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https://api.github.com/repos/huggingface/transformers/issues/208 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/208/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/208/comments | https://api.github.com/repos/huggingface/transformers/issues/208/events | https://github.com/huggingface/transformers/pull/208 | 400,951,566 | MDExOlB1bGxSZXF1ZXN0MjQ2MDE5MTc4 | 208 | Merge run_squad.py and run_squad2.py | {
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"#152 run squad pull request with Squad 2.0 was originally one file but was asked to be separated into two files for commit. #174",
"@abeljim Thanks for your replying, but I think that they should merge to a single file due to easier maintain and too many repeat code.",
"How many epochs used in SQuAD2.0 in your... | 1,547 | 1,597 | 1,549 | CONTRIBUTOR | null | Merge run_squad.py and run_squad2.py to a single file as [official's Bert](https://github.com/google-research/bert).
I did integration test by myself and run once.
The following is the scores with base-uncased model
| Task | Exact | F1 |
| ------------- | ------------- | ------------- |
| SQuAD v1.1 | 80.90... | {
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https://api.github.com/repos/huggingface/transformers/issues/207 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/207/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/207/comments | https://api.github.com/repos/huggingface/transformers/issues/207/events | https://github.com/huggingface/transformers/issues/207 | 400,885,697 | MDU6SXNzdWU0MDA4ODU2OTc= | 207 | AttributeError: 'NoneType' object has no attribute 'start_logit' | {
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"Can you post a self-contained example to reproduce your error ? Which version of python, pytorch and pytorch-pretrained-bert are you using?",
"Closing since there is no recent activity. Feel free to re-open if needed.",
"I ran into the same issue. Any pointers on how I could triage this further?",
"@thomwolf... | 1,547 | 1,554 | 1,549 | CONTRIBUTOR | null | In the `run_squad2` example notebook, the `write_predictions` method fails because `best_non_null_entry` is `None`
```
Evaluating: 100%|βββββββββββββββββββββββββββ| 1529/1529 [05:12<00:00, 4.88it/s]
01/18/2019 21:42:28 - INFO - __main__ - Writing predictions to: ./models/squad2/predictions.json
01/18/2019 21:4... | {
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https://api.github.com/repos/huggingface/transformers/issues/206 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/206/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/206/comments | https://api.github.com/repos/huggingface/transformers/issues/206/events | https://github.com/huggingface/transformers/issues/206 | 400,775,467 | MDU6SXNzdWU0MDA3NzU0Njc= | 206 | Classifier example not training on CoLa data | {
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"Try this for CoLA: `--bert_model bert-base-uncased --do_lower_case`. \r\nYou may also need to increase `num_train_epochs` or `learning_rate` a little. \r\n",
"Thanks for your suggestions, but when following them nothing changes, it always predict one class regardless.\r\nTried:\r\n- 3 and 10 epochs\r\n- differen... | 1,547 | 1,549 | 1,549 | NONE | null | Hi,
I obtained strange classification eval results (always predicting the same label) when trying out the `run_classifier.py` after cloning the repo (no modif) so to dig a bit more I rebalanced the CoLA dataset (train.tsv and dev.tsv) to have a better understanding of what is happening. When running the classifier e... | {
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https://api.github.com/repos/huggingface/transformers/issues/205 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/205/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/205/comments | https://api.github.com/repos/huggingface/transformers/issues/205/events | https://github.com/huggingface/transformers/issues/205 | 400,738,031 | MDU6SXNzdWU0MDA3MzgwMzE= | 205 | What is the meaning of Attention Mask | {
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"Yes, this conversion is done inside the model, see this line: https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L626\r\n(we don't use infinity but a large value that works also when the model is used in half precision mode)",
"Thanks for your answer. Well, I s... | 1,547 | 1,660 | 1,547 | NONE | null | Hi, I noticed that there is something called `Attention Mask` in the model.
In the annotation of class `BertForQuestionAnswering`,
```python
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
selected in [0, 1]. It's a mask to be used if the input seq... | {
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https://api.github.com/repos/huggingface/transformers/issues/204 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/204/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/204/comments | https://api.github.com/repos/huggingface/transformers/issues/204/events | https://github.com/huggingface/transformers/issues/204 | 400,582,170 | MDU6SXNzdWU0MDA1ODIxNzA= | 204 | Two to Three mask word prediction at the same sentence is very complex | {
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"Hi @MuruganR96, from my experiments two to three mask word prediction doesn't seems to be possible with BERT.",
"thanks @thomwolf sir"
] | 1,547 | 1,548 | 1,548 | NONE | null | Two to Three mask word prediction at the same sentence also very complex.
how to get good accuracy?
if i have to pretrained bert model and own dataset with **masked_lm_prob=0.25** (https://github.com/google-research/bert#pre-training-with-bert), what will happened?
Thanks. | {
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https://api.github.com/repos/huggingface/transformers/issues/203 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/203/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/203/comments | https://api.github.com/repos/huggingface/transformers/issues/203/events | https://github.com/huggingface/transformers/issues/203 | 400,544,254 | MDU6SXNzdWU0MDA1NDQyNTQ= | 203 | Add some new layers from BertModel and then 'grad' error occurs | {
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"If you can share a (minimal) example reproducing the error, I can have a look.",
"I'm closing this. Feel free to re-open and share more information if you still have some issues."
] | 1,547 | 1,548 | 1,548 | NONE | null | I wanna do the fine-tuning work by adding a textcnn on the base of BertModel. I write a new class and add two layers of conv (like a textcnn) basically on Embedding Layer. And then an error occurs, called "grad can be implicitly created only for scalar outputs" i search for the Internet and can't find a good solution t... | {
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https://api.github.com/repos/huggingface/transformers/issues/202 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/202/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/202/comments | https://api.github.com/repos/huggingface/transformers/issues/202/events | https://github.com/huggingface/transformers/issues/202 | 400,521,941 | MDU6SXNzdWU0MDA1MjE5NDE= | 202 | training new BERT seems not working | {
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"Hi @UCJerryDong,\r\n\r\nTraining BERT from scratch takes a (very) long time (see the paper for TPU training, an estimation is training time using GPUs is about a week using 64 GPUs), this script is more for fine-tuning (using the pre-training objective) than to train from scratch.\r\n\r\nDid you monitor the losses... | 1,547 | 1,642 | 1,551 | NONE | null | I tried to train a BERT mode from scratch by "run_lm_finetuning.py" with toy training data (samples/sample.txt) by changing the following:
`#model = BertForPreTraining.from_pretrained(args.bert_model)`
`bert_config = BertConfig.from_json_file('bert_config.json')`
`model = BertForPreTraining(bert_config) `
wher... | {
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https://api.github.com/repos/huggingface/transformers/issues/201 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/201/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/201/comments | https://api.github.com/repos/huggingface/transformers/issues/201/events | https://github.com/huggingface/transformers/pull/201 | 400,365,120 | MDExOlB1bGxSZXF1ZXN0MjQ1NTY5NTY0 | 201 | run_squad2 Don't save model if do not train | {
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"Thanks!"
] | 1,547 | 1,547 | 1,547 | CONTRIBUTOR | null | There is a bug in example/run_squad2.py
If the model do not train, the initialized value will cover the pertained model. | {
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https://api.github.com/repos/huggingface/transformers/issues/200 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/200/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/200/comments | https://api.github.com/repos/huggingface/transformers/issues/200/events | https://github.com/huggingface/transformers/pull/200 | 400,164,405 | MDExOlB1bGxSZXF1ZXN0MjQ1NDE1MzQx | 200 | Adding Transformer-XL pre-trained model | {
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"Awesome work!\r\n\r\nI'm not sure I understand how this model integrates with BERT. Did Google release weights for the MLM + next sentence prediction task using the Transformer XL? And if they did not, how well do the classical LM weights performs for finetuning tasks?",
"Oh it's not integrated with BERT, that j... | 1,547 | 1,588 | 1,549 | MEMBER | null | Add Transformer-XL (https://github.com/kimiyoung/transformer-xl) with the pre-trained WT103 model (maybe also the 1B-Word model).
The original Google/CMU PyTorch version (https://github.com/kimiyoung/transformer-xl/tree/master/pytorch) has been slightly modified to better match the TF version which has the SOTA res... | {
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https://api.github.com/repos/huggingface/transformers/issues/198 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/198/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/198/comments | https://api.github.com/repos/huggingface/transformers/issues/198/events | https://github.com/huggingface/transformers/issues/198 | 399,671,672 | MDU6SXNzdWUzOTk2NzE2NzI= | 198 | HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /models.huggingface.co/bert/bert-base-uncased.tar.gz (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x000002456AF21710>: Failed to establish a new connection: [WinError 10060] A connection attemp... | {
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"Hi, you need a (stable) internet connection to download the weights. This operation is only done once as the weights are then cached on you drive.",
"Thankyou so much!",
"@laibamehnaz have you solved the problem? I have a similar problem.",
"Hi,\r\nI am facing a similar issue, can anyone help with this? ",
... | 1,547 | 1,649 | 1,547 | NONE | null | I have been trying to executing this code :
import torch
from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM
# Load pre-trained model tokenizer (vocabulary)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Tokenized input
text = "Who was Jim Henson ? Jim Henson wa... | {
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https://api.github.com/repos/huggingface/transformers/issues/197 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/197/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/197/comments | https://api.github.com/repos/huggingface/transformers/issues/197/events | https://github.com/huggingface/transformers/issues/197 | 399,627,937 | MDU6SXNzdWUzOTk2Mjc5Mzc= | 197 | seems meet the GPU memory leak problem | {
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"Maybe use the `torch.no_grad()` context-manager which is the recommended way to perform inference with PyTorch now?\r\nSee https://pytorch.org/docs/stable/autograd.html#torch.autograd.no_grad",
"Closing this. Feel free to re-open if the issue is still there.",
"Hey there, I also have some memory leak problem w... | 1,547 | 1,680 | 1,548 | NONE | null | I wrap the ``BertModel'' as a persistent object and init it once, then iteratively use it as the feature extractor to generate the feature of data batch, while it seems I met the GPU memory leak problem. After starting the program, the GPU memory usage keeps increasing until 'out-of-memory'. Some key codes are as follo... | {
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https://api.github.com/repos/huggingface/transformers/issues/196 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/196/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/196/comments | https://api.github.com/repos/huggingface/transformers/issues/196/events | https://github.com/huggingface/transformers/issues/196 | 399,155,566 | MDU6SXNzdWUzOTkxNTU1NjY= | 196 | TODO statement on Question/Answering Model | {
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"Not really, I've moved to something else since I don't expect this to change significantly the results.\r\nI will remove the TODO."
