repo stringclasses 1
value | number int64 1 25.3k | state stringclasses 2
values | title stringlengths 1 487 | body stringlengths 0 234k ⌀ | created_at stringlengths 19 19 | closed_at stringlengths 19 19 | comments stringlengths 0 293k |
|---|---|---|---|---|---|---|---|
transformers | 86 | closed | code in run_squad.py line 263 | # Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
in segment_ids array,1 indicates token from passage and 0 indicate token form query.
when padding,why segment_ids filled with 0,which represents que... | 12-04-2018 11:08:09 | 12-04-2018 11:08:09 | 
<|||||>Hi, what is your question?<|||||>Strictly speaking, the zero-padding in segment_ids leads to ambiguous tensor entries, because 0 can mean both "first sentence" (or query in another task?) and "padding"... |
transformers | 85 | closed | How to use pre-trained SQUAD model? | After training squad, I have a model file in a local folder:
```
-rw-rw-r-- 1 khashab2 cs_danr 4.7M Nov 21 19:20 dev-v1.1.json
-rw-rw-r-- 1 khashab2 cs_danr 3.4K Nov 29 22:52 evaluate-v1.1.py
drwxrwsr-x 2 khashab2 cs_danr 10 Nov 30 14:57 out2
-rw-rw-r-- 1 khashab2 cs_danr 29M Nov 21 19:20 train-v1.1.json... | 12-04-2018 03:13:30 | 12-04-2018 03:13:30 | Hi there are now examples on how you can save and reload the models in the examples (`run_classifier`, `run_squad` and `run_swag`) |
transformers | 84 | closed | elementwise_mean -> mean (thinking ahead to pytorch 1.0) | under the pytorch 1.0 nightly this test generates
```
UserWarning: reduction='elementwise_mean' is deprecated, please use reduction='mean' instead.
```
so this PR fixes that. | 12-03-2018 23:59:40 | 12-03-2018 23:59:40 | oops, doesn't work under current pytorch, never mind |
transformers | 83 | closed | Error while runing example | Hi!
I have a problem when running the example, could you please give me a hint on what may I be doing wrong?
I use:
`PYTHONPATH=. python examples/run_classifier.py --task_name MNLI --do_train --do_eval --do_lower_case --data_dir ../GLUE-baselines/glue_data/MNLI/ --bert_model bert-base-uncased --max_seq_len 40 -... | 12-03-2018 20:21:12 | 12-03-2018 20:21:12 | Hi!
In case you haven't already, modifying the source at https://github.com/huggingface/pytorch-pretrained-BERT/blob/e60e8a606837ff7f49e583de8492e55575155eb6/examples/run_classifier.py#L491 and turning it into
`cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank), num_labels = 3)`
... |
transformers | 82 | closed | AttributeError: 'tuple' object has no attribute 'backward' | Traceback (most recent call last): | 0/11 [00:00<?, ?it/s]
File "examples/run_classifier.py", line 637, in <module>
main()
File "examples/run_classifier.py", line 558, in main
... | 12-03-2018 16:06:20 | 12-03-2018 16:06:20 | Looks like there was a code change which changed the forward method of the model involved here from returning a tensor to returning a tuple of tensors and the example hasn't been updated yet to reflect that change. There's probably a line in run_classifier.py like
```Python
loss = model(input...)
```
which now need... |
transformers | 81 | closed | There is some problem in supporting continuously training | I change the run_classfifier.py in order to support continuously training. i save the model.state_dict() and the BertAdam optimizer.state_dict(), and I load them when start continuously training. However, After some epochs, the loss will increase little by little and finally end with a large loss value. I do not know t... | 12-03-2018 12:00:09 | 12-03-2018 12:00:09 | Hi @ZacharyWaseda, continuous training is an open-research problem. You should rather seek some solution in the papers/workshop/conference discussing researches in this field. This is not my personal field of expertise so I can only direct you to google and other search engine for more information. |
transformers | 80 | closed | How can I apply BERT to a cloze task? | Hi, I have a dataset like :
From Monday to Friday most people are busy working or studying, but in the evenings and weekends they are free and _ themselves.
And there are four candidates for the missing blank area:
["love", "work", "enjoy", "play"], here "enjoy" is the correct answer, it is a cloze-style t... | 12-03-2018 10:58:43 | 12-03-2018 10:58:43 | I think that you best option would be to use the masked language modeling head and restrict the output of the softmax layer to your candidates.
