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Commit e468192e authored by thomwolf's avatar thomwolf
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Merge branch 'pytorch-transformers' into xlnet

parents 9dd2c860 4ce237c8
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examples/run_xlnet_squad.py
View file @ e468192e
...@@ -33,10 +33,10 @@ from tqdm import tqdm, trange ...@@ -33,10 +33,10 @@ from tqdm import tqdm, trange
from tensorboardX import SummaryWriter from tensorboardX import SummaryWriter
from pytorch_pretrained_bert import WEIGHTS_NAME, CONFIG_NAME from pytorch_transformers import WEIGHTS_NAME, CONFIG_NAME
from pytorch_pretrained_bert.modeling_xlnet import BertForQuestionAnswering from pytorch_transformers.modeling_xlnet import BertForQuestionAnswering
from pytorch_pretrained_bert.tokenization_xlnet import XLNetTokenizer from pytorch_transformers.tokenization_xlnet import XLNetTokenizer
from pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule from pytorch_transformers.optimization import BertAdam, WarmupLinearSchedule
from utils_squad import read_squad_examples, convert_examples_to_features, RawResult, write_predictions from utils_squad import read_squad_examples, convert_examples_to_features, RawResult, write_predictions
......
examples/test_examples.py 0 → 100644
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# coding=utf-8
# Copyright 2018 HuggingFace Inc..
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import unittest
import argparse
import logging
try:
# python 3.4+ can use builtin unittest.mock instead of mock package
from unittest.mock import patch
except ImportError:
from mock import patch
import run_glue
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
def get_setup_file():
parser = argparse.ArgumentParser()
parser.add_argument('-f')
args = parser.parse_args()
return args.f
class ExamplesTests(unittest.TestCase):
def test_run_glue(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
testargs = ["run_glue.py", "--data_dir=./examples/tests_samples/MRPC/",
"--task_name=mrpc", "--do_train", "--do_eval", "--output_dir=./examples/tests_samples/temp_dir",
"--train_batch_size=4", "--eval_batch_size=2", "--num_train_epochs=2.0", "--overwrite_output_dir"]
model_name = "--model_name=bert-base-uncased"
with patch.object(sys, 'argv', testargs + [model_name]):
result = run_glue.main()
for value in result.values():
self.assertGreaterEqual(value, 0.75)
if __name__ == "__main__":
unittest.main()
examples/tests_samples/.gitignore 0 → 100644
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*.*
cache*
temp*
!*.tsv
!.gitignore
\ No newline at end of file
examples/tests_samples/MRPC/dev.tsv 0 → 100644
View file @ e468192e
Quality #1 ID #2 ID #1 String #2 String
1 1355540 1355592 He said the foodservice pie business doesn 't fit the company 's long-term growth strategy . " The foodservice pie business does not fit our long-term growth strategy .
0 2029631 2029565 Magnarelli said Racicot hated the Iraqi regime and looked forward to using his long years of training in the war . His wife said he was " 100 percent behind George Bush " and looked forward to using his years of training in the war .
0 487993 487952 The dollar was at 116.92 yen against the yen , flat on the session , and at 1.2891 against the Swiss franc , also flat . The dollar was at 116.78 yen JPY = , virtually flat on the session , and at 1.2871 against the Swiss franc CHF = , down 0.1 percent .
1 1989515 1989458 The AFL-CIO is waiting until October to decide if it will endorse a candidate . The AFL-CIO announced Wednesday that it will decide in October whether to endorse a candidate before the primaries .
0 1783137 1782659 No dates have been set for the civil or the criminal trial . No dates have been set for the criminal or civil cases , but Shanley has pleaded not guilty .
1 3039165 3039036 Wal-Mart said it would check all of its million-plus domestic workers to ensure they were legally employed . It has also said it would review all of its domestic employees more than 1 million to ensure they have legal status .
examples/tests_samples/MRPC/train.tsv 0 → 100644
View file @ e468192e
Quality #1 ID #2 ID #1 String #2 String
1 1355540 1355592 He said the foodservice pie business doesn 't fit the company 's long-term growth strategy . " The foodservice pie business does not fit our long-term growth strategy .