] | 1,547 | 1,547 | 1,547 | NONE | null | Has this been confirmed?
https://github.com/huggingface/pytorch-pretrained-BERT/blob/647c98353090ee411e1ef9016b2a458becfe36f9/pytorch_pretrained_bert/modeling.py#L1084 | {
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https://api.github.com/repos/huggingface/transformers/issues/195 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/195/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/195/comments | https://api.github.com/repos/huggingface/transformers/issues/195/events | https://github.com/huggingface/transformers/issues/195 | 398,799,873 | MDU6SXNzdWUzOTg3OTk4NzM= | 195 | Potentially redundant learning rate scheduling | {
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"Humm could be the case indeed. What do think about this @tholor?",
"As far as I can tell this was introduced in c8ea286048517d9072397d77f4de21b8483a4531 as a byproduct of adding float16 support, and was then copied to other example files as well.",
"I agree, there seems to be double LR scheduling. The applied ... | 1,547 | 1,549 | 1,549 | NONE | null | In the following two code snippets below:
https://github.com/huggingface/pytorch-pretrained-BERT/blob/647c98353090ee411e1ef9016b2a458becfe36f9/examples/run_lm_finetuning.py#L570-L573
https://github.com/huggingface/pytorch-pretrained-BERT/blob/647c98353090ee411e1ef9016b2a458becfe36f9/examples/run_lm_finetuning.py#... | {
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https://api.github.com/repos/huggingface/transformers/issues/194 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/194/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/194/comments | https://api.github.com/repos/huggingface/transformers/issues/194/events | https://github.com/huggingface/transformers/issues/194 | 398,771,339 | MDU6SXNzdWUzOTg3NzEzMzk= | 194 | run_classifier.py doesn't save any configurations and I can't load the trained model. | {
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"I trained BertForSequenceClassification model with cola dataset mode for binary classification. It saved only eval_results.txt and pytorch_model.bin files. When I am loading model again like:\r\nmodel = BertForSequenceClassification.from_pretrained('models/') \r\nit produces such error:\r\nwith open(json_file, \"r... | 1,547 | 1,547 | 1,547 | NONE | null | {
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https://api.github.com/repos/huggingface/transformers/issues/193 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/193/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/193/comments | https://api.github.com/repos/huggingface/transformers/issues/193/events | https://github.com/huggingface/transformers/pull/193 | 398,747,903 | MDExOlB1bGxSZXF1ZXN0MjQ0MzM3MDg0 | 193 | Fix importing unofficial TF models | {
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"Thanks !"
] | 1,547 | 1,596 | 1,547 | CONTRIBUTOR | null | Importing unofficial TF models seems to be working well, at least for me.
This PR resolves #50. | {
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https://api.github.com/repos/huggingface/transformers/issues/192 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/192/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/192/comments | https://api.github.com/repos/huggingface/transformers/issues/192/events | https://github.com/huggingface/transformers/pull/192 | 398,671,006 | MDExOlB1bGxSZXF1ZXN0MjQ0Mjg3NDk3 | 192 | Documentation Fixes | {
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"Closing this as I'm not sure it's right."
] | 1,547 | 1,547 | 1,547 | NONE | null | Fixes misnamed documentation comments in `run_squad.py` and `run_squad2.py` and update of `README.md` for updated syntax of file conversion. | {
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https://api.github.com/repos/huggingface/transformers/issues/191 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/191/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/191/comments | https://api.github.com/repos/huggingface/transformers/issues/191/events | https://github.com/huggingface/transformers/pull/191 | 398,655,399 | MDExOlB1bGxSZXF1ZXN0MjQ0Mjc3Njk3 | 191 | lm_finetuning compatibility with Python 3.5 | {
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"Great, thank! Nice to have Python 3.5 compatibility again here!"
] | 1,547 | 1,596 | 1,547 | CONTRIBUTOR | null | dicts are not ordered in Python 3.5 or prior, which is a cause of #175.
This PR replaces one with a list, to keep its order. | {
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https://api.github.com/repos/huggingface/transformers/issues/190 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/190/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/190/comments | https://api.github.com/repos/huggingface/transformers/issues/190/events | https://github.com/huggingface/transformers/pull/190 | 398,655,261 | MDExOlB1bGxSZXF1ZXN0MjQ0Mjc3NjA5 | 190 | Fix documentation (missing backslashes) | {
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"Great, thanks!"
] | 1,547 | 1,596 | 1,547 | CONTRIBUTOR | null | This PR adds missing backslashes in LM Fine-tuning subsection in README.md. | {
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https://api.github.com/repos/huggingface/transformers/issues/189 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/189/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/189/comments | https://api.github.com/repos/huggingface/transformers/issues/189/events | https://github.com/huggingface/transformers/pull/189 | 398,649,768 | MDExOlB1bGxSZXF1ZXN0MjQ0Mjc0MTcz | 189 | [bug fix] args.do_lower_case is always True | {
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"Thanks @donglixp!"
] | 1,547 | 1,547 | 1,547 | CONTRIBUTOR | null | The "default=True" makes args.do_lower_case always True.
```python
parser.add_argument("--do_lower_case",
default=True,
action='store_true')
``` | {
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https://api.github.com/repos/huggingface/transformers/issues/188 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/188/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/188/comments | https://api.github.com/repos/huggingface/transformers/issues/188/events | https://github.com/huggingface/transformers/issues/188 | 398,588,638 | MDU6SXNzdWUzOTg1ODg2Mzg= | 188 | Weight Decay Fix Original Paper | {
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"Yes"
] | 1,547 | 1,547 | 1,547 | NONE | null | Hi There!
Is the weight decay fix from?
https://arxiv.org/abs/1711.05101
Thanks! | {
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"Hi, I don't think you can do that in a clean way, sorry. That how BERT is trained.",
"> That how BERT is trained\r\n\r\ni was pretrained our **bert-base-uncased** model with own dataset. \r\nBatch_size=32\r\nmax_seq_length=128\r\n\r\n> I don't think you can do that in a clean way\r\n\r\nyou asked me \"what was t... | 1,547 | 1,547 | 1,547 | NONE | null | ```
----------------------------------> how much belan i havin my credit card and also debitcard
----------------------------------> ['how', 'much', 'belan', 'i', 'havin', 'my', 'credit', 'card', 'and', 'also', 'debitcard']
----------------------------------> ['**belan**', '**havin**']
-----------------------------... | {
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https://api.github.com/repos/huggingface/transformers/issues/186 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/186/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/186/comments | https://api.github.com/repos/huggingface/transformers/issues/186/events | https://github.com/huggingface/transformers/issues/186 | 398,229,727 | MDU6SXNzdWUzOTgyMjk3Mjc= | 186 | BertOnlyMLMHead is a duplicate of BertLMPredictionHead | {
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"That's an heritage of how I converted the TF code (by reproducing the scope architecture in TF with PyTorch classes). We can't really change that now without re-converting all the TF code.\r\nIf you want a more concise version of PyTorch BERT, you can check [pytorchic-bert](https://github.com/dhlee347/pytorchic-be... | 1,547 | 1,547 | 1,547 | NONE | null | https://github.com/huggingface/pytorch-pretrained-BERT/blob/35becc6d84f620c3da48db460d6fb900f2451782/pytorch_pretrained_bert/modeling.py#L387-L394
I don't understand how it is useful to wrap the BertLMPredictionHead class like that, perhaps it was forgotten in some refactoring ? I can do a PR if you confirm me it ca... | {
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https://api.github.com/repos/huggingface/transformers/issues/185 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/185/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/185/comments | https://api.github.com/repos/huggingface/transformers/issues/185/events | https://github.com/huggingface/transformers/issues/185 | 398,218,741 | MDU6SXNzdWUzOTgyMTg3NDE= | 185 | got an unexpected keyword argument 'cache_dir' | {
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"You should update to the latest version of `pytorch_pretrained_bert`(`pip install pytorch_pretrained_bert --upgrade`)"
] | 1,547 | 1,592 | 1,547 | NONE | null | I used the following code to run the job: `
export GLUE_DIR=./data
python3 run_classifier.py \
--task_name COLA \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/ \
--bert_model bert-large-uncased \
--max_seq_length 20 \
--train_batch_size 10 \
--learning_rate 2e-5 \
--... | {
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https://api.github.com/repos/huggingface/transformers/issues/184 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/184/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/184/comments | https://api.github.com/repos/huggingface/transformers/issues/184/events | https://github.com/huggingface/transformers/issues/184 | 398,208,606 | MDU6SXNzdWUzOTgyMDg2MDY= | 184 | Python 3.5 + Torch 1.0 does not work | {
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"Thank you @yuhui-zh15 sir. i will check.",
"This should be fixed on master now (thanks to #191 )"
] | 1,547 | 1,547 | 1,547 | NONE | null | When running `run_lm_finetuning.py` to fine-tune language model with default settings (see command below), sometimes I could run successfully, but sometimes I received different errors like `RuntimeError: The size of tensor a must match the size of tensor b at non-singleton dimension 1`, `RuntimeError: Creating MTGP c... | {
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https://api.github.com/repos/huggingface/transformers/issues/183 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/183/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/183/comments | https://api.github.com/repos/huggingface/transformers/issues/183/events | https://github.com/huggingface/transformers/pull/183 | 398,173,731 | MDExOlB1bGxSZXF1ZXN0MjQzOTM4OTc1 | 183 | Adding OpenAI GPT pre-trained model | {
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"#254 is now the main PR for the inclusion of OpenAI GPT. Closing this PR."