I think the following code does the job:
```
import torch
from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM
tokenizer = BertTokenizer.fro... |
transformers | 79 | closed | numpy.core._internal.AxisError: axis 1 is out of bounds for array of dimension 1 | hello, when I am running run_classifier.py with MRPC dataset, there seems to be an mistake. the mistake is as following:
<img width="752" alt="default" src="https://user-images.githubusercontent.com/29532760/49360256-9de0e100-f713-11e8-9a5c-d9f2bc5331e6.PNG">
the mistake is happening when training is over and the mod... | 12-03-2018 07:56:56 | 12-03-2018 07:56:56 | Hi, just update the repo to the current master, this should have been fixed this weekend (re-open the issue of it's not). |
transformers | 78 | closed | TypeError: object of type 'WindowsPath' has no len() | Hi, when I run "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')", the error "TypeError: object of type 'WindowsPath' has no len()" occurs, what is the problem? Thank you for your excellent code! | 12-02-2018 12:03:51 | 12-02-2018 12:03:51 | Can you post a more detailed log?<|||||>I install your PyTorch pretrained bert with pip like "pip install pytorch-pretrained-bert", then I run the code in Usage section like:
`import torch`
`from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM`
`# Load pre-trained model tokenizer (vocabu... |
transformers | 77 | closed | Correct assignement for logits in classifier example | I tried to address https://github.com/huggingface/pytorch-pretrained-BERT/issues/76
should be correct, but there's likely a more efficient way. | 12-02-2018 11:38:51 | 12-02-2018 11:38:51 | Ok thanks, that should work for now. I simplified the output of the classes indeed (only send back loss when a label is provided) so this example broke. |
transformers | 76 | closed | Wrong signature in model call in run_classifier.py example (?) | I think that
https://github.com/huggingface/pytorch-pretrained-BERT/blob/063be09b714bf4d2fbbc3de7f52c45b8bc6817eb/examples/run_classifier.py#L608
may well have a problem, as it's not consistent with
https://github.com/huggingface/pytorch-pretrained-BERT/blob/063be09b714bf4d2fbbc3de7f52c45b8bc6817eb/examples/run_cl... | 12-01-2018 19:34:40 | 12-01-2018 19:34:40 | You are right, I also encountered this small error.<|||||>Thanks for noticing, fixed in #77. |
transformers | 75 | closed | Point typo fix | 12-01-2018 00:07:09 | 12-01-2018 00:07:09 | ||
transformers | 74 | closed | Update finetuning example in README adding --do_lower_case | Should be consistent with the fact that an uncased model is used | 12-01-2018 00:06:52 | 12-01-2018 00:06:52 | Indeed |
transformers | 73 | closed | Third release | This third release comprise the following updates:
- added the two new pre-trained model from Google: `bert-large-cased` and `bert-multilingual-cased`,
- added a model for token-level classification: `BertForTokenClassification`,
- added tests for every model class, with and without labels,
- fixed tokenizer loadin... | 11-30-2018 22:10:22 | 11-30-2018 22:10:22 | |
transformers | 72 | closed | Fix internal hyperlink typo | Fix #tup to #tpu | 11-30-2018 21:13:47 | 11-30-2018 21:13:47 | |
transformers | 71 | closed | run_squad script gets stuck | Hello,
I am trying to run the squad fine tuning script, but it hangs after printing out a few predictions. I am attaching the log. Can you help take a look?
I am running the script on a machine with 8 M40s.
[bert_squad.log](https://github.com/huggingface/pytorch-pretrained-BERT/files/2634588/bert_squad.log)
... | 11-30-2018 18:39:54 | 11-30-2018 18:39:54 | Never mind, it just needed time to process the examples. It might be good to have the progress bar inside convert_examples_to_features.<|||||>Maybe try distributed training? I don't think PyTorch `DataParallel` will be very efficient on 8 GPUs due to the python GIL.<|||||>Thanks for the suggestion. I will try that. Cur... |
transformers | 70 | closed | fix typo in input for masked lm loss function | Fixing #55 . There was still a typo. | 11-30-2018 15:56:00 | 11-30-2018 15:56:00 | thanks |
transformers | 69 | closed | cannot access to pretrained vocab file on S3 | Hi, thanks for develop well-made pytorch version of BERT.
Unfortunately, pretrained vocab files are not reachable.
error traceback is below.
> File "/usr/local/lib/python3.6/dist-packages/pytorch_pretrained_bert/tokenization.py", line 124, in from_pretrained
resolved_vocab_file = cached_path(vocab_file)
Fi... | 11-30-2018 13:57:03 | 11-30-2018 13:57:03 | I have the same issue.
> OSError: HEAD request failed for url https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt with status code 404
It would be nice to be able to cache the vocab files as well as the model weights out of the box.<|||||>I found temporary solution for this issue.
... |
transformers | 68 | closed | Accuracy on classification task is lower than the official tensorflow version | Hi, I am running the same task with the same hyper parameters as the official Google Tensorflow implementation of BERT, however, I am getting around 1.5% lower accuracy. Can you please give any hint about the possible cause?
Thanks！ | 11-30-2018 06:30:56 | 11-30-2018 06:30:56 | Hi!
Could it be different seeds?
See e.g. https://github.com/huggingface/pytorch-pretrained-BERT/issues/53#issuecomment-441565229<|||||>Hi @ejld, yes BERT has a large variance on many fine-tuning tasks (see also the discussion in #64).
You should try a bunch of different seeds (like 10 seeds for example) and compare... |
transformers | 67 | closed | `TypeError: object of type 'NoneType' has no len()` when tuning on squad | When running the following command for tuning on squad, I am getting a petty error inside logger `TypeError: object of type 'NoneType' has no len()`. Any thoughts what could be the main cause of the problem?
Full log:
```
python3.6 examples/run_squad.py \
> --bert_model bert-base-uncased \
> --do_train ... | 11-30-2018 05:48:04 | 11-30-2018 05:48:04 | Oh I see, this should be fixed in `master` by 257a35134a1bd378b16aa985ee76675289ff439c just update your repo please. |
transformers | 66 | closed | speedup by truncating unused part | 11-29-2018 14:56:39 | 11-29-2018 14:56:39 | Hi Mathis,
Thanks for that. I think it's better for the user to send inputs that they truncated themselves rather than doing that hidden inside the model.
Best,
Thomas | |
transformers | 65 | closed | 3 sentences as input for BertForSequenceClassification? | Hi there,
Thanks for releasing this awesome repo, it does lots people like me a great favor.