0 2029631 2029565 Magnarelli said Racicot hated the Iraqi regime and looked forward to using his long years of training in the war . His wife said he was " 100 percent behind George Bush " and looked forward to using his years of training in the war .
0 487993 487952 The dollar was at 116.92 yen against the yen , flat on the session , and at 1.2891 against the Swiss franc , also flat . The dollar was at 116.78 yen JPY = , virtually flat on the session , and at 1.2871 against the Swiss franc CHF = , down 0.1 percent .
1 1989515 1989458 The AFL-CIO is waiting until October to decide if it will endorse a candidate . The AFL-CIO announced Wednesday that it will decide in October whether to endorse a candidate before the primaries .
0 1783137 1782659 No dates have been set for the civil or the criminal trial . No dates have been set for the criminal or civil cases , but Shanley has pleaded not guilty .
1 3039165 3039036 Wal-Mart said it would check all of its million-plus domestic workers to ensure they were legally employed . It has also said it would review all of its domestic employees more than 1 million to ensure they have legal status .
examples/utils_glue.py
View file @ e468192e
...@@ -396,7 +396,7 @@ def convert_examples_to_features(examples, label_list, max_seq_length, ...@@ -396,7 +396,7 @@ def convert_examples_to_features(examples, label_list, max_seq_length,
mask_padding_with_zero=True): mask_padding_with_zero=True):
""" Loads a data file into a list of `InputBatch`s """ Loads a data file into a list of `InputBatch`s
`cls_token_at_end` define the location of the CLS token: `cls_token_at_end` define the location of the CLS token:
- False (BERT pattern): [CLS] + A + [SEP] + B + [SEP] - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
- True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS] - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
`cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet) `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
""" """
...@@ -489,8 +489,7 @@ def convert_examples_to_features(examples, label_list, max_seq_length, ...@@ -489,8 +489,7 @@ def convert_examples_to_features(examples, label_list, max_seq_length,
[str(x) for x in tokens])) [str(x) for x in tokens]))
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
logger.info( logger.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
logger.info("label: %s (id = %d)" % (example.label, label_id)) logger.info("label: %s (id = %d)" % (example.label, label_id))
features.append( features.append(
...@@ -583,6 +582,7 @@ processors = { ...@@ -583,6 +582,7 @@ processors = {
output_modes = { output_modes = {
"cola": "classification", "cola": "classification",
"mnli": "classification", "mnli": "classification",
"mnli-mm": "classification",
"mrpc": "classification", "mrpc": "classification",
"sst-2": "classification", "sst-2": "classification",
"sts-b": "regression", "sts-b": "regression",
......
examples/utils_squad.py
View file @ e468192e
...@@ -24,7 +24,7 @@ import math ...@@ -24,7 +24,7 @@ import math
import collections import collections
from io import open from io import open
from pytorch_pretrained_bert.tokenization_bert import BasicTokenizer, whitespace_tokenize from pytorch_transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
......
hubconfs/bert_hubconf.py
View file @ e468192e
from pytorch_pretrained_bert.tokenization_bert import BertTokenizer from pytorch_transformers.tokenization_bert import BertTokenizer
from pytorch_pretrained_bert.modeling_bert import ( from pytorch_transformers.modeling_bert import (
BertModel, BertModel,
BertForNextSentencePrediction, BertForNextSentencePrediction,
BertForMaskedLM, BertForMaskedLM,
...@@ -86,7 +86,7 @@ def bertTokenizer(*args, **kwargs): ...@@ -86,7 +86,7 @@ def bertTokenizer(*args, **kwargs):
Example: Example:
>>> import torch >>> import torch
>>> sentence = 'Hello, World!' >>> sentence = 'Hello, World!'
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False) >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
>>> toks = tokenizer.tokenize(sentence) >>> toks = tokenizer.tokenize(sentence)
['Hello', '##,', 'World', '##!'] ['Hello', '##,', 'World', '##!']