] | 1,547 | 1,549 | 1,549 | MEMBER | null | Adding OpenAI GPT pretrained model. | {
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https://api.github.com/repos/huggingface/transformers/issues/182 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/182/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/182/comments | https://api.github.com/repos/huggingface/transformers/issues/182/events | https://github.com/huggingface/transformers/pull/182 | 398,166,198 | MDExOlB1bGxSZXF1ZXN0MjQzOTMzOTkz | 182 | add do_lower_case arg and adjust model saving for lm finetuning. | {
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https://api.github.com/repos/huggingface/transformers/issues/181 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/181/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/181/comments | https://api.github.com/repos/huggingface/transformers/issues/181/events | https://github.com/huggingface/transformers/issues/181 | 398,148,589 | MDU6SXNzdWUzOTgxNDg1ODk= | 181 | All about the training speed in classification job | {
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"Maybe try to use a bigger batch size or try fp16 training?\r\nPlease refer to the [detailed instructions in the readme](https://github.com/huggingface/pytorch-pretrained-BERT#examples)."
] | 1,547 | 1,547 | 1,547 | NONE | null | I run the bert-base-uncased model with task 'mrpc' in ubuntu,nvidia p4000 8G.
It's a classification problem, and I use the default demo data.
But the training speed is about 2 batch every second. Any problem?
I think it maybe too slow, but can not find why. I have another task with 1300000 data costs 6 hours per ep... | {
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https://api.github.com/repos/huggingface/transformers/issues/180 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/180/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/180/comments | https://api.github.com/repos/huggingface/transformers/issues/180/events | https://github.com/huggingface/transformers/issues/180 | 398,143,878 | MDU6SXNzdWUzOTgxNDM4Nzg= | 180 | Weights not initialized from pretrained model | {
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"Hi!\r\n\r\nThose messages are correct, the pretrained weights that have been released by Google Brain are just the ones of the core network. They did not release task specific weights. To get a model that solves a specific classification task, you would have to train one yourself or get it from someone else.\r\n\r... | 1,547 | 1,547 | 1,547 | NONE | null | Thanks for your awesome work!
When I execute the following code for a named entity recognition tasks:
`model = BertForTokenClassification.from_pretrained("bert-base-uncased", num_labels=num_labels)`
Output the following information:
> Weights of BertForTokenClassification not initialized from pretrained model... | {
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https://api.github.com/repos/huggingface/transformers/issues/179 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/179/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/179/comments | https://api.github.com/repos/huggingface/transformers/issues/179/events | https://github.com/huggingface/transformers/pull/179 | 397,817,028 | MDExOlB1bGxSZXF1ZXN0MjQzNjc1ODgw | 179 | Fix it to run properly even if without `--do_train` param. | {
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"Thanks!"
] | 1,547 | 1,547 | 1,547 | CONTRIBUTOR | null | It was modified similar to `run_classifier.py`, and Fixed to run properly even if without `--do_train` param. | {
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https://api.github.com/repos/huggingface/transformers/issues/178 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/178/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/178/comments | https://api.github.com/repos/huggingface/transformers/issues/178/events | https://github.com/huggingface/transformers/issues/178 | 397,703,107 | MDU6SXNzdWUzOTc3MDMxMDc= | 178 | Can we use BERT for Punctuation Prediction? | {
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"Hi, I don't really now. I guess you should just give it a try."
] | 1,547 | 1,547 | 1,547 | NONE | null | Can we use the pre-trained BERT model for Punctuation Prediction for Conversational Speech? Let say punctuating an ASR output? | {
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https://api.github.com/repos/huggingface/transformers/issues/177 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/177/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/177/comments | https://api.github.com/repos/huggingface/transformers/issues/177/events | https://github.com/huggingface/transformers/issues/177 | 397,673,308 | MDU6SXNzdWUzOTc2NzMzMDg= | 177 | run_lm_finetuning.py does not define a do_lower_case argument | {
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"On a related note: I see there is learning rate scheduling happening [here](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_lm_finetuning.py#L608), but also inside the BertAdam class. Is this not redundant and erroneous? For reference I'm not using FP16 training, which has its own s... | 1,547 | 1,547 | 1,547 | NONE | null | The file references `args.do_lower_case`, but doesn't have the corresponding `parser.add_argument` call.
As an aside, has anyone successfully applied LM fine-tuning for a downstream task (using this code, or maybe using the original tensorflow implementation)? I'm not even sure if the code will run in its current st... | {
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https://api.github.com/repos/huggingface/transformers/issues/176 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/176/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/176/comments | https://api.github.com/repos/huggingface/transformers/issues/176/events | https://github.com/huggingface/transformers/issues/176 | 397,286,604 | MDU6SXNzdWUzOTcyODY2MDQ= | 176 | Add [CLS] and [SEP] tokens in Usage | {
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"You are right, I'll fix the readme",
"So, just to clarify, I should add '[CLS]' and '[SEP]' to the beginning and end of each utterance respectively, and it's a bug in the examples that they dont do this?",
"@hughperkins did you get any clarification on this?"
] | 1,547 | 1,566 | 1,547 | CONTRIBUTOR | null | Thank you for this great job.
In the Usage section, the `[CLS]` and `[SEP]` tokens should be added in the beginning and ending of `tokenized_text`?
```
# Tokenized input
text = "Who was Jim Henson ? Jim Henson was a puppeteer"
tokenized_text = tokenizer.tokenize(text)
```
In the current example, if the fi... | {
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https://api.github.com/repos/huggingface/transformers/issues/175 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/175/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/175/comments | https://api.github.com/repos/huggingface/transformers/issues/175/events | https://github.com/huggingface/transformers/issues/175 | 397,243,635 | MDU6SXNzdWUzOTcyNDM2MzU= | 175 | RuntimeError: Dimension out of range (expected to be in range of [-1, 0], but got 1) | {
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"Sir how to resolve this? i am beginner for pytorch. \r\nThanks.",
"I will have a look, I am not familiar with `run_lm_finetuning` yet.\r\nIn the meantime maybe @tholor has an advice?",
"Haven't seen this error before, but how does your training corpus \"vocab007.txt\" look like? Is training working successfull... | 1,547 | 1,547 | 1,547 | NONE | null | sir i was pretrained for our BERT-Base model for Multi-GPU training 8 GPUs. preprocessing succeed but next step training it shown error. in run_lm_finetuning.py.
--
`python3 run_lm_finetuning.py --bert_model bert-base-uncased --do_train --train_file vocab007.txt --output_dir models --num_train_epochs 5.0 --learning_r... | {
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https://api.github.com/repos/huggingface/transformers/issues/174 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/174/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/174/comments | https://api.github.com/repos/huggingface/transformers/issues/174/events | https://github.com/huggingface/transformers/pull/174 | 397,138,625 | MDExOlB1bGxSZXF1ZXN0MjQzMTU2ODg5 | 174 | Added Squad 2.0 | {
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"Great, thanks @abeljim.\r\nDo you have the associated results when you run this command?",
"python run_squad2.py \\\r\n --bert_model bert-large-uncased \\\r\n --do_train \\\r\n --do_predict \\\r\n --do_lower_case \\\r\n --train_file $SQUAD_DIR/train-v2.0.json \\\r\n --predict_file $SQUAD_DIR/dev-v2.0.json ... | 1,546 | 1,550 | 1,547 | CONTRIBUTOR | null | Accidentally closed the last pull request. Created Separate file for Squad 2.0 run with
python3 run_squad.py
--bert_model bert-large-uncased_up
--do_predict
--do_lower_case
--train_file squad/train-v2.0.json
--predict_file squad/dev-v2.0.json
--learning_rate 3e-5
--num_train_epochs 2
--max_seq_length ... | {
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https://api.github.com/repos/huggingface/transformers/issues/173 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/173/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/173/comments | https://api.github.com/repos/huggingface/transformers/issues/173/events | https://github.com/huggingface/transformers/issues/173 | 396,776,254 | MDU6SXNzdWUzOTY3NzYyNTQ= | 173 | What 's the mlm accuracy of pretrained model? | {
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"Hi, we didn't evaluate this metric. If you do feel free to share the results.\r\nRegarding the comparison between the Google and PyTorch implementations, please refere to the included Notebooks and the associated section of the readme."