So far I've tried sentence-pair BertForSequenceClassification task, and it indeed work. I'd like to know if it is possible to use BertForSequenceClassification to model triple sentences classification problem and its inpu... | 11-29-2018 09:18:21 | 11-29-2018 09:18:21 | Technically it is possible but BERT was not pretrained to handle multiple SEP tokens between sentences and does not have a third token_type, so I think it won't be easy to make it work. You may also want to use a new token for the second separation.<|||||>> Technically it is possible but BERT was not pretrained to hand... |
transformers | 64 | closed | Feature extraction for sequential labelling | Hi, I have a question in terms of using BERT for sequential labeling task.
Please correct me if I'm wrong.
My understanding is:
1. Use BertModel loaded with pretrained weights instead of MaskedBertModel.
2. In such case, take a sequence of tokens as input, BertModel would output a list of hidden states, I only use ... | 11-29-2018 03:33:09 | 11-29-2018 03:33:09 | Well that seems like a good approach. Maybe you can find some inspiration in the code of the `BertForQuestionAnswering` model? It is not exactly what you are doing but maybe it can help.<|||||>Thanks. It worked. However, a interesting issue about BERT is that it's highly sensitive to learning rate, which makes it very ... |
transformers | 63 | closed | Unseen Vocab | Thank you so much for this well-documented and easy-to-understand implementation! I remember meeting you at WeCNLP and am so happy to see you push out usable implementations of the SOA in pytorch for the community!!!!!
I have a question: The convert_tokens_to_ids method in the BertTokenizer that provides input to th... | 11-28-2018 22:38:57 | 11-28-2018 22:38:57 | If you tokenize properly the input (tokenize before convert_tokens), it automatically 'fallbacks' to subword/character-level(-like) embedding.
You can add new words in the vocabulary but you'll have to train the corresponding embeddings.<|||||>Hi @siddsach,
Thanks for your kind words!
@artemisart is right, BPE progr... |
transformers | 62 | closed | Specify a model from a specific directory for extract_features.py | I have downloaded the model and vocab files into a specific location, using their original file names, so my directory for bert-base-cased contains:
```
bert-base-cased-vocab.txt
bert_config.json
pytorch_model.bin
```
But when I try to specify the directory which contains these files for the `--bert_model` par... | 11-28-2018 17:04:39 | 11-28-2018 17:04:39 | The last update broke this, but you can fix this in tokenization.py, you have to add this after `vocab_file = pretrained_model_name`:
```
if os.path.isdir(vocab_file):
vocab_file = os.path.join(vocab_file, "vocab.txt")
```
<|||||>Thank you, is it fair to assume that this will get accepted as an issue and fixed... |
transformers | 61 | closed | BERTConfigs in example usages in `modeling.py` are not OK (?) | Hi!
In the `config` definition https://github.com/huggingface/pytorch-pretrained-BERT/blob/21f0196412115876da1c38652d22d1f7a14b36ff/pytorch_pretrained_bert/modeling.py#L848
in the Example usage of `BertForSequenceClassification` in `modeling.py`, there's things I don't understand:
- `vocab_size` in not an accept... | 11-28-2018 14:53:01 | 11-28-2018 14:53:01 | Hi @davidefiocco, you are right, I updated the docstrings in the new release 0.3.0. |
transformers | 60 | closed | Updated quick-start example with `BertForMaskedLM` | As `convert_ids_to_tokens` returns a list, the code in the README currently throws an `AssertionError`, so I propose a quick fix. | 11-28-2018 13:54:01 | 11-28-2018 13:54:01 | Nice, thanks @davidefiocco |
transformers | 59 | closed | not good when I use BERT for seq2seq model in keyphrase generation | Hi,
recently, I am researching about Keyphrase generation. Usually, people use seq2seq with attention model to deal with such problem. Specifically I use the framework: https://github.com/memray/seq2seq-keyphrase-pytorch, which is implementation of http://memray.me/uploads/acl17-keyphrase-generation.pdf .
Now I ... | 11-28-2018 08:44:24 | 11-28-2018 08:44:24 | have u tried transformer decoder ?instead of rnn decoder. <|||||>not yet, I will try. But I think rnn decoder should not be such bad. <|||||>> not yet, I will try. But I think rnn decoder should not be such bad.
emmm，maybe u should used mean of last layer to initialize decoder, not the last token representation of... |
transformers | 58 | closed | Bug fix in examples;correct t_total for distributed training;run pred… | Bug fix in examples;
correct t_total for distributed training;
run prediction for full dataset | 11-27-2018 09:10:10 | 11-27-2018 09:10:10 | Thanks @lliimsft! |
transformers | 57 | closed | Missing function convert_to_unicode in tokenization.py | The function _convert_to_unicode_ is not in tokenization.py but used to be there in v0.1.2. When fine tuning with run_classifier.py, you get an ImportError: cannot import name 'convert_to_unicode'.
https://github.com/huggingface/pytorch-pretrained-BERT/blob/ce37b8e4819142171b61558e64f7dcb0286e9937/examples/run_class... | 11-26-2018 21:50:15 | 11-26-2018 21:50:15 | Fixed in master, thanks! |
transformers | 56 | closed | [Feature request ] Add support for the new cased version of the multilingual model | https://github.com/google-research/bert/commit/332a68723c34062b8f58e5fec3e430db4563320a | 11-26-2018 10:56:18 | 11-26-2018 10:56:18 | Hi @elyase, this model is now added in the new release 0.3.0.