>>> ids = tokenizer.convert_tokens_to_ids(toks) >>> ids = tokenizer.convert_tokens_to_ids(toks)
...@@ -106,7 +106,7 @@ def bertModel(*args, **kwargs): ...@@ -106,7 +106,7 @@ def bertModel(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False) >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input # Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]" >>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text) >>> tokenized_text = tokenizer.tokenize(text)
...@@ -115,7 +115,7 @@ def bertModel(*args, **kwargs): ...@@ -115,7 +115,7 @@ def bertModel(*args, **kwargs):
>>> tokens_tensor = torch.tensor([indexed_tokens]) >>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids]) >>> segments_tensors = torch.tensor([segments_ids])
# Load bertModel # Load bertModel
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertModel', 'bert-base-cased') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertModel', 'bert-base-cased')
>>> model.eval() >>> model.eval()
# Predict hidden states features for each layer # Predict hidden states features for each layer
>>> with torch.no_grad(): >>> with torch.no_grad():
...@@ -135,7 +135,7 @@ def bertForNextSentencePrediction(*args, **kwargs): ...@@ -135,7 +135,7 @@ def bertForNextSentencePrediction(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False) >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input # Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]" >>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text) >>> tokenized_text = tokenizer.tokenize(text)
...@@ -144,7 +144,7 @@ def bertForNextSentencePrediction(*args, **kwargs): ...@@ -144,7 +144,7 @@ def bertForNextSentencePrediction(*args, **kwargs):
>>> tokens_tensor = torch.tensor([indexed_tokens]) >>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids]) >>> segments_tensors = torch.tensor([segments_ids])
# Load bertForNextSentencePrediction # Load bertForNextSentencePrediction
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertForNextSentencePrediction', 'bert-base-cased') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForNextSentencePrediction', 'bert-base-cased')
>>> model.eval() >>> model.eval()
# Predict the next sentence classification logits # Predict the next sentence classification logits
>>> with torch.no_grad(): >>> with torch.no_grad():
...@@ -165,7 +165,7 @@ def bertForPreTraining(*args, **kwargs): ...@@ -165,7 +165,7 @@ def bertForPreTraining(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False) >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input # Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]" >>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text) >>> tokenized_text = tokenizer.tokenize(text)
...@@ -173,7 +173,7 @@ def bertForPreTraining(*args, **kwargs): ...@@ -173,7 +173,7 @@ def bertForPreTraining(*args, **kwargs):
>>> tokens_tensor = torch.tensor([indexed_tokens]) >>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids]) >>> segments_tensors = torch.tensor([segments_ids])
# Load bertForPreTraining # Load bertForPreTraining
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertForPreTraining', 'bert-base-cased') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForPreTraining', 'bert-base-cased')
>>> masked_lm_logits_scores, seq_relationship_logits = model(tokens_tensor, segments_tensors) >>> masked_lm_logits_scores, seq_relationship_logits = model(tokens_tensor, segments_tensors)
""" """
model = BertForPreTraining.from_pretrained(*args, **kwargs) model = BertForPreTraining.from_pretrained(*args, **kwargs)
...@@ -189,7 +189,7 @@ def bertForMaskedLM(*args, **kwargs): ...@@ -189,7 +189,7 @@ def bertForMaskedLM(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False) >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input # Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]" >>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text) >>> tokenized_text = tokenizer.tokenize(text)
...@@ -200,7 +200,7 @@ def bertForMaskedLM(*args, **kwargs): ...@@ -200,7 +200,7 @@ def bertForMaskedLM(*args, **kwargs):
>>> tokens_tensor = torch.tensor([indexed_tokens]) >>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids]) >>> segments_tensors = torch.tensor([segments_ids])
# Load bertForMaskedLM # Load bertForMaskedLM
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertForMaskedLM', 'bert-base-cased') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForMaskedLM', 'bert-base-cased')
>>> model.eval() >>> model.eval()
# Predict all tokens # Predict all tokens
>>> with torch.no_grad(): >>> with torch.no_grad():