] | 1,546 | 1,546 | 1,546 | NONE | null | What 's the mlm accuracy of pretrained model? In my case, I find the scores of candidate in top 10 are very closeοΌbut most are not suitable. Is this the same prediction as Google's original project?
_Originally posted by @l126t in https://github.com/huggingface/pytorch-pretrained-BERT/issues/155#issuecomment-452195... | {
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https://api.github.com/repos/huggingface/transformers/issues/172 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/172/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/172/comments | https://api.github.com/repos/huggingface/transformers/issues/172/events | https://github.com/huggingface/transformers/pull/172 | 396,731,874 | MDExOlB1bGxSZXF1ZXN0MjQyODQzOTI4 | 172 | Never split some texts. | {
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"Please have a look. :)",
"Looks good indeed, thanks @WrRan!"
] | 1,546 | 1,548 | 1,547 | CONTRIBUTOR | null | I have noticed bert tokenize texts by two steps:
1. punctuation: split text to tokens
2. wordpiece: split token to word_pieces
Some texts such as `"[UNK]"` are supposed to be left as they are. However, they become `["[", "UNK", "]"]` or something like this.
This PR is to solve the above problem. | {
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https://api.github.com/repos/huggingface/transformers/issues/171 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/171/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/171/comments | https://api.github.com/repos/huggingface/transformers/issues/171/events | https://github.com/huggingface/transformers/pull/171 | 396,382,404 | MDExOlB1bGxSZXF1ZXN0MjQyNTc4NjA0 | 171 | LayerNorm initialization | {
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"Yes, the weights are overwritten by the loading model. But if the code is used to pretrain a model from scratch, it might affect performance. (related issue: https://github.com/huggingface/pytorch-pretrained-BERT/issues/143 )",
"Great, thanks @donglixp !"
] | 1,546 | 1,546 | 1,546 | CONTRIBUTOR | null | The LayerNorm gamma and beta should be initialized by .fill_(1.0) and .zero_().
reference links:
https://github.com/tensorflow/tensorflow/blob/989e78c412a7e0f5361d4d7dfdfb230c8136e749/tensorflow/contrib/layers/python/layers/layers.py#L2298
https://github.com/tensorflow/tensorflow/blob/989e78c412a7e0f5361d4d7df... | {
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https://api.github.com/repos/huggingface/transformers/issues/170 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/170/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/170/comments | https://api.github.com/repos/huggingface/transformers/issues/170/events | https://github.com/huggingface/transformers/issues/170 | 396,375,768 | MDU6SXNzdWUzOTYzNzU3Njg= | 170 | How to pretrain my own data with this pytorch code? | {
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"Sir @Gpwner i think you have to refer to do pretrain for google/bert repo( https://github.com/google-research/bert#pre-training-with-bert ) and then convert tensorflow model as pytorch.",
"A pre-training script is now included in `master` thanks to @tholor's PR #124 ",
"> A pre-training script is now included... | 1,546 | 1,546 | 1,546 | NONE | null | I wonder how to pretrain with my own data. | {
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https://api.github.com/repos/huggingface/transformers/issues/169 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/169/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/169/comments | https://api.github.com/repos/huggingface/transformers/issues/169/events | https://github.com/huggingface/transformers/pull/169 | 396,300,026 | MDExOlB1bGxSZXF1ZXN0MjQyNTIwODEw | 169 | Update modeling.py to fix typo | {
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"Thanks @ichn-hu this issue was resolved in a previous PR."
] | 1,546 | 1,546 | 1,546 | NONE | null | Fix typo in the documentation for the description of not using masked_lm_labels | {
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https://api.github.com/repos/huggingface/transformers/issues/168 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/168/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/168/comments | https://api.github.com/repos/huggingface/transformers/issues/168/events | https://github.com/huggingface/transformers/issues/168 | 396,232,776 | MDU6SXNzdWUzOTYyMzI3NzY= | 168 | Cannot reproduce the result of run_squad 1.1 | {
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"I can reproduce the results, learning rate is 3e-5 , epoch is 2.0",
"by using fp16, the f1 is 90.8 ",
"> by using fp16, the f1 is 90.8\r\n\r\nSo the key is set fp16 is True?",
"@hmt2014 could you give the exact command line that you use to train your model?",
"Yes, please use the command line example indic... | 1,546 | 1,546 | 1,546 | NONE | null | I train 5 epochs with learning rate 5e-5, but my evaluation result is {'exact_match': 32.04351939451277, 'f1': 36.53574674513405}.
What is the problem? | {
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https://api.github.com/repos/huggingface/transformers/issues/167 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/167/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/167/comments | https://api.github.com/repos/huggingface/transformers/issues/167/events | https://github.com/huggingface/transformers/issues/167 | 396,141,181 | MDU6SXNzdWUzOTYxNDExODE= | 167 | Question about hidden layers from pretained model | {
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"Yes you are right. The first value returned is the output for `BertEncoder.forward`.\r\n\r\nhttps://github.com/huggingface/pytorch-pretrained-BERT/blob/8da280ebbeca5ebd7561fd05af78c65df9161f92/pytorch_pretrained_bert/modeling.py#L623-L634 "
] | 1,546 | 1,546 | 1,546 | NONE | null | In the example shown to get hidden states https://github.com/huggingface/pytorch-pretrained-BERT#usage
I want to confirm - the final hidden layer corresponds to the last element of `encoded_layers`, right? | {
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https://api.github.com/repos/huggingface/transformers/issues/166 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/166/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/166/comments | https://api.github.com/repos/huggingface/transformers/issues/166/events | https://github.com/huggingface/transformers/pull/166 | 396,125,596 | MDExOlB1bGxSZXF1ZXN0MjQyNDE1MzY5 | 166 | Fix error when `bert_model` param is path or url. | {
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"Ok, looks good to me, thanks @likejazz "
] | 1,546 | 1,546 | 1,546 | CONTRIBUTOR | null | Error occurs when `bert_model` param is path or url. Therefore, if it is path, specify the last path to prevent error. | {
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https://api.github.com/repos/huggingface/transformers/issues/165 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/165/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/165/comments | https://api.github.com/repos/huggingface/transformers/issues/165/events | https://github.com/huggingface/transformers/pull/165 | 395,972,623 | MDExOlB1bGxSZXF1ZXN0MjQyMjk5MTM2 | 165 | fixed model names in help string | {
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"Hi Oliver,\r\n\r\nI've already created a pull request which fixes this problem for all the example files (#156).\r\n\r\nCheers!",
"Ok, great."
] | 1,546 | 1,546 | 1,546 | CONTRIBUTOR | null | Set correct model names according to modeling.py | {
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"All the code related to word embeddings is located there https://github.com/huggingface/pytorch-pretrained-BERT/blob/8da280ebbeca5ebd7561fd05af78c65df9161f92/pytorch_pretrained_bert/modeling.py#L172-L200\r\n\r\nIf you want to access pretrained embeddings, the easier thing to do would be to load a pretrained model ... | 1,546 | 1,546 | 1,546 | NONE | null | is the pretrained model downloaded include word embedding?
I do not see any embedding in your code
please | {
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"Hi, I came across this error after running `import pytorch_pretrained_bert`. My configurations are as follows:\r\ntorch version 1.0.0\r\npython version 3.6\r\ncuda 9.2",
"I uninstalled the old version of apex and reinstalled a new version. It worked. Thanks.\r\n\r\ngit clone https://www.github.com/nvidia/apex\r\... | 1,546 | 1,590 | 1,546 | NONE | null | ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-1-ee86003eab97> in <module>()
----> 1 from pytorch_pretrained_bert import BertTokenizer
/opt/conda/envs/py3/lib/python3.6/site-packages/pytorch_pre... | {
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ref #1 | {
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"@thomwolf Can you please reply here. This issue is different from issue#160\r\n#160 is for training mode, this issue is for prediction mode. ( I hope prediction can run on CPU)",
"Those messages are correct, the pretrained weights that have been released by Google Brain are just the ones of the core network. The... | 1,546 | 1,548 | 1,546 | NONE | null | I am running following in just prediction mode:
`(berttorch363) sandeepbhutani304@pytorch-bert-2:~/pytorch-pretrained-BERT/examples$ python run_squad.py --bert_model bert-large-uncased --do_predict --do_lower_case --predict_file $SQUAD_DIR/dev-v1.1_sand.json --train_batch_size 12 --learning_rate 3e-5 -... | {
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"What kind of GPU are you using?",
"I am on CPU as of now\r\n\r\n```\r\n(berttorch363) sandeepbhutani304@pytorch-bert-2:~/pytorch-pretrained-BERT/examples$ lscpu\r\nArchitecture: x86_64\r\nCPU op-mode(s): 32-bit, 64-bit\r\nByte Order: Little Endian\r\nCPU(s): 2\r\nOn-line... | 1,546 | 1,591 | 1,546 | NONE | null | Trying to run cloned code from git but not able to train. Please suggest
`python run_squad.py --bert_model bert-base-uncased --do_train --do_predict --do_lower_case --train_file $SQUAD_DIR/train-v1.1.json --predict_file $SQUAD_DIR/dev-v1.1_sand.json --train_batch_size 12 --learning_rate 3e-5 --num_... | {
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https://api.github.com/repos/huggingface/transformers/issues/159 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/159/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/159/comments | https://api.github.com/repos/huggingface/transformers/issues/159/events | https://github.com/huggingface/transformers/pull/159 | 395,535,069 | MDExOlB1bGxSZXF1ZXN0MjQxOTY1NTU4 | 159 | Allow do_eval to be used without do_train and to use the pretrained model in the output folder | {
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"Indeed!"