I also added the other new model by Google (`bert-large-cased`) |
transformers | 55 | closed | Loss calculation error | https://github.com/huggingface/pytorch-pretrained-BERT/blob/982339d82984466fde3b1466f657a03200aa2ffb/pytorch_pretrained_bert/modeling.py#L744
Got `ValueError: Expected target size (1, 30522), got torch.Size([1, 11])` at line 744 of `modeling.py`. I think the line should be changed to `masked_lm_loss = loss_fct(predi... | 11-25-2018 03:48:17 | 11-25-2018 03:48:17 | Hi Jian, can you give me a small (self-contained) example showing how to get this error?<|||||>Hi Thomas! I modified the code in your `README.md` for an example:
```python
from pytorch_pretrained_bert.modeling import BertForMaskedLM, BertConfig
from pytorch_pretrained_bert import BertTokenizer
import torch
mod... |
transformers | 54 | closed | example in BertForSequenceClassification() conflicts with the api | Hi, firstly, admire u for the great job. but I encounter 2 problems when i use it:
**1**. `UnicodeDecodeError: 'gbk' codec can't decode byte 0x85 in position 4527: illegal multibyte sequence`,
same problem as ISSUE 52 when I excute the `BertTokenizer.from_pretrained('bert-base-uncased')`, but I successfully excute `... | 11-24-2018 07:27:50 | 11-24-2018 07:27:50 | Hi,
(1) is solved on master. I will release a new release soon with the fixes on pip. In the mean time you can install from sources if you want.
I fixed the typo in the docstring you mention in (2), thanks, it should be a `1` instead of a `2`. |
transformers | 53 | closed | Multi-GPU training vs Distributed training | Hi,
I have a question about Multi-GPU vs Distributed training, probably unrelated to BERT itself.
I have a 4-GPU server, and was trying to run `run_classifier.py` in two ways:
(a) run single-node distributed training with 4 processes and minibatch of 32 each
(b) run Multi-GPU training with minibatch of 128, a... | 11-24-2018 00:49:45 | 11-24-2018 00:49:45 | Hi,
Thanks for the feedback, it's always interesting to compare the various possible ways to train the model indeed.
The most likely cause for (2) is that MRPC is a small dataset and the model shows a high variance in the results depending on the initialization of the weights for example (see the original BERT re... |
transformers | 52 | closed | UnicodeDecodeError: 'charmap' codec can't decode byte 0x90 in position 3920: character maps to <undefined> | Installed pytorch-pretrained-BERT from source, Python 3.7, Windows 10
When I run the following snippet:
import torch
from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM
# Load pre-trained model tokenizer (vocabulary)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
... | 11-22-2018 15:42:08 | 11-22-2018 15:42:08 | I am facing the same problem.
Fixed it with "with open(vocab_file, "r"**, encoding="utf-8"**) as reader:" in line 68 of tokenization.py<|||||>Thanks, it's fixed on master and will be included in the next release. |
transformers | 51 | closed | Missing options/arguments in run_squad.py for BERT Large | Thanks for the great code..However, the `run_squad.py` for BERT Large seems to not have the `vocab_file` and `bert_config_file` (or other) options/arguments. Did you push the latest version?
Also, it is looking for a pytorch model file (a bin file). Does it need to be there?
I also had to add this line to the file... | 11-21-2018 15:10:45 | 11-21-2018 15:10:45 | Yes, the readme example was for an older version. I have updated them with the simplified parameters used in the current release. Thanks. |
transformers | 50 | closed | pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py error | attributeError: 'BertForPreTraining' object has no attribute 'global_step' | 11-21-2018 10:36:49 | 11-21-2018 10:36:49 | Maybe some additional information could help me help you?<|||||>Initialize PyTorch weight ['cls', 'seq_relationship', 'output_weights']
Skipping cls/seq_relationship/output_weights/adam_m
Skipping cls/seq_relationship/output_weights/adam_v
Traceback (most recent call last):
File "/home/tiandan.cxj/python/model_se... |
transformers | 49 | closed | Multilingual Issue | Dear authors,
I have two questions.
First, how can I use multilingual pre-trained BERT in pytorch?
Is it all download model to $BERT_BASE_DIR?
Second is tokenization issue.
For Chinese and Japanese, tokenizer may works, however, for Korean, it shows different result that I expected
```
import torch
from p... | 11-21-2018 09:32:32 | 11-21-2018 09:32:32 | Hi, you can use the multilingual model as [indicated in the readme](https://github.com/huggingface/pytorch-pretrained-BERT#loading-google-ais-pre-trained-weigths-and-pytorch-dump) with the commands:
```python
tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual')
model = BertModel.from_pretrained('bert-... |
transformers | 48 | closed | example for is next sentence | Can you make up a working example for 'is next sentence'
Is this expected to work properly ?
```
# Load pre-trained model tokenizer (vocabulary)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Tokenized input
text = "Who was Jim Morrison ? Jim Morrison was a puppeteer"
tokenized_text = tok... | 11-21-2018 03:16:00 | 11-21-2018 03:16:00 | I think it should work. You should get a [1, 2] tensor of logits where `predictions[0, 0]` is the score of Next sentence being `True` and `predictions[0, 1]` is the score of Next sentence being `False`. So just take the max of the two (or use a `SoftMax` to get probabilities).
Did you try it?
The model behaves better... |
transformers | 47 | closed | Fine-Tuned BERT-base on Squad v1. | I have fine-tuned the TF model on SQuAD v1 and I've made the weights available at: https://s3.eu-west-2.amazonaws.com/nlpfiles/squad_bert_base.tgz
I get 88.5 FM using these weights on SQuAD dev. (If I recall correctly I get roughly 82 EM).