...@@ -231,7 +231,7 @@ def bertForSequenceClassification(*args, **kwargs): ...@@ -231,7 +231,7 @@ def bertForSequenceClassification(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False) >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input # Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]" >>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text) >>> tokenized_text = tokenizer.tokenize(text)
...@@ -240,7 +240,7 @@ def bertForSequenceClassification(*args, **kwargs): ...@@ -240,7 +240,7 @@ def bertForSequenceClassification(*args, **kwargs):
>>> tokens_tensor = torch.tensor([indexed_tokens]) >>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids]) >>> segments_tensors = torch.tensor([segments_ids])
# Load bertForSequenceClassification # Load bertForSequenceClassification
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertForSequenceClassification', 'bert-base-cased', num_labels=2) >>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForSequenceClassification', 'bert-base-cased', num_labels=2)
>>> model.eval() >>> model.eval()
# Predict the sequence classification logits # Predict the sequence classification logits
>>> with torch.no_grad(): >>> with torch.no_grad():
...@@ -266,7 +266,7 @@ def bertForMultipleChoice(*args, **kwargs): ...@@ -266,7 +266,7 @@ def bertForMultipleChoice(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False) >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input # Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]" >>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text) >>> tokenized_text = tokenizer.tokenize(text)
...@@ -275,7 +275,7 @@ def bertForMultipleChoice(*args, **kwargs): ...@@ -275,7 +275,7 @@ def bertForMultipleChoice(*args, **kwargs):
>>> tokens_tensor = torch.tensor([indexed_tokens, indexed_tokens]).unsqueeze(0) >>> tokens_tensor = torch.tensor([indexed_tokens, indexed_tokens]).unsqueeze(0)
>>> segments_tensors = torch.tensor([segments_ids, segments_ids]).unsqueeze(0) >>> segments_tensors = torch.tensor([segments_ids, segments_ids]).unsqueeze(0)
# Load bertForMultipleChoice # Load bertForMultipleChoice
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertForMultipleChoice', 'bert-base-cased', num_choices=2) >>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForMultipleChoice', 'bert-base-cased', num_choices=2)
>>> model.eval() >>> model.eval()
# Predict the multiple choice logits # Predict the multiple choice logits
>>> with torch.no_grad(): >>> with torch.no_grad():
...@@ -299,7 +299,7 @@ def bertForQuestionAnswering(*args, **kwargs): ...@@ -299,7 +299,7 @@ def bertForQuestionAnswering(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False) >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input # Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]" >>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text) >>> tokenized_text = tokenizer.tokenize(text)
...@@ -308,7 +308,7 @@ def bertForQuestionAnswering(*args, **kwargs): ...@@ -308,7 +308,7 @@ def bertForQuestionAnswering(*args, **kwargs):
>>> tokens_tensor = torch.tensor([indexed_tokens]) >>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids]) >>> segments_tensors = torch.tensor([segments_ids])
# Load bertForQuestionAnswering # Load bertForQuestionAnswering
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertForQuestionAnswering', 'bert-base-cased') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForQuestionAnswering', 'bert-base-cased')
>>> model.eval() >>> model.eval()
# Predict the start and end positions logits # Predict the start and end positions logits
>>> with torch.no_grad(): >>> with torch.no_grad():
...@@ -338,7 +338,7 @@ def bertForTokenClassification(*args, **kwargs): ...@@ -338,7 +338,7 @@ def bertForTokenClassification(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False) >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input # Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]" >>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text) >>> tokenized_text = tokenizer.tokenize(text)
...@@ -347,7 +347,7 @@ def bertForTokenClassification(*args, **kwargs): ...@@ -347,7 +347,7 @@ def bertForTokenClassification(*args, **kwargs):
>>> tokens_tensor = torch.tensor([indexed_tokens]) >>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids]) >>> segments_tensors = torch.tensor([segments_ids])
# Load bertForTokenClassification # Load bertForTokenClassification
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertForTokenClassification', 'bert-base-cased', num_labels=2) >>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForTokenClassification', 'bert-base-cased', num_labels=2)
>>> model.eval() >>> model.eval()
# Predict the token classification logits # Predict the token classification logits
>>> with torch.no_grad(): >>> with torch.no_grad():
......