] | 1,546 | 1,546 | 1,546 | CONTRIBUTOR | null | If you wanted to use the pre-trained model to redo evaluation without training, it errors because the output directory already exists (the output directory that contains the pre-trained model that one might like to evaluate).
Additionally, a couple of fields are not initialised if one does not train and only evalua... | {
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https://api.github.com/repos/huggingface/transformers/issues/158 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/158/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/158/comments | https://api.github.com/repos/huggingface/transformers/issues/158/events | https://github.com/huggingface/transformers/issues/158 | 395,517,012 | MDU6SXNzdWUzOTU1MTcwMTI= | 158 | AttributeError: 'BertForPreTraining' object has no attribute 'global_step' | {
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"I have the same issue",
"Did you find any solution?\r\n",
"I have the same issue too"
] | 1,546 | 1,596 | 1,546 | NONE | null | @thomwolf sir, i am also same issue (https://github.com/huggingface/pytorch-pretrained-BERT/issues/50#issuecomment-440624216). it doen't resolve. how i am convert my finetuned pretrained model to pytorch?
```
export BERT_BASE_DIR=/home/dell/backup/NWP/bert-base-uncased/bert_tensorflow_e100
pytorch_pretrained_be... | {
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https://api.github.com/repos/huggingface/transformers/issues/157 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/157/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/157/comments | https://api.github.com/repos/huggingface/transformers/issues/157/events | https://github.com/huggingface/transformers/issues/157 | 395,255,132 | MDU6SXNzdWUzOTUyNTUxMzI= | 157 | Is it feasible to set num_workers>=1 in DataLoader to quickly load data? | {
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"Are you asking if it possible or do you want this change included to the code?\r\n\r\nI don't see why this change would cause a problem, if we choose to implement it we should add a command line argument to specify this value.",
"Yes, feel free to submit a PR if you have a working implementation.",
"@thomwolf ... | 1,546 | 1,591 | 1,546 | NONE | null | {
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https://api.github.com/repos/huggingface/transformers/issues/156 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/156/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/156/comments | https://api.github.com/repos/huggingface/transformers/issues/156/events | https://github.com/huggingface/transformers/pull/156 | 395,241,547 | MDExOlB1bGxSZXF1ZXN0MjQxNzQyMDE3 | 156 | Adding new pretrained model to the help of the `bert_model` argument. | {
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"Thanks Gregory!"
] | 1,546 | 1,546 | 1,546 | CONTRIBUTOR | null | The help for the `bert_model` command line argument has not been updated in the examples files. | {
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https://api.github.com/repos/huggingface/transformers/issues/155 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/155/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/155/comments | https://api.github.com/repos/huggingface/transformers/issues/155/events | https://github.com/huggingface/transformers/issues/155 | 394,870,891 | MDU6SXNzdWUzOTQ4NzA4OTE= | 155 | Why not the mlm use the information of adjacent sentences? | {
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"The model is already using adjacent sentences to make its predictions, it just happens to be wrong in your case.\r\n\r\nIf you would like to make it choose from a specific list of words, you could use the code that I mentionned in #80. ",
"Thanks @rodgzilla !",
"What 's the mlm accuracy of pretrained model? I ... | 1,546 | 1,546 | 1,546 | NONE | null |
I prepare two sentences for mlm predict the mask part:"Tom cant run fast. He [mask] his back a few years ago." The result of model (uncased base) is 'got'. That is meaningless. Obviously ,"hurt" is better.
I wander how to make mlm to use the information of adjacent sentences. | {
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https://api.github.com/repos/huggingface/transformers/issues/154 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/154/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/154/comments | https://api.github.com/repos/huggingface/transformers/issues/154/events | https://github.com/huggingface/transformers/issues/154 | 394,865,030 | MDU6SXNzdWUzOTQ4NjUwMzA= | 154 | the run_squad report "for training,each question should exactly have 1 answer" when I tried to fintune bert on squad2.0 | {
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"This is still not supported but should be soon. You can follow/try this PR by @abeljim here:\r\n\r\nhttps://github.com/huggingface/pytorch-pretrained-BERT/pull/152",
"This is now on master"
] | 1,546 | 1,547 | 1,547 | NONE | null | What is the command to reproduce the results of squad2.0 reported in the BERT.
Thanks~ | {
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https://api.github.com/repos/huggingface/transformers/issues/152 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/152/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/152/comments | https://api.github.com/repos/huggingface/transformers/issues/152/events | https://github.com/huggingface/transformers/pull/152 | 394,759,764 | MDExOlB1bGxSZXF1ZXN0MjQxNDIzMzk2 | 152 | Squad 2.0 | {
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"The new run_squad.py can train and predict , but can't predict only.\r\n\r\n\r\n\r\n",
"in the predict only mode, len(nbest) is always 1",
"my scripts for predicting is \r\n\r\n\r\npython3 run_squad.py... | 1,546 | 1,572 | 1,546 | CONTRIBUTOR | null | Added Squad 2.0 support. It has been tested on Bert large with a null threshold of zero with the result of
{
"exact": 75.26320222353239,
"f1": 78.41636742280099,
"total": 11873,
"HasAns_exact": 74.51079622132254,
"HasAns_f1": 80.82616909765808,
"HasAns_total": 5928,
"NoAns_exact": 76.0134566862910... | {
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https://api.github.com/repos/huggingface/transformers/issues/151 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/151/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/151/comments | https://api.github.com/repos/huggingface/transformers/issues/151/events | https://github.com/huggingface/transformers/issues/151 | 394,673,351 | MDU6SXNzdWUzOTQ2NzMzNTE= | 151 | Using large model with fp16 enable causes the server down | {
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"Could you give more informations such as the command that you are using to run the model and the batch size that you are using? Have you tried reducing it?",
"Hi, @hguan6 try to adjust the batch size and use gradient accumulation (see [this section](https://github.com/huggingface/pytorch-pretrained-BERT#training... | 1,546 | 1,546 | 1,546 | NONE | null | I am using a server with Ubuntu 16.04 and 4 TITAN X GPUs. The server runs the base model with no problems. But it cannot run the large model with 32-bit float point, so I enabled fp16, and the server went down.
(When I successfully ran the base model, it consumes 8G GPU memory for each of the 4 GPUS. ) | {
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"Hi @tholor, apex is a GPU specific extension.\r\nWhat kind of use-case do you have in which you have apex installed but no GPU (also fp16 doesn't work on CPU, it's not supported on PyTorch currently)?",
"The two cases I came across this: \r\n1) testing if some code works for both GPU and CPU (on a GPU machine wi... | 1,545 | 1,561 | 1,561 | CONTRIBUTOR | null | I am running into an exception when loading a model on CPU in one of the example scripts. I suppose this is related to loading the FusedLayerNorm from apex, even when `--no_cuda` has been set.
https://github.com/huggingface/pytorch-pretrained-BERT/blob/8da280ebbeca5ebd7561fd05af78c65df9161f92/pytorch_pretrained_bert/... | {
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https://api.github.com/repos/huggingface/transformers/issues/149 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/149/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/149/comments | https://api.github.com/repos/huggingface/transformers/issues/149/events | https://github.com/huggingface/transformers/issues/149 | 394,507,967 | MDU6SXNzdWUzOTQ1MDc5Njc= | 149 | Speedup using NVIDIA Apex | {
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"Maybe. You can try with pytorch docker image `dockerhub 1.0-cuda10.0-cudnn7` to debug, as we did in the discussion in PR #116.",
"Just verified that CUDA10.0 makes 4x speedup. It should be better to include this in the main document.",
"What GPU do you run ... and how do you increase such a speedup? Is this po... | 1,545 | 1,563 | 1,546 | CONTRIBUTOR | null | Hi,
According to PR https://github.com/huggingface/pytorch-pretrained-BERT/pull/116, we should be able to achieve a 3-4 x speed up for both bert-base and bert-large. However, I can only achieve 2x speed up with bert-base. My docker image uses CUDA9.0 while the discussion in the PR https://github.com/huggingface/pyt... | {
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https://api.github.com/repos/huggingface/transformers/issues/148 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/148/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/148/comments | https://api.github.com/repos/huggingface/transformers/issues/148/events | https://github.com/huggingface/transformers/issues/148 | 394,310,682 | MDU6SXNzdWUzOTQzMTA2ODI= | 148 | Embeddings from BERT for original tokens | {
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"Hi, you should read the discussion in #64. I left this issue open for reference on these questions.\r\nDon't hesitate to participate there."
] | 1,545 | 1,545 | 1,545 | NONE | null | I am trying out the `extract_features.py` example program. I noticed that a sentence gets split into tokens and the embeddings are generated. For example, if you had the sentence βDefinitely notβ, and the corresponding workpieces can be [βDefβ, β##inβ, β##iteβ, β##lyβ, βnotβ]. It then generates the embeddings for thes... | {
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https://api.github.com/repos/huggingface/transformers/issues/147 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/147/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/147/comments | https://api.github.com/repos/huggingface/transformers/issues/147/events | https://github.com/huggingface/transformers/issues/147 | 394,064,499 | MDU6SXNzdWUzOTQwNjQ0OTk= | 147 | Does the final hidden state contains the <CLS> for Squad2.0 | {
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"I'm sorry that I have found a bug in my code. I have invalidly called a attribute of the `InputFeature` but it have run successfully. Now I have fixed it and re-run it. If I have more questions I will reopen this. Sorry to bother you!"