I think it may be beneficial to have these weights here, so that people c... | 11-20-2018 17:04:09 | 11-20-2018 17:04:09 | Thanks for the details.
This PyTorch repo is starting to be used by a larger community so we would have to be a little more precise than just rough numbers if we want to include such pre-trained weights.
If you want to add your weights to the repo, you should convert the weights in the PyTorch repo model and get eval... |
transformers | 46 | closed | Assertion `srcIndex < srcSelectDimSize` failed. | Sorry to bother you
I recently have used your extract_features.py to extract features of some data set but failed. The error information is as follows:
`/opt/conda/conda-bld/pytorch_1532584813488/work/aten/src/THC/THCTensorIndex.cu:362: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, Te... | 11-20-2018 12:50:41 | 11-20-2018 12:50:41 | Your log is very hard to read. Can you format it cleanly?<|||||>I'm so sorry
The first error log is as follows:
```bash
/opt/conda/conda-bld/pytorch_1532584813488/work/aten/src/THC/THCTensorIndex.cu:362: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, ... |
transformers | 45 | closed | Issue of `bert_model` arg in `run_classify.py` | Hi,
I am trying to understand the `bert_model` arg in `run_classify.py`. In the file, I can see
```
tokenizer = BertTokenizer.from_pretrained(args.bert_model)
```
where `bert_model` is expected to be the vocab text file of the model
However, I also see
```
model = BertForSequenceClassification.from_pretr... | 11-20-2018 09:48:09 | 11-20-2018 09:48:09 | Hi, please read [this section](https://github.com/huggingface/pytorch-pretrained-BERT#loading-google-ais-pre-trained-weigths-and-pytorch-dump) of the readme. |
transformers | 44 | closed | Race condition when prepare pretrained model in distributed training | Hi,
I launched two processes per node to run distributed run_classifier.py. However, I am occasionally get below error:
```
11/20/2018 09:31:48 - INFO - pytorch_pretrained_bert.file_utils - copying /tmp/tmpa25_y4es to cache at /root/.pytorch_pretrained_bert/9c41111e2de84547a463fd39217199738d1e3deb72d4fec4399e6... | 11-20-2018 09:40:25 | 11-20-2018 09:40:25 | My current workaround is to set the env var `PYTORCH_PRETRAINED_BERT_CACHE` to a different path per process before import `pytorch_pretrained_bert`. But I think the module itself should handle this properly<|||||>I see, thanks for the feedback. I will find a way to make that better in the next release. Not sure we need... |
transformers | 43 | closed | grad is None in squad example | Hi, guys, I try the `run_squad` example with
```
Traceback (most recent call last): | 0/7331 [00:00<?, ?it/s]
File "examples/run_squad.py", line 973, in <m... | 11-20-2018 08:38:03 | 11-20-2018 08:38:03 | Oh you're right. I've just fixed that. you can try to pull the current master and test again.<|||||>@thomwolf it works, thanks |
transformers | 42 | closed | Fixed UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 | I encountered `UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 3793: ordinal not in range(128)` when running the starter example shown under the Usage section. It turned out to be related to the `load_vocab` function in `tokenization.py`. Forcing `open` to use encoding `utf8` solved this issue on ... | 11-20-2018 04:09:44 | 11-20-2018 04:09:44 | Thanks! |
transformers | 41 | closed | Typo in README | I think I spotted a typo in the README file under the Usage header. There is a piece of code that uses `BertTokenizer` and the typo is on this line:
`tokenized_text = "Who was Jim Henson ? Jim Henson was a puppeteer"`
I think `tokenized_text` should be replaced with `text`, since the next line is
`tokenized_text =... | 11-20-2018 03:52:35 | 11-20-2018 03:52:35 | Yes |
transformers | 40 | closed | update pip package name | dashes not underscores | 11-19-2018 17:50:54 | 11-19-2018 17:50:54 | |
transformers | 39 | closed | Command-line interface Document Bug | There is a bug in README.md about Command-line interface:
`export BERT_BASE_DIR=chinese_L-12_H-768_A-12`
**Wrong:**
```
pytorch_pretrained_bert convert_tf_checkpoint_to_pytorch \
--tf_checkpoint_path $BERT_BASE_DIR/bert_model.ckpt.index \
--bert_config_file $BERT_BASE_DIR/bert_config.json \
--pytorch_... | 11-19-2018 16:42:56 | 11-19-2018 16:42:56 | Thanks! |
transformers | 38 | closed | truncated normal initializer | I have a reasonable truncated normal approximation. (Actually that is what tf does).
https://discuss.pytorch.org/t/implementing-truncated-normal-initializer/4778/16?u=ruotianluo
| 11-19-2018 16:35:08 | 11-19-2018 16:35:08 | We could try that. Not sure how important it is though. Did you try it?<|||||>Ok I think we will stick to the normal_initializer for now. Thanks for indicating this option! |
transformers | 37 | closed | using BERT as a language Model | I was trying to use BERT as a language model to assign a score(could be PPL score) of a given sentence. Something like
P("He is go to school")=0.008
P("He is going to school")=0.08
Which is indicating that the probability of second sentence is higher than first sentence. Is there a way to get a score like this?
... | 11-19-2018 15:26:20 | 11-19-2018 15:26:20 | I don't think you can do that with Bert. The masked LM loss is not a Language Modeling loss, it doesn't work nicely with the [chain rule](https://en.wikipedia.org/wiki/Chain_rule_%28probability%29) like the usual Language Modeling loss.