hubconfs/gpt2_hubconf.py
View file @ e468192e
from pytorch_pretrained_bert.tokenization_gpt2 import GPT2Tokenizer from pytorch_transformers.tokenization_gpt2 import GPT2Tokenizer
from pytorch_pretrained_bert.modeling_gpt2 import ( from pytorch_transformers.modeling_gpt2 import (
GPT2Model, GPT2Model,
GPT2LMHeadModel, GPT2LMHeadModel,
GPT2DoubleHeadsModel GPT2DoubleHeadsModel
...@@ -53,7 +53,7 @@ def gpt2Tokenizer(*args, **kwargs): ...@@ -53,7 +53,7 @@ def gpt2Tokenizer(*args, **kwargs):
Example: Example:
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'gpt2Tokenizer', 'gpt2') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
>>> text = "Who was Jim Henson ?" >>> text = "Who was Jim Henson ?"
>>> indexed_tokens = tokenizer.encode(tokenized_text) >>> indexed_tokens = tokenizer.encode(tokenized_text)
...@@ -72,7 +72,7 @@ def gpt2Model(*args, **kwargs): ...@@ -72,7 +72,7 @@ def gpt2Model(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'gpt2Tokenizer', 'gpt2') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
# Prepare tokenized input # Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?" >>> text_1 = "Who was Jim Henson ?"
...@@ -83,7 +83,7 @@ def gpt2Model(*args, **kwargs): ...@@ -83,7 +83,7 @@ def gpt2Model(*args, **kwargs):
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2]) >>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load gpt2Model # Load gpt2Model
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'gpt2Model', 'gpt2') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Model', 'gpt2')
>>> model.eval() >>> model.eval()
# Predict hidden states features for each layer # Predict hidden states features for each layer
...@@ -105,7 +105,7 @@ def gpt2LMHeadModel(*args, **kwargs): ...@@ -105,7 +105,7 @@ def gpt2LMHeadModel(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'gpt2Tokenizer', 'gpt2') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
# Prepare tokenized input # Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?" >>> text_1 = "Who was Jim Henson ?"
...@@ -116,7 +116,7 @@ def gpt2LMHeadModel(*args, **kwargs): ...@@ -116,7 +116,7 @@ def gpt2LMHeadModel(*args, **kwargs):
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2]) >>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load gpt2LMHeadModel # Load gpt2LMHeadModel
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'gpt2LMHeadModel', 'gpt2') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2LMHeadModel', 'gpt2')
>>> model.eval() >>> model.eval()
# Predict hidden states features for each layer # Predict hidden states features for each layer
...@@ -144,7 +144,7 @@ def gpt2DoubleHeadsModel(*args, **kwargs): ...@@ -144,7 +144,7 @@ def gpt2DoubleHeadsModel(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'gpt2Tokenizer', 'gpt2') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
# Prepare tokenized input # Prepare tokenized input
>>> text1 = "Who was Jim Henson ? Jim Henson was a puppeteer" >>> text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
...@@ -157,7 +157,7 @@ def gpt2DoubleHeadsModel(*args, **kwargs): ...@@ -157,7 +157,7 @@ def gpt2DoubleHeadsModel(*args, **kwargs):
>>> mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]]) >>> mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
# Load gpt2DoubleHeadsModel # Load gpt2DoubleHeadsModel
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'gpt2DoubleHeadsModel', 'gpt2') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2DoubleHeadsModel', 'gpt2')
>>> model.eval() >>> model.eval()
# Predict hidden states features for each layer # Predict hidden states features for each layer
......
