= | 1,439 | Input length is not equal to output length? | {
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"Hi @RichardHWD, I'm afraid we'll need a bit more information than what you have given.",
"@LysandreJik Sorry. In your example:\r\n```\r\nimport torch\r\nfrom transformers import *\r\n\r\nmodel_class = BertModel\r\ntokenizer_class = BertTokenizer\r\npretrained_weights = 'bert-base-uncased'\r\n\r\ntokenizer = tok... | 1,570 | 1,570 | 1,570 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
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https://api.github.com/repos/huggingface/transformers/issues/1438 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1438/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1438/comments | https://api.github.com/repos/huggingface/transformers/issues/1438/events | https://github.com/huggingface/transformers/pull/1438 | 503,319,398 | MDExOlB1bGxSZXF1ZXN0MzI1MTk1OTIz | 1,438 | fix pytorch-transformers migration description in README | {
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"Yes! Thanks!",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1438?src=pr&el=h1) Report\n> Merging [#1438](https://codecov.io/gh/huggingface/transformers/pull/1438?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/904158ac4dbce046dd02be8382fdb8e52f0e691c?src=pr... | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/1437 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1437/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1437/comments | https://api.github.com/repos/huggingface/transformers/issues/1437/events | https://github.com/huggingface/transformers/issues/1437 | 503,230,431 | MDU6SXNzdWU1MDMyMzA0MzE= | 1,437 | how to do next word prediction in xlnet? | {
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"Take a look at the example code [here](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py). 1000 lines of text for fine-tuning shouldn't be an issue I think, since you're just fine-tuning. As always, try it out and you'll see.",
"Thank you so much for your reply",
"@BramVanroy B... | 1,570 | 1,583 | 1,570 | NONE | null | ## how to do next word prediction in xlnet?
First of all thanks for **huggingface - transformers**
community.
I am actually beginner for XLnet. I want to do Next word prediction by using XLnet. How can i do this?
and I have my own domain-specific datasets(1000 lines), finetune this dataset in xlnet.
is this dat... | {
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https://api.github.com/repos/huggingface/transformers/issues/1436 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1436/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1436/comments | https://api.github.com/repos/huggingface/transformers/issues/1436/events | https://github.com/huggingface/transformers/issues/1436 | 503,177,358 | MDU6SXNzdWU1MDMxNzczNTg= | 1,436 | Which model should I use for machine translation? | {
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"Hi I recommend using XLM from Facebook for MT currently: https://github.com/facebookresearch/XLM\r\nWe may add some models for MT in the mid-term though.",
"[MASS](https://arxiv.org/pdf/1905.02450.pdf) reports higher BLEU-scores than [XLM](https://arxiv.org/abs/1901.07291) which is good in pretraining an encoder... | 1,570 | 1,589 | 1,589 | NONE | null | ## ❓ Questions & Help
I’m interested in training a model for translating articles from Spanish to English. There is too little information (Tutorials) about MT, should I use BERT, XLM or any other one? Also could you explain how to train the proposed model feeding the data, and output the predicted translation.
An... | {
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https://api.github.com/repos/huggingface/transformers/issues/1435 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1435/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1435/comments | https://api.github.com/repos/huggingface/transformers/issues/1435/events | https://github.com/huggingface/transformers/issues/1435 | 503,147,344 | MDU6SXNzdWU1MDMxNDczNDQ= | 1,435 | GPT2 Tokenizer | {
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"Hi! GPT-2 doesn't use padding tokens in its tokenizer. In order to manage padding, you should use the `attention_mask` detailed in the [documentation](https://huggingface.co/transformers/model_doc/gpt2.html#transformers.GPT2Model).",
"Closing as of now, feel free to reopen if @LysandreJik did not answer your que... | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | I want to know the pad token value for the gpt2 tokenizer. I have checked the [vocab](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json) but couldn't find any.