Please see the discussion on the TensorFlow repo on that [here](https://github.com... |
transformers | 36 | closed | How to detokenize a BertTokenizer output? | I was wondering if there's a proper way of detokenizing the output tokens, i.e., constructing the sentence back from the tokens? Considering the fact that the word-piece tokenisation introduces lots of `#`s. | 11-19-2018 04:39:04 | 11-19-2018 04:39:04 | You can remove ' ##' but you cannot know if there was a space around punctuations tokens or uppercase words.<|||||>Yes. I don't plan to include a reverse conversion of tokens in the tokenizer.
For an example on how to keep track of the original characters position, please read the `run_squad.py` example.<|||||>In my c... |
transformers | 35 | closed | issues with accents on convert_ids_to_tokens() | Hello, the BertTokenizer seems loose accents when convert_ids_to_tokens() is used :
Example:
- original sentence: "great breakfasts in a nice furnished cafè, slightly bohemian."
- corresponding list of token produced : ['great', 'breakfast', '##s', 'in', 'a', 'nice', 'fur', '##nis', '##hed', 'cafe', ',', 'slightly... | 11-18-2018 20:41:24 | 11-18-2018 20:41:24 | This is expected behaviour and is how the multilingual and the uncased models were trained. From the [original repo](https://github.com/google-research/bert/blob/master/README.md):
> We are releasing the BERT-Base and BERT-Large models from the paper. Uncased means that the text has been lowercased before WordPiece ... |
transformers | 34 | closed | Can not find vocabulary file for Chinese model | After I convert the TF model to pytorch model, I run a classification task on a new Chinese dataset, but get this:
CUDA_VISIBLE_DEVICES=3 python run_classifier.py --task_name weibo --do_eval --do_train --bert_model chinese_L-12_H-768_A-12 --max_seq_length 128 --train_batch_size 32 --learning_rate 2e-5 --num_... | 11-18-2018 14:33:58 | 11-18-2018 14:33:58 | need to specify the path of vocab.txt for:
tokenizer = BertTokenizer.from_pretrained(args.bert_model)<|||||>@zlinao ，i try to load the vocab using the following code：
tokenizer = BertTokenizer.from_pretrained("bert-base-chinese//vocab.txt"
however，get errors
11/19/2018 15:33:13 - INFO - pytorch_pretrained_bert.to... |
transformers | 33 | closed | [Bug report] Ineffective no_decay when using BERTAdam | https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_classifier.py#L505-L508
With this code, all parameters are decayed because the condition "parameter_name in no_decay" will never be satisfied.
I've made a PR #32 to fix it. | 11-18-2018 08:28:52 | 11-18-2018 08:28:52 | You're right, thanks! |
transformers | 32 | closed | Fix ineffective no_decay bug when using BERTAdam | With the original code, all parameters are decayed because the condition "parameter_name in no_decay" will never be satisfied. | 11-18-2018 08:21:37 | 11-18-2018 08:21:37 | thanks!<|||||>Question - wouldn't `.named_parameters()` for the model return a tuple `(name, param_tensor)`, where name looks similar to these
```
['bert.embeddings.word_embeddings.weight',
'bert.embeddings.position_embeddings.weight',
'bert.embeddings.token_type_embeddings.weight',
'bert.embeddings.LayerNorm.w... |
transformers | 31 | closed | BERT model for Machine Translation | Is there a way to use any of the provided pre-trained models in the repository for machine translation task?
Thanks | 11-18-2018 02:10:15 | 11-18-2018 02:10:15 | Hi Kerem, I don't think so. Have a look at the fairsep repo maybe.<|||||>@thomwolf hi there, I couldn't find out anything about the fairsep repo. Could you post a link? Thanks!<|||||>Hi, I am talking about this repo: https://github.com/pytorch/fairseq.
Have a look at their Transformer's models for machine translation.... |
transformers | 30 | closed | [Feature request] Add example of finetuning the pretrained models on custom corpus | 11-17-2018 15:19:58 | 11-17-2018 15:19:58 | Hi I don't plan to add that in the near future but feel free to open a PR if you would like to share an additional example.<|||||>Necrobumping this for reference, as this is addressed in https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_lm_finetuning.py | |
transformers | 29 | closed | First release | 11-17-2018 11:19:41 | 11-17-2018 11:19:41 | ||
transformers | 28 | closed | speed is very slow | convert samples to features, is very slow | 11-17-2018 06:51:54 | 11-17-2018 06:51:54 | Running on a GPU, I find that dumping extracted features takes up most time. So you may optimize it yourself. <|||||>Hi, these examples are provided as starting point to write your own training scripts using the package modules. I don't plan to update them any further. |
transformers | 27 | closed | how to load checkpoint? | i download the model from bert, it only has model.ckpt.data，model.ckpt.meta and model.ckpt.index, i donnot which to load, what is checkpoint file for convert.py? | 11-17-2018 06:23:28 | 11-17-2018 06:23:28 | Converting TensorFlow checkpoint from ../dataset/bert/uncased_L-12_H-768_A-12/bert_model
Traceback (most recent call last):
File "convert_tf_checkpoint_to_pytorch.py", line 111, in <module>
convert()
File "convert_tf_checkpoint_to_pytorch.py", line 60, in convert
init_vars = tf.train.list_variables(pat... |
transformers | 26 | closed | Checkpoints not saved | There is an option `save_checkpoints_steps` that seems to control checkpointing. However, there is no actual saving operation in the `run_*` scripts. So, should we add that functionality or remove this argument? | 11-16-2018 18:50:27 | 11-16-2018 18:50:27 | In the `run_squad.py`script, I added the following lines after the training loop:
```
logger.info(***** Saving fine-tuned model *****)
output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
if n_gpu > 1:
torch.save(model.module.bert.state_dict(), output_model_file)
else:
torch.save(mode... |
transformers | 25 | closed | can you push the run-pretraining and create_pretraining_data codes? | just want to study codes, don't need to have same pre-train performance. | 11-16-2018 08:15:33 | 11-16-2018 08:15:33 | Hi, I don't have plan for that in the near future. |
transformers | 24 | closed | [Feature request] Port SQuAD 2.0 support | Recently the Google team added support for Squad 2.0:
https://github.com/google-research/bert/commit/60454702590a6c69bd45c5d4258c7e17b8a3e1da
Would be great to also have it available in the Pytorch version. | 11-15-2018 23:47:04 | 11-15-2018 23:47:04 | Hi, I don't have plan for that in the near future but feel free to open a PR. |
transformers | 23 | closed | ValueError while using --optimize_on_cpu | > Traceback (most recent call last): | 1/87970 [00:00<8:35:35, 2.84it/s]
File "./run_squad.py", line 990, in <module>
main()
File "./run_squad.py", line 922, in main
is_nan = set_optimizer_params_grad(param_optimizer, model.named_parameters(), test_nan=True)
File "./run_squad.py", line 691, in set_optimizer_params... | 11-15-2018 16:53:12 | 11-15-2018 16:53:12 | Thanks! I pushed a fix for that, you can try it again. You should be able to increase a bit the batch size.