] | 1,545 | 1,545 | 1,545 | NONE | null | Recently I'm modifying the `run_squad.py` to run on CoQA. In the implementation of TensorFlow from Google, they use the probability on the first token of a context segment, where is the location of `<CLS>` to as the that of the question is unanswerable. So I try to modified the `run_squad.py` in your implementation as ... | {
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https://api.github.com/repos/huggingface/transformers/issues/146 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/146/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/146/comments | https://api.github.com/repos/huggingface/transformers/issues/146/events | https://github.com/huggingface/transformers/issues/146 | 393,876,320 | MDU6SXNzdWUzOTM4NzYzMjA= | 146 | BertForQuestionAnswering: Predicting span on the question? | {
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"This is the original behavior from the TF implementation.\r\nThe predictions are filtered afterward (in `write_predictions`) so this is probably not a big issue.\r\nMaybe try with another behavior and see if it improve upon the results?"
] | 1,545 | 1,545 | 1,545 | NONE | null | Hello,
I have a question regarding the `BertForQuestionAnswering` implementation. If I am not mistaken, for this model the sequence should be of the form `Question tokens [SEP] Passage tokens`. Therefore, the embedded representation computed by `BertModel` returns the states of both the question and the passage (a t... | {
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https://api.github.com/repos/huggingface/transformers/issues/145 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/145/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/145/comments | https://api.github.com/repos/huggingface/transformers/issues/145/events | https://github.com/huggingface/transformers/pull/145 | 393,669,641 | MDExOlB1bGxSZXF1ZXN0MjQwNjMyNjY5 | 145 | Correct the wrong note | {
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"Thanks!"
] | 1,545 | 1,546 | 1,546 | CONTRIBUTOR | null | Correct the wrong note in #144 | {
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https://api.github.com/repos/huggingface/transformers/issues/144 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/144/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/144/comments | https://api.github.com/repos/huggingface/transformers/issues/144/events | https://github.com/huggingface/transformers/issues/144 | 393,669,200 | MDU6SXNzdWUzOTM2NjkyMDA= | 144 | Some questions in Loss Function for MaskedLM | {
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"@julien-c Seem some conflict with the original BERT in tf.\r\nThe code in tf is as follows:\r\n\r\n```\r\ndef gather_indexes(sequence_tensor, positions):\r\n \"\"\"Gathers the vectors at the specific positions over a minibatch.\"\"\"\r\n...\r\ninput_tensor = gather_indexes(input_tensor, positions)\r\n```\r\n\r\n... | 1,545 | 1,584 | 1,546 | CONTRIBUTOR | null | Use the same sentence in your **Usage** Section:
```
# Tokenized input
text = "Who was Jim Henson ? Jim Henson was a puppeteer"
tokenized_text = tokenizer.tokenize(text)
# Mask a token that we will try to predict back with `BertForMaskedLM`
masked_index = 6
tokenized_text[masked_index] = '[MASK]'
```
Q1.
... | {
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https://api.github.com/repos/huggingface/transformers/issues/143 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/143/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/143/comments | https://api.github.com/repos/huggingface/transformers/issues/143/events | https://github.com/huggingface/transformers/issues/143 | 393,365,633 | MDU6SXNzdWUzOTMzNjU2MzM= | 143 | bug in init_bert_weights | {
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"Fixed, thanks"
] | 1,545 | 1,546 | 1,546 | NONE | null | hi ,
there is a bug in init_bert_weights().
the BERTLayerNorm has twice init, the first init is in the BERTLayerNorm module __init__(). the second init in init_bert_weights().
if you want to get pre-training model that is not from google model, the second init will lead to bad convergence in my experime... | {
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https://api.github.com/repos/huggingface/transformers/issues/142 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/142/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/142/comments | https://api.github.com/repos/huggingface/transformers/issues/142/events | https://github.com/huggingface/transformers/pull/142 | 393,327,036 | MDExOlB1bGxSZXF1ZXN0MjQwMzc5MzM3 | 142 | change in run_classifier.py | {
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"Thanks! #141 already addressed this problem."
] | 1,545 | 1,546 | 1,546 | NONE | null | while running the dev set for multi-label classification (more than two), it gives an assertion error. Specifying the num_labels while creating the model again for eval testing solves this problem. Thus the only change is while running classification for multi label is in the starting dict of num labels and specifying ... | {
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https://api.github.com/repos/huggingface/transformers/issues/141 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/141/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/141/comments | https://api.github.com/repos/huggingface/transformers/issues/141/events | https://github.com/huggingface/transformers/pull/141 | 393,226,114 | MDExOlB1bGxSZXF1ZXN0MjQwMzEzMjkz | 141 | loading saved model when n_classes != 2 | {
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"This problem is discussed is #135 and I don't think that this is the right way to patch this problem. The saved model contains the `num_labels` information.",
"cf discussion in #135 let's go for the `mandatory-argument`solution for now."
] | 1,545 | 1,546 | 1,546 | CONTRIBUTOR | null | Required to for: Assertion `t >= 0 && t < n_classes` failed, if your default number of classes is not 2. | {
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https://api.github.com/repos/huggingface/transformers/issues/140 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/140/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/140/comments | https://api.github.com/repos/huggingface/transformers/issues/140/events | https://github.com/huggingface/transformers/issues/140 | 393,167,870 | MDU6SXNzdWUzOTMxNjc4NzA= | 140 | Not able to use FP16 in pytorch-pretrained-BERT. Getting error **Runtime error: Expected scalar type object Half but got scalar type Float for argument #2 target** | {
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"Which kind of GPU are you using? `fp16` only works on recent GPU (better with Tesla and Volta series).",
"I experienced a similar issue with CUDA 9.1. Using 9.2 solved this for me. ",
"Yes, CUDA 10 is recommended for using fp16 with good performances."
] | 1,545 | 1,546 | 1,546 | NONE | null | I'm not able to work with FP16 for pytorch BERT code. Particularly for BertForSequenceClassification, which I tried and got the issue
**Runtime error: Expected scalar type object Half but got scalar type Float for argument #2 target**
when I enabled fp16.
Also when using
`logits = logits.half()
labels = labels.ha... | {
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https://api.github.com/repos/huggingface/transformers/issues/139 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/139/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/139/comments | https://api.github.com/repos/huggingface/transformers/issues/139/events | https://github.com/huggingface/transformers/issues/139 | 393,167,784 | MDU6SXNzdWUzOTMxNjc3ODQ= | 139 | Not able to use FP16 in pytorch-pretrained-BERT | {
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**Runtime error: Expected scalar type object Half but got scalar type Float for argument #2 target**
when I enabled fp16.
Also when using
`logits = logits.half()
labels = labels.ha... | {
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https://api.github.com/repos/huggingface/transformers/issues/138 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/138/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/138/comments | https://api.github.com/repos/huggingface/transformers/issues/138/events | https://github.com/huggingface/transformers/issues/138 | 393,142,144 | MDU6SXNzdWUzOTMxNDIxNDQ= | 138 | Problem loading finetuned model for squad | {
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"Judging from the error message, I would say that the error is caused by the following line: https://github.com/huggingface/pytorch-pretrained-BERT/blob/7fb94ab934b2ad1041613fc93c61d13105faf98a/pytorch_pretrained_bert/modeling.py#L541\r\n\r\nApparently, the proper way to save a model is the following one:\r\nhttps:... | 1,545 | 1,546 | 1,546 | NONE | null | Hi,
i'm trying to load a fine tuned model for question answering which i trained with squad.py:
```
import torch
from pytorch_pretrained_bert import BertModel, BertForQuestionAnswering
from pytorch_pretrained_bert import modeling
config = modeling.BertConfig(attention_probs_dropout_prob=0.1, hidden_dropout_prob... | {
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https://api.github.com/repos/huggingface/transformers/issues/137 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/137/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/137/comments | https://api.github.com/repos/huggingface/transformers/issues/137/events | https://github.com/huggingface/transformers/issues/137 | 393,079,924 | MDU6SXNzdWUzOTMwNzk5MjQ= | 137 | run_squad.py without GPU.. Without CUPY | {
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"@SandeepBhutani What was the conclusion of this issue? ",
"Is this issue still open.. It can be closed.. It was an environment issue..\n\nOn Sat, 13 Jul, 2019, 5:00 AM Peter, <notifications@github.com> wrote:\n\n> @SandeepBhutani <https://github.com/SandeepBhutani> What was the\n> conclusion of this issue?\n>\n>... | 1,545 | 1,563 | 1,545 | NONE | null | I am trying to run_squad.py for QnA (Squad) case. Its dependency is on GPU.. i.e., cupy is to be installed.
In one of my environment I dont have GPU therefore cupy is not getting installed and I am not able to proceed with training.