Thanks,
Suchith | {
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https://api.github.com/repos/huggingface/transformers/issues/1434 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1434/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1434/comments | https://api.github.com/repos/huggingface/transformers/issues/1434/events | https://github.com/huggingface/transformers/pull/1434 | 503,133,516 | MDExOlB1bGxSZXF1ZXN0MzI1MDU1OTYy | 1,434 | Remove unnecessary use of FusedLayerNorm in XLNet | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1434?src=pr&el=h1) Report\n> Merging [#1434](https://codecov.io/gh/huggingface/transformers/pull/1434?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f3e0218fbb6bcc40b40f10089dae8876654edb23?src=pr&el=desc) will **d... | 1,570 | 1,571 | 1,571 | CONTRIBUTOR | null | Fix #1172 for XLNet | {
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https://api.github.com/repos/huggingface/transformers/issues/1433 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1433/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1433/comments | https://api.github.com/repos/huggingface/transformers/issues/1433/events | https://github.com/huggingface/transformers/pull/1433 | 503,131,119 | MDExOlB1bGxSZXF1ZXN0MzI1MDU0NDk0 | 1,433 | Fix some typos in README | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1433?src=pr&el=h1) Report\n> Merging [#1433](https://codecov.io/gh/huggingface/transformers/pull/1433?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f3e0218fbb6bcc40b40f10089dae8876654edb23?src=pr&el=desc) will **n... | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | This PR fixes some typos in README.md and overall makes it slightly more readable.
No code changes. | {
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https://api.github.com/repos/huggingface/transformers/issues/1432 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1432/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1432/comments | https://api.github.com/repos/huggingface/transformers/issues/1432/events | https://github.com/huggingface/transformers/issues/1432 | 503,111,141 | MDU6SXNzdWU1MDMxMTExNDE= | 1,432 | How to return bert self attention, so that i can do visualization?? | {
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"Hi! Could you specify which version of our library you are using? Thank you.",
"Hi\r\nI am useing \"pip install pytorch-pretrained-bert pytorch-nlp\"",
"I believe the way to output attentions in `pytorch-pretrained-BERT` v0.6.2 was to specify the `output_all_encoded_layers` to `True` in the model forward call.... | 1,570 | 1,576 | 1,576 | NONE | null | ## ❓ Questions & Help
model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels= 2, output_attentions=True)
model.cuda()
I am useing the above code to return the attention weights, for visualizing the attention by BertViz.
But it gave me this error ( __init__( ) got an unexpected keyword... | {
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https://api.github.com/repos/huggingface/transformers/issues/1431 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1431/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1431/comments | https://api.github.com/repos/huggingface/transformers/issues/1431/events | https://github.com/huggingface/transformers/issues/1431 | 503,083,514 | MDU6SXNzdWU1MDMwODM1MTQ= | 1,431 | Fine-tune specific layers | {
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"In Pytorch or Tensorflow? If Pytorch, [this issue](https://github.com/huggingface/transformers/issues/400) might be of help.",
"In my scripts, I use the following code. Passing down a parameter 'freeze' (list) to the config that I use. All layers that start with any of the given strings will be frozen. \r\n\r\n`... | 1,570 | 1,570 | 1,570 | NONE | null | Is there any easy way to fine-tune specific layers of the model instead of fine-tuning the complete model?
