Thomas Wolf
e57d00ee10
Merge pull request #1984 from huggingface/squad-refactor
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[WIP] Squad refactor
2019-12-10 11:07:26 +01:00
Julien Chaumond
1d18930462
Harmonize no_cuda flag with other scripts
2019-12-09 20:37:55 -05:00
Rémi Louf
f7eba09007
clean for release
2019-12-09 20:37:55 -05:00
Rémi Louf
2a64107e44
improve device usage
2019-12-09 20:37:55 -05:00
Rémi Louf
c0707a85d2
add README
2019-12-09 20:37:55 -05:00
Rémi Louf
ade3cdf5ad
integrate ROUGE
2019-12-09 20:37:55 -05:00
Rémi Louf
076602bdc4
prevent BERT weights from being downloaded twice
2019-12-09 20:37:55 -05:00
Rémi Louf
a1994a71ee
simplified model and configuration
2019-12-09 20:37:55 -05:00
Rémi Louf
3a9a9f7861
default output dir to documents dir
2019-12-09 20:37:55 -05:00
Rémi Louf
693606a75c
update the docs
2019-12-09 20:37:55 -05:00
Rémi Louf
2403a66598
give transformers API to BertAbs
2019-12-09 20:37:55 -05:00
Rémi Louf
ba089c780b
share pretrained embeddings
2019-12-09 20:37:55 -05:00
Rémi Louf
9660ba1cbd
Add beam search
2019-12-09 20:37:55 -05:00
Rémi Louf
1c71ecc880
load the pretrained weights for encoder-decoder
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We currently save the pretrained_weights of the encoder and decoder in
two separate directories `encoder` and `decoder`. However, for the
`from_pretrained` function to operate with automodels we need to
specify the type of model in the path to the weights.
The path to the encoder/decoder weights is handled by the
`PreTrainedEncoderDecoder` class in the `save_pretrained` function. Sice
there is no easy way to infer the type of model that was initialized for
the encoder and decoder we add a parameter `model_type` to the function.
This is not an ideal solution as it is error prone, and the model type
should be carried by the Model classes somehow.
This is a temporary fix that should be changed before merging.
2019-12-09 20:37:55 -05:00
Rémi Louf
07f4cd73f6
update function to add special tokens
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Since I started my PR the `add_special_token_single_sequence` function
has been deprecated for another; I replaced it with the new function.
2019-12-09 20:37:55 -05:00
Bilal Khan
79526f82f5
Remove unnecessary epoch variable
2019-12-09 16:24:35 -05:00
Bilal Khan
9626e0458c
Add functionality to continue training from last saved global_step
2019-12-09 16:24:35 -05:00
Bilal Khan
2d73591a18
Stop saving current epoch
2019-12-09 16:24:35 -05:00
Bilal Khan
0eb973b0d9
Use saved optimizer and scheduler states if available
2019-12-09 16:24:35 -05:00
Bilal Khan
a03fcf570d
Save tokenizer after each epoch to be able to resume training from a checkpoint
2019-12-09 16:24:35 -05:00
Bilal Khan
f71b1bb05a
Save optimizer state, scheduler state and current epoch
2019-12-09 16:24:35 -05:00
LysandreJik
2a4ef098d6
Add ALBERT and XLM to SQuAD script
2019-12-09 10:46:47 -05:00
Lysandre Debut
00c4e39581
Merge branch 'master' into squad-refactor
2019-12-09 10:41:15 -05:00
Thomas Wolf
5482822a2b
Merge pull request #2046 from jplu/tf2-ner-example
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Add NER TF2 example.
2019-12-06 12:12:22 +01:00
LysandreJik
e9217da5ff
Cleanup
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Improve global visibility on the run_squad script, remove unused files and fixes related to XLNet.
2019-12-05 16:01:51 -05:00
LysandreJik
9ecd83dace
Patch evaluation for impossible values + cleanup
2019-12-05 14:44:57 -05:00
VictorSanh
35ff345fc9
update requirements
2019-12-05 12:07:04 -05:00
VictorSanh
552c44a9b1
release distilm-bert
2019-12-05 10:14:58 -05:00
Rosanne Liu
ee53de7aac
Pr for pplm ( #2060 )
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* license
* changes
* ok
* Update paper link and commands to run
* pointer to uber repo
2019-12-05 09:20:07 -05:00
Julien Plu
9200a759d7
Add few tests on the TF optimization file with some info in the documentation. Complete the README.
2019-12-05 12:56:43 +01:00
thomwolf
75a97af6bc
fix #1450 - add doc
2019-12-05 11:26:55 +01:00
LysandreJik
f7e4a7cdfa
Cleanup
2019-12-04 16:24:15 -05:00
LysandreJik
cca75e7884
Kill the demon spawn
2019-12-04 15:42:29 -05:00
LysandreJik
9ddc3f1a12
Naming update + XLNet/XLM evaluation
2019-12-04 10:37:00 -05:00
thomwolf
5bfcd0485e
fix #1991
2019-12-04 14:53:11 +01:00
Julien Plu
ecb923da9c
Create a NER example similar to the Pytorch one. It takes the same options, and can be run the same way.
2019-12-04 09:43:15 +01:00
LysandreJik
de276de1c1
Working evaluation
2019-12-03 17:15:51 -05:00
Julien Chaumond
7edb51f3a5
[pplm] split classif head into its own file
2019-12-03 22:07:25 +00:00
VictorSanh
48cbf267c9
Use full dataset for eval (SequentialSampler in Distributed setting)
2019-12-03 11:01:37 -05:00
Julien Chaumond
f434bfc623
[pplm] Update S3 links
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Co-Authored-By: Piero Molino <w4nderlust@gmail.com>
2019-12-03 10:53:02 -05:00
Ethan Perez
96e83506d1
Always use SequentialSampler during evaluation
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When evaluating, shouldn't we always use the SequentialSampler instead of DistributedSampler? Evaluation only runs on 1 GPU no matter what, so if you use the DistributedSampler with N GPUs, I think you'll only evaluate on 1/N of the evaluation set. That's at least what I'm finding when I run an older/modified version of this repo.
2019-12-03 10:15:39 -05:00
Julien Chaumond
3b48806f75
[pplm] README: add setup + tweaks
2019-12-03 10:14:02 -05:00
Julien Chaumond
0cb2c90890
readme
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Co-Authored-By: Rosanne Liu <mimosavvy@gmail.com>
2019-12-03 10:14:02 -05:00
Julien Chaumond
1efb2ae7fc
[pplm] move scripts under examples/pplm/
2019-12-03 10:14:02 -05:00
Piero Molino
a59fdd1627
generate_text_pplm now works with batch_size > 1
2019-12-03 10:14:02 -05:00
w4nderlust
893d0d64fe
Changed order of some parameters to be more consistent. Identical results.
2019-12-03 10:14:02 -05:00
w4nderlust
f42816e7fc
Added additional check for url and path in discriminator model params
2019-12-03 10:14:02 -05:00
w4nderlust
f10b925015
Imrpovements: model_path renamed pretrained_model, tokenizer loaded from pretrained_model, pretrained_model set to discriminator's when discrim is specified, sample = False by default but cli parameter introduced. To obtain identical samples call the cli with --sample
2019-12-03 10:14:02 -05:00
w4nderlust
75904dae66
Removed global variable device
2019-12-03 10:14:02 -05:00
piero
7fd54b55a3
Added support for generic discriminators
2019-12-03 10:14:02 -05:00