hubconfs/gpt_hubconf.py
View file @ e468192e
from pytorch_pretrained_bert.tokenization_openai import OpenAIGPTTokenizer from pytorch_transformers.tokenization_openai import OpenAIGPTTokenizer
from pytorch_pretrained_bert.modeling_openai import ( from pytorch_transformers.modeling_openai import (
OpenAIGPTModel, OpenAIGPTModel,
OpenAIGPTLMHeadModel, OpenAIGPTLMHeadModel,
OpenAIGPTDoubleHeadsModel OpenAIGPTDoubleHeadsModel
...@@ -77,7 +77,7 @@ def openAIGPTTokenizer(*args, **kwargs): ...@@ -77,7 +77,7 @@ def openAIGPTTokenizer(*args, **kwargs):
Example: Example:
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'openAIGPTTokenizer', 'openai-gpt') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
>>> text = "Who was Jim Henson ? Jim Henson was a puppeteer" >>> text = "Who was Jim Henson ? Jim Henson was a puppeteer"
>>> tokenized_text = tokenizer.tokenize(text) >>> tokenized_text = tokenizer.tokenize(text)
...@@ -98,7 +98,7 @@ def openAIGPTModel(*args, **kwargs): ...@@ -98,7 +98,7 @@ def openAIGPTModel(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'openAIGPTTokenizer', 'openai-gpt') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
# Prepare tokenized input # Prepare tokenized input
>>> text = "Who was Jim Henson ? Jim Henson was a puppeteer" >>> text = "Who was Jim Henson ? Jim Henson was a puppeteer"
...@@ -107,7 +107,7 @@ def openAIGPTModel(*args, **kwargs): ...@@ -107,7 +107,7 @@ def openAIGPTModel(*args, **kwargs):
>>> tokens_tensor = torch.tensor([indexed_tokens]) >>> tokens_tensor = torch.tensor([indexed_tokens])
# Load openAIGPTModel # Load openAIGPTModel
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'openAIGPTModel', 'openai-gpt') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTModel', 'openai-gpt')
>>> model.eval() >>> model.eval()
# Predict hidden states features for each layer # Predict hidden states features for each layer
...@@ -127,7 +127,7 @@ def openAIGPTLMHeadModel(*args, **kwargs): ...@@ -127,7 +127,7 @@ def openAIGPTLMHeadModel(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'openAIGPTTokenizer', 'openai-gpt') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
# Prepare tokenized input # Prepare tokenized input
>>> text = "Who was Jim Henson ? Jim Henson was a puppeteer" >>> text = "Who was Jim Henson ? Jim Henson was a puppeteer"
...@@ -136,7 +136,7 @@ def openAIGPTLMHeadModel(*args, **kwargs): ...@@ -136,7 +136,7 @@ def openAIGPTLMHeadModel(*args, **kwargs):
>>> tokens_tensor = torch.tensor([indexed_tokens]) >>> tokens_tensor = torch.tensor([indexed_tokens])
# Load openAIGPTLMHeadModel # Load openAIGPTLMHeadModel
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'openAIGPTLMHeadModel', 'openai-gpt') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTLMHeadModel', 'openai-gpt')
>>> model.eval() >>> model.eval()
# Predict hidden states features for each layer # Predict hidden states features for each layer
...@@ -162,7 +162,7 @@ def openAIGPTDoubleHeadsModel(*args, **kwargs): ...@@ -162,7 +162,7 @@ def openAIGPTDoubleHeadsModel(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'openAIGPTTokenizer', 'openai-gpt') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
# Prepare tokenized input # Prepare tokenized input
>>> text1 = "Who was Jim Henson ? Jim Henson was a puppeteer" >>> text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
...@@ -175,7 +175,7 @@ def openAIGPTDoubleHeadsModel(*args, **kwargs): ...@@ -175,7 +175,7 @@ def openAIGPTDoubleHeadsModel(*args, **kwargs):
>>> mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]]) >>> mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
# Load openAIGPTDoubleHeadsModel # Load openAIGPTDoubleHeadsModel
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'openAIGPTDoubleHeadsModel', 'openai-gpt') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTDoubleHeadsModel', 'openai-gpt')
>>> model.eval() >>> model.eval()
# Predict hidden states features for each layer # Predict hidden states features for each layer
......