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https://api.github.com/repos/huggingface/transformers/issues/1430 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1430/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1430/comments | https://api.github.com/repos/huggingface/transformers/issues/1430/events | https://github.com/huggingface/transformers/issues/1430 | 503,078,491 | MDU6SXNzdWU1MDMwNzg0OTE= | 1,430 | AttributeError: 'BertOnlyMLMHead' object has no attribute 'bias' | {
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"> ## ❓ Questions & Help\r\n> I was trying to load a RuBERT model from [DeepPavlov](http://docs.deeppavlov.ai/en/master/features/models/bert.html) but ran into this error. The model is in TensorFlow and the code I used to load it is:\r\n> \r\n> ```\r\n> config = BertConfig.from_json_file('rubert_cased_L-12_H-768_A-... | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I was trying to load a RuBERT model from [DeepPavlov](http://docs.deeppavlov.ai/en/master/features/models/bert.html) but ran into this error. The model is in TensorFlow and the code I used to load it is:
```
config = BertConfig.from... | {
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https://api.github.com/repos/huggingface/transformers/issues/1429 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1429/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1429/comments | https://api.github.com/repos/huggingface/transformers/issues/1429/events | https://github.com/huggingface/transformers/pull/1429 | 503,061,518 | MDExOlB1bGxSZXF1ZXN0MzI1MDA3MTQ5 | 1,429 | Checkpoint rotation | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1429?src=pr&el=h1) Report\n> Merging [#1429](https://codecov.io/gh/huggingface/transformers/pull/1429?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/8fcc6507ce9d0922ddb60f4a31d4b9a839de1270?src=pr&el=desc) will **n... | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | By default, no change in existing behavior. However, if you pass in an argument with `save_total_limit` flag and a natural number as value, then, your machine might not run out of space when fine-tuning. Because, it will only keep the latest `save_total_limit` number of checkpoints and delete the older checkpoints. | {
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https://api.github.com/repos/huggingface/transformers/issues/1428 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1428/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1428/comments | https://api.github.com/repos/huggingface/transformers/issues/1428/events | https://github.com/huggingface/transformers/issues/1428 | 503,029,365 | MDU6SXNzdWU1MDMwMjkzNjU= | 1,428 | Problem with word prediction with GPT2 | {
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"Hi, indeed it should be the other way around! I believe it's due to a misconception, you're initializing your model as follows:\r\n```py\r\nconfig = GPT2Config.from_pretrained('gpt2-medium')\r\nmodel = GPT2LMHeadModel(config)\r\n```\r\nHowever, as noted in the [documentation](https://huggingface.co/transformers/ma... | 1,570 | 1,570 | 1,570 | NONE | null | ## ❓ Questions & Help
I'm trying to understand how to obtain the probability of specific word predictions, but I am getting bad results. For example, according to the code below, the sequence "It seems that" is more likely followed by "ago" than by "we", which surely is not correct. What am I doing wrong?
```impor... | {
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https://api.github.com/repos/huggingface/transformers/issues/1427 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1427/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1427/comments | https://api.github.com/repos/huggingface/transformers/issues/1427/events | https://github.com/huggingface/transformers/issues/1427 | 503,000,236 | MDU6SXNzdWU1MDMwMDAyMzY= | 1,427 | Replace TensorboardX with Pytorch's built in SummaryWriter | {
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"You cannot assume that suddenly _everyone_ is on 1.2. You'll need a fallback for people who are 1.x. Something like\r\n\r\n```python\r\ntry:\r\n from torch.utils.tensorboard import SummaryWriter\r\nexcept AttributeError\r\n from tensorboardX import SummaryWriter\r\n```\r\n\r\nThat's a good way to 'ease into'... | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | ## 🚀 Feature
Import `SummaryWriter` from `from torch.utils.tensorboard` instead of from `tensorboardX`
If you're interested, I can make a pull request to merge the changes that I made in my [fork](https://github.com/bkkaggle/transformers) into the main repository.
## Motivation
TensorboardX isn't needed an... | {
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https://api.github.com/repos/huggingface/transformers/issues/1426 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1426/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1426/comments | https://api.github.com/repos/huggingface/transformers/issues/1426/events | https://github.com/huggingface/transformers/issues/1426 | 502,966,278 | MDU6SXNzdWU1MDI5NjYyNzg= | 1,426 | GPU Benchmarking + Accumulated Optimizer for TF2 | {
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"I would also like to see benchmarks, however this is a computationally heavy task. It might be useful to provide a benchmark script and a benchmark table. Contributors can then run the script on their available hardware, and add their results to the table - highlighting the used parameters and hardware.",
"Here'... | 1,570 | 1,576 | 1,576 | NONE | null | ## 🚀 Feature
- Create a GPU benchmarking section in Documentation (Wiki).
- Build and Include TF2 Optimizer with gradient accumulation.