Can I train on CPU itself?
following is I am trying to run:
```
python run_squ... | {
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https://api.github.com/repos/huggingface/transformers/issues/136 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/136/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/136/comments | https://api.github.com/repos/huggingface/transformers/issues/136/events | https://github.com/huggingface/transformers/issues/136 | 393,058,463 | MDU6SXNzdWUzOTMwNTg0NjM= | 136 | It's possible to avoid download the pretrained model? | {
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"I just find the way.",
"@rxy1212 could you explain the method used ",
"@makkunda \r\nIn `modeling.py`, you can find this codes\r\n```\r\nPRETRAINED_MODEL_ARCHIVE_MAP = {\r\n 'bert-base-uncased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz\",\r\n 'bert-large-uncased': \... | 1,545 | 1,545 | 1,545 | NONE | null | When I run this code `model = BertModel.from_pretrained('bert-base-uncased')` , it would download a big file and sometimes that's very slow. Now I have download the model from [https://github.com/google-research/bert](url). So, It's possible to avoid download the pretrained model when I use pytorch-pretrained-BERT at ... | {
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"Ok I managed to find the problem. It comes from:\r\n\r\nhttps://github.com/huggingface/pytorch-pretrained-BERT/blob/7fb94ab934b2ad1041613fc93c61d13105faf98a/pytorch_pretrained_bert/modeling.py#L534-L540\r\n\r\nWhen trying to load `classifier.weight` and `classifier.bias`, the following line gets added to `error_ms... | 1,545 | 1,628 | 1,546 | CONTRIBUTOR | null | Hi!
There is a problem with the way model are saved and loaded. The following code should crash and doesn't:
```python
import torch
from pytorch_pretrained_bert import BertForSequenceClassification
model_fn = 'model.bin'
bert_model = 'bert-base-multilingual-cased'
model = BertForSequenceClassification.from... | {
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"Nice, thanks Gregory!"
] | 1,545 | 1,546 | 1,546 | CONTRIBUTOR | null | Hi!
The documentation of `PretrainedBertModel` was missing the new pre-trained model names and the one of `BertForQuestionAnswering` was wrong (due to a copy-pasting mistake I assume).
Cheers! | {
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"We need more informations on the parameters you use to run this training in order to understand what might be wrong.",
"> We need more informations on the parameters you use to run this training in order to understand what might be wrong.\r\n\r\nTHANK YOU! Because of limited mermory, the batch_size is 8 and epoc... | 1,545 | 1,546 | 1,546 | NONE | null | I run the classification task with BERT pretrianed model, but while it's much lower than other methods on OMD dataset, which has 2 labels. The final accuracy result is only 62% on binary classification task! | {
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"Hi @itchanghi, thanks for the feedback. Indeed the `run_squad` example was not updated for `cased` models. I fixed that in commits c9fd3505678d581388fb44ba1d79ac41e8fb28a4 and 2e4db64cab198dc241e18221ef088908f2587c61.\r\n\r\nPlease re-open the issue if your problem is not fixed (and maybe summarize it in an update... | 1,545 | 1,601 | 1,546 | NONE | null | I'm working on fine-tuning squad task with multilingual-cased model.
Google says "When using a cased model, make sure to pass --do_lower=False to the training scripts. (Or pass do_lower_case=False directly to FullTokenizer if you're using your own script.)"
So, I added "do_lower_case" argument to run squad script... | {
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"Looks great indeed, thanks for that!"
] | 1,545 | 1,545 | 1,545 | CONTRIBUTOR | null | The recommended approach to create launch scripts is to use entry_points
and console_scripts.
xref:
https://packaging.python.org/guides/distributing-packages-using-setuptools/#scripts | {
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"I don't know...\r\nIf you can open-source a self contained example with data and code I can try to give it a deeper look.\r\nAre you using `apex`? That's the main change in 0.4.0.",
"Hi Thomas! I've found the problem. I think It's because you modified your `from_pretrained` function and I'm still using a part of... | 1,545 | 1,545 | 1,545 | NONE | null | I used BERT in a very simple sentence classification task:
in `__init__` I have
```python3
self.bert = BertModel(config)
self.cnn_classifier = CNNClassifier(self.config.hidden_size, intent_cls_num)
```
and in forward it's just
```python3
encoded_layers, _ = self.bert(input_ids, token_type_ids, attention_mask, o... | {
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"Thanks!"
] | 1,545 | 1,545 | 1,545 | CONTRIBUTOR | null | The `LICENSE` file in the git repository contains the Apache license text
but it not included the source `.tar.gz` distribution.
This PR adds a `MANIFEST.in` file with a directive to include the LICENSE. | {
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"Thanks @patrick-s-h-lewis, this is nice.\r\n\r\nThe max number of positional embeddings is also available in the pretrained models configuration files (as `max_position_embeddings`) but accessing this requires some change in the models stored on S3 (not storing them as tar.gz files) so I will take care of it in th... | 1,545 | 1,545 | 1,545 | CONTRIBUTOR | null | addesses #125
(all pre-trained bert models have a positional embedding matrix with 512 embeddings. Sequences longer than 512 tokens will cause indexing errors when you attempt to run a bert forward pass on them)
added a max_len arg to bert tokenizer. the function convert_tokens_to_indices will raise a value erro... | {
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"Do you have a dataset in mind for the benchmark?\r\nWe can do a simple benchmark by timing the duration of evaluation on the SQuAD dev set for example.",
"Yes, that would be perfect! Ideally, it would exclude loading and setting up the model (something that the tf implementation literally does not allow for :P) ... | 1,545 | 1,591 | 1,557 | CONTRIBUTOR | null | In reference to following [tweet](https://twitter.com/Thom_Wolf/status/1074983741716602882):
Would it be possible to do a benchmark on the speed of prediction? I was working with the tensorflow version of BERT, but it uses the new Estimators and I'm struggling to find a straight-forward way to benchmark it since it... | {
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"Could do that indeed Patrick.\r\nIn particular when the tokenizer is loaded from one of Google pre-trained model.\r\nIf you have a working implementation feel free to do a PR.\r\nOtherwise I will have a look at that when I start working on the next release.",
"Happy to do a PR :) will do today or tomorrow"
] | 1,545 | 1,546 | 1,546 | CONTRIBUTOR | null | Hi team, love the work.
Just a feature suggestion: when running on GPU (presumably the CPU too), BERT will break when you try to run on sentences longer than 512 tokens (on bert-base).
This is because the position embedding matrix size is only 512 (or whatever else it is for the other bert models)
Could the to... | {
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"This looks like a great addition!\r\n\r\nIs it a full re-implementation of the pre-training script?",
"The implementation uses the same sampling parameters and logic, but it's not a one-by-one re-implementation of the original pre-training script. \r\n\r\n**Main differences:**\r\n- In the original repo they firs... | 1,545 | 1,597 | 1,546 | NONE | null | We are currently working on fine-tuning the language model on a new target corpus. This should improve the model, if the language style in your target corpus differs significantly from the one initially used for training BERT (Wiki + BookCorpus), but is still too small for training BERT from scratch. In our case, we ap... | {
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https://api.github.com/repos/huggingface/transformers/issues/123 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/123/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/123/comments | https://api.github.com/repos/huggingface/transformers/issues/123/events | https://github.com/huggingface/transformers/issues/123 | 391,979,075 | MDU6SXNzdWUzOTE5NzkwNzU= | 123 | big memory occupied | {
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"You should lower the batch size probably"
] | 1,545 | 1,545 | 1,545 | NONE | null | When I run the examples for MRPC, my program was always killed becaused of big memory occupied. Anyone encounter with this issue? | {
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https://api.github.com/repos/huggingface/transformers/issues/122 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/122/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/122/comments | https://api.github.com/repos/huggingface/transformers/issues/122/events | https://github.com/huggingface/transformers/issues/122 | 391,564,653 | MDU6SXNzdWUzOTE1NjQ2NTM= | 122 | _load_from_state_dict() takes 7 positional arguments but 8 were given | {
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"Full log of the error?",
"This is caused by pytorch version.\r\nI found , In 0.4.0 version, _load_from_state_dict() only take 7 arguments, but In 0.4.1 and this code, we need feed 8 arguments.\r\n\r\n```\r\nmodule._load_from_state_dict(\r\n state_dict, prefix, local_metadata, True, missing_keys, u... | 1,545 | 1,546 | 1,546 | NONE | null | {
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https://api.github.com/repos/huggingface/transformers/issues/121 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/121/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/121/comments | https://api.github.com/repos/huggingface/transformers/issues/121/events | https://github.com/huggingface/transformers/issues/121 | 391,458,997 | MDU6SXNzdWUzOTE0NTg5OTc= | 121 | High accuracy for CoLA task | {
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"The metric used for evaluation of CoLA in the GLUE benchmark is not accuracy but the https://en.wikipedia.org/wiki/Matthews_correlation_coefficient (see https://gluebenchmark.com/tasks).\r\nIndeed authors report in https://arxiv.org/abs/1810.04805 0.521 for Matthews correlation with BERT-base.",
"Makes sense, lo... | 1,544 | 1,545 | 1,545 | NONE | null | I try to reproduce the CoLA results from the BERT paper (BERTBase, Single GPU).