By the way, the real batch size that is used on the gpu is `train_batch_size / gradient_accumulation_steps` so `2` in your case. I think you should be able to go to `3` with `--optimize_on_cpu`
The recommen... |
transformers | 22 | closed | adding `no_cuda` flag | The `--no_cuda` flag is missing from the flagset in `extract_features.py`. On running the current code, the following error occurs.
```
(py3.5) [rahul pytorch-pretrained-BERT]$ python extract_features.py \
> --input_file=./input.txt \
> --output_file=./output.jsonl \
> --vocab_file=$BERT_BASE_DIR/vocab.txt... | 11-15-2018 10:33:03 | 11-15-2018 10:33:03 | Thanks, I've added that manually (the library organization has changed a bit with the first pip release). |
transformers | 21 | closed | Fix some glitches in extract_features.py | Do the following fixing to make the extract_features.py runnable:
1. Add no_cuda argument
2. Fix the "not all arguments converted during string formatting" error thrown at line 230 | 11-15-2018 07:49:20 | 11-15-2018 07:49:20 | Thanks, I've pushed these fixes in the first release (the organization of the library changed quite a bit). |
transformers | 20 | closed | model loading the checkpoint error | RuntimeError: Error(s) in loading state_dict for BertModel:
size mismatch for embeddings.token_type_embeddings.weight: copying a param of torch.Size([16, 768]) from checkpoint, where the shape is torch.Size([2, 768]) in current model. | 11-14-2018 08:13:34 | 11-14-2018 08:13:34 | But I print the model.embeddings.token_type_embeddings it was Embedding(16,768) .<|||||>which model are you loading?<|||||>> which model are you loading?
the pre-trained model chinese_L-12_H-768_A-12<|||||>mycode:
bert_config = BertConfig.from_json_file('bert_config.json')
model=BertModel(bert_config)
model... |
transformers | 19 | closed | will you push the pytorch code for the pre-training process? | Can you push the pytorch code for the pre-training process,such as MLM task, please?
I really want to study, but I can't understand tensorflow, it's so complex.
thanks!!! | 11-14-2018 06:30:59 | 11-14-2018 06:30:59 | Hi, I don't have plan for that in the near future. |
transformers | 18 | closed | include the output layer in the model using the pretrained weights | This is to be able to load the final output layer (bert.output_layer) from the TensorFlow pre-trained model.
In particular, it is a fully connected layer that is used to map the final hidden layer to the vocabulary size, to then apply the softmax, as follows:
logits = bert.output_layer(sequence_output)
log_softmax... | 11-13-2018 16:15:03 | 11-13-2018 16:15:03 | Thanks for that. I've ended up taking a more modular approach in the first pip release of the library. |
transformers | 17 | closed | activation function in BERTIntermediate | Was previously hardcoded to gelu because pretrained BERT models use gelu.
Changed to make BERTIntermediate use functions and "gelu", "relu" or "swish" from `config`. | 11-13-2018 15:47:46 | 11-13-2018 15:47:46 | Looks good, thanks for that! |
transformers | 16 | closed | Excluding AdamWeightDecayOptimizer internal variables from restoring | I tried to use convert_tf_checkpoint_to_pytorch.py script to convert my pretrained model, but in order to do so, I had to make some minor tweaks. I thought I would share in case you find it useful. | 11-13-2018 15:13:18 | 11-13-2018 15:13:18 | Is your pre-trained model a TensorFlow model?<|||||>Yes<|||||>Nice, thanks for that! |
transformers | 15 | closed | activation function in BERTIntermediate | BERTConfig is not used for `BERTIntermediate`'s activation function. `intermediate_act_fn` is always `gelu`. Is this normal?
https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/modeling.py#L240 | 11-13-2018 15:09:33 | 11-13-2018 15:09:33 | Yes, I hard coded that since the pre-trained models are all trained with gelu anyway.<|||||>ok. but since config is there anyway, isn't it cleaner to use it (to avoid errors for people using configs that use a different activation for some reason) ?<|||||>Yes we can, I'll change that in the coming first release (unless... |
transformers | 14 | closed | fixed typo | When test with SQuAD | 11-12-2018 01:18:24 | 11-12-2018 01:18:24 | Hi,
Thanks for the PR, we don't want to add a shell script to the repo.