```python
optimizer=AccumulatedOptimizer(Adam(lr=2e-5, clipnorm=1.0), accumulate_steps=4)
```
## Motivation
I experiment with transformers library on Tensorflow 2 for a... | {
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https://api.github.com/repos/huggingface/transformers/issues/1425 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1425/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1425/comments | https://api.github.com/repos/huggingface/transformers/issues/1425/events | https://github.com/huggingface/transformers/issues/1425 | 502,827,796 | MDU6SXNzdWU1MDI4Mjc3OTY= | 1,425 | ELECTRA Model | {
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"Hi @josecannete \r\n\r\nThanks for the tip! We are busy building other awesome things at the moment, but feel free to start a PR with a first draft and we will be happy to have a look at it 😄 ",
"And note that it's probably better to wait for the author's original code and pretrained weights.",
"This issue ha... | 1,570 | 1,618 | 1,587 | NONE | null | ## 🚀 Feature
New Transformer based model: ELECTRA
## Motivation
Hi guys, did you see the following paper: https://openreview.net/forum?id=r1xMH1BtvB ? There is a new Transformer based model called ELECTRA that seems very interesting and promising. It would be very useful to have a implementation of the model ... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1424?src=pr&el=h1) Report\n> Merging [#1424](https://codecov.io/gh/huggingface/transformers/pull/1424?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b3cfd979460d6ff828741eddffc72c34417b5046?src=pr&el=desc) will **d... | 1,570 | 1,651 | 1,580 | MEMBER | null | **_Disclaimer: This pull request is under active development and is being improved daily._**
This pull request aims to train a BERT model on GLUE, using a TPU. Several approaches are tested: keras' fit method (doesn't work yet), and a custom training loop using TPUStrategy.
The custom training loop currently work... | {
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https://api.github.com/repos/huggingface/transformers/issues/1423 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1423/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1423/comments | https://api.github.com/repos/huggingface/transformers/issues/1423/events | https://github.com/huggingface/transformers/issues/1423 | 502,720,736 | MDU6SXNzdWU1MDI3MjA3MzY= | 1,423 | Problem loading trained keras model | {
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"It seems to me like your file is corrupted 😕",
"You can refresh the file in the cache with the `force_download` option (`model.from_pretrained(shortcut_name, force_download=True)`)",
"I think this worked @thomwolf, thanks!",
"Bringing this back up because it seems like the corrupted file actually happens ev... | 1,570 | 1,581 | 1,581 | NONE | null | I'm running the following line of code:
```
model = TFBertForSequenceClassification.from_pretrained(model_dir, num_labels=len(labels))
```
where model_dir is a directory containing a tf_model.h5 and a config.json file that was exported using the .save_pretrained() method.
However I get the following error shown ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1422 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1422/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1422/comments | https://api.github.com/repos/huggingface/transformers/issues/1422/events | https://github.com/huggingface/transformers/issues/1422 | 502,343,910 | MDU6SXNzdWU1MDIzNDM5MTA= | 1,422 | Option to upload a trained model from gpt-2-simple to use with Write With Transformer | {
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"That's on the long term horizon, but that'd be a cool feature, indeed. We are working on a way to let users of `🤗/transformers` upload their weights to share them with the community super easily.\r\n\r\nOnce we ship this, it would be doable to also host some of those on Write With Transformer. (with some *interes... | 1,570 | 1,578 | 1,578 | NONE | null | ## 🚀 Feature
I would like to be able to upload model checkpoints created in GPT-2-simple to use with Write with Transformer.
## Motivation
It would be really fun and allow people to use their own checkpoints without having to get them approved or anything or make them public.
## Additional context
none | {
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https://api.github.com/repos/huggingface/transformers/issues/1421 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1421/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1421/comments | https://api.github.com/repos/huggingface/transformers/issues/1421/events | https://github.com/huggingface/transformers/pull/1421 | 502,328,383 | MDExOlB1bGxSZXF1ZXN0MzI0NDMzOTky | 1,421 | Rbert - follow-up to #1301 - more robust configuration class loading | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1421?src=pr&el=h1) Report\n> :exclamation: No coverage uploaded for pull request base (`master@ecc4f1b`). [Click here to learn what that means](https://docs.codecov.io/docs/error-reference#section-missing-base-commit).\n> The diff coverage is `97.95%`... | 1,570 | 1,651 | 1,583 | MEMBER | null | This PR update #1301 as discussed in the thread of #1308.