Running the following command
```
python run_classifier.py \
--task_name cola \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/CoLA/ \
--bert_model bert-base-uncased \
--max_seq_length 128 \
-... | {
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https://api.github.com/repos/huggingface/transformers/issues/120 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/120/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/120/comments | https://api.github.com/repos/huggingface/transformers/issues/120/events | https://github.com/huggingface/transformers/issues/120 | 391,402,013 | MDU6SXNzdWUzOTE0MDIwMTM= | 120 | RuntimeError: Expected object of type torch.LongTensor but found type torch.cuda.LongTensor for argument #3 'index' | {
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"The issue was, not properly loading the model file and moving it to GPU. "
] | 1,544 | 1,544 | 1,544 | CONTRIBUTOR | null | I am using part of your evaluation code, with slight modifications:
https://github.com/danyaljj/pytorch-pretrained-BERT/blob/92e22d710287db1b4aa4fda951714887878fa728/examples/daniel_run.py#L582-L616
Wondering if you have encountered the following error:
```
(env3.6) khashab2@gissing:/shared/shelley/khashab2/... | {
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https://api.github.com/repos/huggingface/transformers/issues/119 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/119/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/119/comments | https://api.github.com/repos/huggingface/transformers/issues/119/events | https://github.com/huggingface/transformers/pull/119 | 391,231,432 | MDExOlB1bGxSZXF1ZXN0MjM4ODE1NDQx | 119 | Minor README fix | {
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"Indeed!"
] | 1,544 | 1,544 | 1,544 | CONTRIBUTOR | null | I think `optimize_on_cpu` option was dropped in #112 | {
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https://api.github.com/repos/huggingface/transformers/issues/118 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/118/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/118/comments | https://api.github.com/repos/huggingface/transformers/issues/118/events | https://github.com/huggingface/transformers/issues/118 | 390,950,821 | MDU6SXNzdWUzOTA5NTA4MjE= | 118 | Segmentation fault (core dumped) | {
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"Hi, you need to give me more information (a screen copy of a full log of the error).",
"Actually, this is all I got:\r\n\r\n>> python bert.py\r\n12/15/2018 19:43:06 - INFO - pytorch_pretrained_bert.tokenization - loading vocabulary file /home/snow/bert_models_path/vocab.txt\r\n12/15/2018 19:43:06 - INFO - pyt... | 1,544 | 1,600 | 1,545 | NONE | null | Hi,
I downloaded pretrained model and vocabulary fileοΌ and wanted to test BertModel to get hidden states.
when this
```encoded_layers, _ = model(tokens_tensor, segments_tensors)``` lines run, I got this error: Segmentation fault (core dumped).
I wonder what caused this error | {
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https://api.github.com/repos/huggingface/transformers/issues/117 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/117/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/117/comments | https://api.github.com/repos/huggingface/transformers/issues/117/events | https://github.com/huggingface/transformers/issues/117 | 390,793,183 | MDU6SXNzdWUzOTA3OTMxODM= | 117 | logging.basicConfig overrides user logging | {
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"You're right. It's removed."
] | 1,544 | 1,544 | 1,544 | NONE | null | I think logging.basicConfig should not be called inside library code
check out this SO thread
https://stackoverflow.com/questions/27016870/how-should-logging-be-used-in-a-python-package | {
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https://api.github.com/repos/huggingface/transformers/issues/116 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/116/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/116/comments | https://api.github.com/repos/huggingface/transformers/issues/116/events | https://github.com/huggingface/transformers/pull/116 | 390,028,146 | MDExOlB1bGxSZXF1ZXN0MjM3ODg1MTEz | 116 | Change to use apex for better fp16 and multi-gpu support | {
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"That's really awesome! I love the work you guys did on apex and I would be super happy to have an 'official' implementation of BERT using apex (plus it showcases all the major modules: FusedAdam, FusedLayerNorm, 16bits, distributed optimizer...). And the speed improvement is impressive, fine-tuning BERT-large on S... | 1,544 | 1,563 | 1,544 | CONTRIBUTOR | null | Hi there,
This PR includes changes to improve FP16 and multi-gpu performance. We get over 3.5x performance increase on Tesla V100 across all examples.
NVIDIA Apex([https://github.com/NVIDIA/apex](url)) is added as a new dependency. It fixed issues with existing fp16 implementation(for example not converting loss/... | {
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https://api.github.com/repos/huggingface/transformers/issues/115 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/115/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/115/comments | https://api.github.com/repos/huggingface/transformers/issues/115/events | https://github.com/huggingface/transformers/issues/115 | 389,950,888 | MDU6SXNzdWUzODk5NTA4ODg= | 115 | How to run a saved model? | {
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"It looks like @thomwolf is planning to illustrate this in the examples soon.\r\nYou find some useful code to do what you want to do in https://github.com/huggingface/pytorch-pretrained-BERT/pull/112/",
"Hi this is now included in the new release 0.4.0 and there are examples on how you can save and reload the mod... | 1,544 | 1,544 | 1,544 | NONE | null | How can you run the model without training the model? If we already trained a model with run_classifer? | {
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https://api.github.com/repos/huggingface/transformers/issues/114 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/114/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/114/comments | https://api.github.com/repos/huggingface/transformers/issues/114/events | https://github.com/huggingface/transformers/issues/114 | 389,846,897 | MDU6SXNzdWUzODk4NDY4OTc= | 114 | What is the best dataset structure for BERT? | {
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I am using BertForSequenceClassification and am wondering what the optimal way is to structure my sequences.
Right now my sequences are blog post which could be upwards to 400 words long.
Would it be better to split my blog posts in sentences and use the se... | {
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https://api.github.com/repos/huggingface/transformers/issues/113 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/113/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/113/comments | https://api.github.com/repos/huggingface/transformers/issues/113/events | https://github.com/huggingface/transformers/pull/113 | 389,741,749 | MDExOlB1bGxSZXF1ZXN0MjM3NjYxMzY5 | 113 | fix compatibility with python 3.5.2 | {
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"Thanks, it could be nice to keep Python 3.5 compatibility indeed (see #110) but I think this will break (at least) the other examples (`run_squad` and `run_classifier`) which uses the Pathlib syntax `PATH / 'string'`.",
"I'm sorry for my previous stupid workaround, but now I modify some functions in ``file_utils... | 1,544 | 1,544 | 1,544 | CONTRIBUTOR | null | When I run the following command on python 3.5.2
```
python3 extract_features.py --input_file input.txt --output_file output.txt --bert_model bert-base-uncased --do_lower_case
```
Get this error:
```
Traceback (most recent call last):
File "extract_features.py", line 298, in <module>
main()
File ... | {
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https://api.github.com/repos/huggingface/transformers/issues/112 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/112/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/112/comments | https://api.github.com/repos/huggingface/transformers/issues/112/events | https://github.com/huggingface/transformers/pull/112 | 389,707,309 | MDExOlB1bGxSZXF1ZXN0MjM3NjM1MDky | 112 | Fourth release | {
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- 3-4 times speed-up in fp16 thanks to NVIDIA's work on apex
- SWAG (multiple-choice) model added + example fine-tuning on SWAG
- bump up to PyTorch 1.0
- backward compatibility to python 3.5
- load fine-tuned model with `from_pretrained`
- add examples on how to save and load fine-tuned models | {
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https://api.github.com/repos/huggingface/transformers/issues/111 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/111/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/111/comments | https://api.github.com/repos/huggingface/transformers/issues/111/events | https://github.com/huggingface/transformers/pull/111 | 389,696,001 | MDExOlB1bGxSZXF1ZXN0MjM3NjI2NDQw | 111 | update: add from_state_dict for PreTrainedBertModel | {
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"Hi, I like the idea but I not a big fan of all the code duplication I'ld rather fuse the two loading functions in one.\r\nBasically we can just add a `state_dict` argument to `from_pretrained` and add a check in `from_pretrained` to handle the case.",
"> Hi, I like the idea but I not a big fan of all the code du... | 1,544 | 1,544 | 1,544 | NONE | null | For restoring the training procedure.
Now we can use torch.save to store their model and restore their model by e.g. `model = BertForSequenceClassification.from_state_dict('bert-large-uncased', state_dict=torch.load('xx.pth'))` | {
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https://api.github.com/repos/huggingface/transformers/issues/110 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/110/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/110/comments | https://api.github.com/repos/huggingface/transformers/issues/110/events | https://github.com/huggingface/transformers/issues/110 | 389,549,868 | MDU6SXNzdWUzODk1NDk4Njg= | 110 | Pretrained Tokenizer Loading Fails: 'PosixPath' object has no attribute 'rfind' | {
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"Oh you are right, the file caching utilities requires python 3.6.\r\n\r\nI don't intend to maintain a lot of backward compatibility in terms of Python versions (I already surrendered maintaining a Python 2 version) so I will bump up the requirements to python 3.6.\r\n\r\nIf you are limited to python 3.5 and find a... | 1,544 | 1,544 | 1,544 | NONE | null | I was trying to work through the toy tokenization example from the main README, and I hit an error on the step of loading in a pre-trained BERT tokenizer.
```
~/bert_transfer$ python3 test_tokenizer.py
Traceback (most recent call last):
File "test_tokenizer.py",... | {
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https://api.github.com/repos/huggingface/transformers/issues/109 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/109/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/109/comments | https://api.github.com/repos/huggingface/transformers/issues/109/events | https://github.com/huggingface/transformers/issues/109 | 389,540,611 | MDU6SXNzdWUzODk1NDA2MTE= | 109 | UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte | {
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"I had the same question/confusion! Thanks for clarifying it should be the path to the directory and not the filename itself. ",
"Great help, thanks.",
"Thanks"
] | 1,544 | 1,592 | 1,544 | NONE | null | When I convert a TensorFlow checkpoint in a `pytorch_model.bin` and run `run_classifier.py` with `--bert_model /path/to/pytorch_model.bin` option, following error occurs in `tokenization.py`.
```shell
12/10/2018 18:11:59 - INFO - pytorch_pretrained_bert.tokenization - loading vocabulary file /Users/MAC/bert/model... | {
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