hubconfs/transformer_xl_hubconf.py
View file @ e468192e
from pytorch_pretrained_bert.tokenization_transfo_xl import TransfoXLTokenizer from pytorch_transformers.tokenization_transfo_xl import TransfoXLTokenizer
from pytorch_pretrained_bert.modeling_transfo_xl import ( from pytorch_transformers.modeling_transfo_xl import (
TransfoXLModel, TransfoXLModel,
TransfoXLLMHeadModel TransfoXLLMHeadModel
) )
...@@ -46,7 +46,7 @@ def transformerXLTokenizer(*args, **kwargs): ...@@ -46,7 +46,7 @@ def transformerXLTokenizer(*args, **kwargs):
Example: Example:
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'transformerXLTokenizer', 'transfo-xl-wt103') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLTokenizer', 'transfo-xl-wt103')
>>> text = "Who was Jim Henson ?" >>> text = "Who was Jim Henson ?"
>>> tokenized_text = tokenizer.tokenize(tokenized_text) >>> tokenized_text = tokenizer.tokenize(tokenized_text)
...@@ -64,7 +64,7 @@ def transformerXLModel(*args, **kwargs): ...@@ -64,7 +64,7 @@ def transformerXLModel(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'transformerXLTokenizer', 'transfo-xl-wt103') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLTokenizer', 'transfo-xl-wt103')
# Prepare tokenized input # Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?" >>> text_1 = "Who was Jim Henson ?"
...@@ -77,7 +77,7 @@ def transformerXLModel(*args, **kwargs): ...@@ -77,7 +77,7 @@ def transformerXLModel(*args, **kwargs):
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2]) >>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load transformerXLModel # Load transformerXLModel
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'transformerXLModel', 'transfo-xl-wt103') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLModel', 'transfo-xl-wt103')
>>> model.eval() >>> model.eval()
# Predict hidden states features for each layer # Predict hidden states features for each layer
...@@ -99,7 +99,7 @@ def transformerXLLMHeadModel(*args, **kwargs): ...@@ -99,7 +99,7 @@ def transformerXLLMHeadModel(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'transformerXLTokenizer', 'transfo-xl-wt103') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLTokenizer', 'transfo-xl-wt103')
# Prepare tokenized input # Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?" >>> text_1 = "Who was Jim Henson ?"
...@@ -112,7 +112,7 @@ def transformerXLLMHeadModel(*args, **kwargs): ...@@ -112,7 +112,7 @@ def transformerXLLMHeadModel(*args, **kwargs):
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2]) >>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load transformerXLLMHeadModel # Load transformerXLLMHeadModel
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'transformerXLLMHeadModel', 'transfo-xl-wt103') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLLMHeadModel', 'transfo-xl-wt103')
>>> model.eval() >>> model.eval()
# Predict hidden states features for each layer # Predict hidden states features for each layer
......
hubconfs/xlm_hubconf.py
View file @ e468192e
from pytorch_pretrained_bert.tokenization_xlm import XLMTokenizer from pytorch_transformers.tokenization_xlm import XLMTokenizer
from pytorch_pretrained_bert.modeling_xlm import ( from pytorch_transformers.modeling_xlm import (
XLMConfig, XLMConfig,
XLMModel, XLMModel,
XLMWithLMHeadModel, XLMWithLMHeadModel,
...@@ -18,7 +18,7 @@ xlm_start_docstring = """ ...@@ -18,7 +18,7 @@ xlm_start_docstring = """
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlmTokenizer', 'xlm-mlm-en-2048') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlmTokenizer', 'xlm-mlm-en-2048')
# Prepare tokenized input # Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?" >>> text_1 = "Who was Jim Henson ?"
...@@ -77,7 +77,7 @@ def xlmTokenizer(*args, **kwargs): ...@@ -77,7 +77,7 @@ def xlmTokenizer(*args, **kwargs):
Example: Example:
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlmTokenizer', 'xlm-mlm-en-2048') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlmTokenizer', 'xlm-mlm-en-2048')
>>> text = "Who was Jim Henson ?" >>> text = "Who was Jim Henson ?"