The configuration classes are updated to be more robust to the addition of new parameters (load defaults value first and then update with pretrained configuration if needed).
This incorporates the entity token ids directly in `BertConfig`.
cc @RichJacks... | {
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https://api.github.com/repos/huggingface/transformers/issues/1420 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1420/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1420/comments | https://api.github.com/repos/huggingface/transformers/issues/1420/events | https://github.com/huggingface/transformers/issues/1420 | 502,239,149 | MDU6SXNzdWU1MDIyMzkxNDk= | 1,420 | ALBERT Model Incoming? | {
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"Duplicate of #1370"
] | 1,570 | 1,570 | 1,570 | NONE | null | ALBERT: https://arxiv.org/abs/1909.11942v1 was just released. Are there plans to implement this in Transformers? | {
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https://api.github.com/repos/huggingface/transformers/issues/1419 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1419/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1419/comments | https://api.github.com/repos/huggingface/transformers/issues/1419/events | https://github.com/huggingface/transformers/issues/1419 | 502,213,491 | MDU6SXNzdWU1MDIyMTM0OTE= | 1,419 | question for one parameter matrix in transformers/GPT2 | {
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"I dont think something like that is there, please have a look. Or paste everything from h.0 as it is here. Then it will be easy.",
"> I dont think something like that is there, please have a look. Or paste everything from h.0 as it is here. Then it will be easy.\r\n\r\nHi, thank you for your reply. Here's the pa... | 1,570 | 1,570 | 1,570 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
In transformers/gpt-2 model, there's a weight matrix called "transformer.h.0.attn.bias" whose size is torch.Size([1, 1, 1024, 1024]). I checked the original paper but still get confused by what it is for?
This parameter matrix is be... | {
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https://api.github.com/repos/huggingface/transformers/issues/1418 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1418/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1418/comments | https://api.github.com/repos/huggingface/transformers/issues/1418/events | https://github.com/huggingface/transformers/pull/1418 | 502,170,195 | MDExOlB1bGxSZXF1ZXN0MzI0MzAzMDIw | 1,418 | DistillBert Documentation Code Example fixes | {
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"Indeed, thanks for the PR @drc10723 !"
] | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | Following code examples in the documentation are throwing errors:-
1. [DistilBertForQuestionAnswering](https://huggingface.co/transformers/model_doc/distilbert.html#transformers.DistilBertForQuestionAnswering)
```
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertForQu... | {
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https://api.github.com/repos/huggingface/transformers/issues/1417 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1417/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1417/comments | https://api.github.com/repos/huggingface/transformers/issues/1417/events | https://github.com/huggingface/transformers/issues/1417 | 502,137,187 | MDU6SXNzdWU1MDIxMzcxODc= | 1,417 | How to replicate Arxiv-NLP but for different subject? | {
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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"
] | 1,570 | 1,576 | 1,576 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
Hi, I'm fairly new to NLP so apologies for my ignorance on some things.
If I wanted to fine tune text generation on a subject matter ( like Harry Potter), how would I do that?