I will correct the typo,
Best,
Thom |
transformers | 13 | closed | Bug in run_classifier.py | If I am running only evaluation and not training, there are errors as tr_loss and nb_tr_steps are undefined. | 11-10-2018 17:16:01 | 11-10-2018 17:16:01 | |
transformers | 12 | closed | py2 code | if I convert code to python2 version of code, it can't converage ; Would you present py2 code? | 11-10-2018 13:23:31 | 11-10-2018 13:23:31 | Hi, we won't provide a python 2 version but if you want to do a python 2/3 compatible version feel free to open a PR. |
transformers | 11 | closed | Swapped to_seq_len/from_seq_len in comment | I'm pretty sure this comment:
https://github.com/huggingface/pytorch-pretrained-BERT/blob/2c5d993ba48841575d9c58f0754bca00b288431c/modeling.py#L339-L343
should instead say:
```
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
```
... | 11-09-2018 06:13:08 | 11-09-2018 06:13:08 | Yes! fixed the comment |
transformers | 10 | closed | Is there a plan to have a FP16 for GPU so to have larger batch size or longer text documents support ? | Is there a plan to have an FP16 for GPU so to have a larger batch size or longer text documents support? | 11-09-2018 02:23:34 | 11-09-2018 02:23:34 | Yes probably. I am testing fp16 right now. If it works well I will push it to the repo.<|||||>Ok I've added FP16 support (see updated readme)<|||||>Thanks for this quick updates.<|||||>I'm not able to work with FP16 for pytorch BERT code. Particularly for BertForSequenceClassification, which I tried and got the issue
... |
transformers | 9 | closed | Crash at the end of training | Hi, I tried running the Squad model this morning (on a single GPU with gradient accumulation over 3 steps) but after 3 hours of training, my job failed with the following output:
I was running the code, unmodified, from commit 3bfbc21376af691b912f3b6256bbeaf8e0046ba8
Is this an issue you know about?
```
11/08/2... | 11-08-2018 22:01:57 | 11-08-2018 22:01:57 | Here's the specific command I ran for more context:
```
python3.6 code/run_squad.py \
--bert_config_file bert/bert_config.json \
--vocab_file bert/vocab.txt \
--output_dir output \
--train_file data/original/train.json \
--predict_file data/original/dev.json \
--init_checkpoint bert-pytorch/pytorch... |
transformers | 8 | closed | fixed small typos in the README.md | 11-08-2018 18:24:27 | 11-08-2018 18:24:27 | Many thanks! | |
transformers | 7 | closed | Develop | Fixing `run_squad.py` pre-processing bug.
Various clean-ups:
- the weight initialization was not optimal (tf. truncated_normal_initializer(stddev=0.02) was translated in weight.data.normal_(0.02) instead of weight.data.normal_(mean=0.0, std=0.02) which likely affected the performance of run_classifer.py also.
- ... | 11-07-2018 22:34:18 | 11-07-2018 22:34:18 | |
transformers | 6 | closed | Failure during pytest (and solution for python3) | ```
foo@bar:~/foo/bar/pytorch-pretrained-BERT$ pytest -sv ./tests/
===================================================================================================================== test session starts =================================================================================================================... | 11-06-2018 08:23:29 | 11-06-2018 08:23:29 | Thanks, I update the readme. |
transformers | 5 | closed | MRPC hyperparameters question | When describing how you reproduced the MRPC results, you say:
"Our test ran on a few seeds with the original implementation hyper-parameters gave evaluation results between 82 and 87."
and you link to the SQuAD hyperparameters (https://github.com/google-research/bert#squad).
Is the link a mistake? Or did you use t... | 11-06-2018 05:30:36 | 11-06-2018 05:30:36 | Hi Ethan,
Thanks we used the MRPC hyper-parameters indeed, I corrected the README.
Regarding the dev set accuracy, I am not really surprised there is a slightly lower accuracy with the PyTorch version (even though the variance is high so it's hard to get something significant). That is something that is generally obs... |
transformers | 4 | closed | Fix typo in subheader BertForQuestionAnswering | Should say `BertForQuestionAnswering`, but says `BertForSequenceClassification`. | 11-05-2018 23:04:03 | 11-05-2018 23:04:03 | exact thanks ! |
transformers | 3 | closed | run_squad questions | Thanks a lot for the port! I have some minor questions, for the run_squad file, I see two options for accumulating gradients, accumulate_gradients and gradient_accumulation_steps but it seems to me that it can be combined into one. The other one is for the global_step variable, seems we are only counting but not using ... | 11-05-2018 21:35:51 | 11-05-2018 21:35:51 | It also seems to me that the SQuAD 1.1 can not reproduce the google tensorflow version performance.<|||||>> It also seems to me that the SQuAD 1.1 can not reproduce the google tensorflow version performance.
What batch size are you running?<|||||>I'm running on 4 GPU with a batch size of 48, the result is {"exact_ma... |
transformers | 2 | closed | Port tokenization for the multilingual model | 11-05-2018 21:35:36 | 11-05-2018 21:35:36 | Thanks for that, sorry for the delay | |
transformers | 1 | closed | Create DataParallel model if several GPUs | 11-03-2018 14:10:20 | 11-03-2018 14:10:20 |
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