Im looking to use XLNET and it seems like there isn'... | {
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https://api.github.com/repos/huggingface/transformers/issues/1416 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1416/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1416/comments | https://api.github.com/repos/huggingface/transformers/issues/1416/events | https://github.com/huggingface/transformers/issues/1416 | 502,132,497 | MDU6SXNzdWU1MDIxMzI0OTc= | 1,416 | How to install transformers with pytorch only? | {
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"Hi! No, you should be able to import every torch-related model without having TensorFlow installed. \r\n\r\nAs I understand it, our method for identifying if you had TensorFlow 2.0 installed broke because the TensorFlow version you have in your environment does not have the attribute `__version__`.\r\n\r\nCould yo... | 1,570 | 1,570 | 1,570 | NONE | null | ## ❓ Questions & Help
Hi! Pytorch1.0 is installed and I'm installing the transformers with pip, everything is fine. But when I try:
```
import torch
from transformers import BertModel
```
then, an error occurred:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/pc/min... | {
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https://api.github.com/repos/huggingface/transformers/issues/1415 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1415/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1415/comments | https://api.github.com/repos/huggingface/transformers/issues/1415/events | https://github.com/huggingface/transformers/issues/1415 | 502,124,904 | MDU6SXNzdWU1MDIxMjQ5MDQ= | 1,415 | run_glue.py - Import Error | {
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"Hi! You may have seen this warning when importing from our library: `To use data.metrics please install scikit-learn. See https://scikit-learn.org/stable/index.html`.\r\n\r\nDo you have this issue even with scikit-learn installed?",
"Hi @LysandreJik ,\r\n\r\nThanks for the answer. I already have scikit-learn but... | 1,570 | 1,598 | 1,570 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): XLNET (from example/run_glue.py)
The problem arise when using:
* [ ] the official example scripts: run_glue.py
- Stacktrace:
Traceback (most recent call last):
File "run_glue.py", line 49, in <module>
from transformers impo... | {
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https://api.github.com/repos/huggingface/transformers/issues/1414 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1414/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1414/comments | https://api.github.com/repos/huggingface/transformers/issues/1414/events | https://github.com/huggingface/transformers/issues/1414 | 502,063,950 | MDU6SXNzdWU1MDIwNjM5NTA= | 1,414 | Instruction for Using XLM Text Generations | {
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"I am working on similar situation. If anyone solves this problem please help me",
"It is really hard issue in my project. I can’t find anything as helpful about it and I really need this. I spent on this problem hours and hours. I found several resources but they didn’t have enough information. Please help us th... | 1,570 | 1,577 | 1,576 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
Hi, I've reviewed every file in the document. I couldn't find an instruction to use **XLM** for text generation. What I really want to do is use a pre-trained **XLM** model for **English** text generation and examine the results. Then I... | {
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https://api.github.com/repos/huggingface/transformers/issues/1413 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1413/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1413/comments | https://api.github.com/repos/huggingface/transformers/issues/1413/events | https://github.com/huggingface/transformers/issues/1413 | 502,049,608 | MDU6SXNzdWU1MDIwNDk2MDg= | 1,413 | Adding New Vocabulary Tokens to the Models | {
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"Hi, I believe this method does exactly what you're looking for: [add_tokens](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.PreTrainedTokenizer.add_tokens). There's an example right below it.",
"thanks @LysandreJik ! yes, that's exactly what I was looking for. A follow-up question: ... | 1,570 | 1,708 | 1,570 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
Hi,
How could I extend the vocabulary of the pre-trained models, e.g. by adding new tokens to the lookup table?
Any examples demonstrating this? | {
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https://api.github.com/repos/huggingface/transformers/issues/1412 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1412/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1412/comments | https://api.github.com/repos/huggingface/transformers/issues/1412/events | https://github.com/huggingface/transformers/issues/1412 | 501,955,005 | MDU6SXNzdWU1MDE5NTUwMDU= | 1,412 | How to use model.fit in GPT2 TF Model | {
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"Hello! Are you sure this is the script with which you get your error? The `model.fit` argument `epoch` doesn't exist (it should be `epochs`) and your model has not been compiled beforehand. Could you provide an example script which throws the error you're mentioning?",
"Hi , I tried to minimize the code as much ... | 1,570 | 1,605 | 1,575 | NONE | null | ## ❓ Questions & Help
```import tensorflow as tf
from transformers import *
import numpy as np
# Load dataset, tokenizer, model from pretrained model/vocabulary
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = TFGPT2LMHeadModel.from_pretrainedmodel = TFGPT2LMHeadModel.from_pretrained('gpt2')
np.rand... | {
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"Great thanks!"
] | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | add DistilBert model shortcut name into ALL_MODELS | {
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