>>> indexed_tokens = tokenizer.encode(tokenized_text) >>> indexed_tokens = tokenizer.encode(tokenized_text)
...@@ -91,7 +91,7 @@ def xlmTokenizer(*args, **kwargs): ...@@ -91,7 +91,7 @@ def xlmTokenizer(*args, **kwargs):
def xlmModel(*args, **kwargs): def xlmModel(*args, **kwargs):
""" """
# Load xlmModel # Load xlmModel
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlmModel', 'xlm-mlm-en-2048') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'xlmModel', 'xlm-mlm-en-2048')
>>> model.eval() >>> model.eval()
# Predict hidden states features for each layer # Predict hidden states features for each layer
...@@ -116,7 +116,7 @@ def xlmLMHeadModel(*args, **kwargs): ...@@ -116,7 +116,7 @@ def xlmLMHeadModel(*args, **kwargs):
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2]) >>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load xlnetLMHeadModel # Load xlnetLMHeadModel
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlnetLMHeadModel', 'xlm-mlm-en-2048') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetLMHeadModel', 'xlm-mlm-en-2048')
>>> model.eval() >>> model.eval()
# Predict hidden states features for each layer # Predict hidden states features for each layer
...@@ -143,7 +143,7 @@ def xlmLMHeadModel(*args, **kwargs): ...@@ -143,7 +143,7 @@ def xlmLMHeadModel(*args, **kwargs):
# Example: # Example:
# # Load the tokenizer # # Load the tokenizer
# >>> import torch # >>> import torch
# >>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlnetTokenizer', 'xlm-mlm-en-2048') # >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlm-mlm-en-2048')
# # Prepare tokenized input # # Prepare tokenized input
# >>> text1 = "Who was Jim Henson ? Jim Henson was a puppeteer" # >>> text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
...@@ -156,7 +156,7 @@ def xlmLMHeadModel(*args, **kwargs): ...@@ -156,7 +156,7 @@ def xlmLMHeadModel(*args, **kwargs):
# >>> mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]]) # >>> mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
# # Load xlnetForSequenceClassification # # Load xlnetForSequenceClassification
# >>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlnetForSequenceClassification', 'xlm-mlm-en-2048') # >>> model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetForSequenceClassification', 'xlm-mlm-en-2048')
# >>> model.eval() # >>> model.eval()
# # Predict sequence classes logits # # Predict sequence classes logits
......
hubconfs/xlnet_hubconf.1.py
View file @ e468192e
from pytorch_pretrained_bert.tokenization_xlnet import XLNetTokenizer from pytorch_transformers.tokenization_xlnet import XLNetTokenizer
from pytorch_pretrained_bert.modeling_xlnet import ( from pytorch_transformers.modeling_xlnet import (
XLNetConfig, XLNetConfig,
XLNetModel, XLNetModel,
XLNetLMHeadModel, XLNetLMHeadModel,
...@@ -54,7 +54,7 @@ def xlnetTokenizer(*args, **kwargs): ...@@ -54,7 +54,7 @@ def xlnetTokenizer(*args, **kwargs):
Example: Example:
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlnetTokenizer', 'xlnet-large-cased') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
>>> text = "Who was Jim Henson ?" >>> text = "Who was Jim Henson ?"
>>> indexed_tokens = tokenizer.encode(tokenized_text) >>> indexed_tokens = tokenizer.encode(tokenized_text)
...@@ -73,7 +73,7 @@ def xlnetModel(*args, **kwargs): ...@@ -73,7 +73,7 @@ def xlnetModel(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlnetTokenizer', 'xlnet-large-cased') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
# Prepare tokenized input # Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?" >>> text_1 = "Who was Jim Henson ?"
...@@ -84,7 +84,7 @@ def xlnetModel(*args, **kwargs): ...@@ -84,7 +84,7 @@ def xlnetModel(*args, **kwargs):
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2]) >>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load xlnetModel # Load xlnetModel
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlnetModel', 'xlnet-large-cased') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetModel', 'xlnet-large-cased')
>>> model.eval() >>> model.eval()
# Predict hidden states features for each layer # Predict hidden states features for each layer
...@@ -107,7 +107,7 @@ def xlnetLMHeadModel(*args, **kwargs): ...@@ -107,7 +107,7 @@ def xlnetLMHeadModel(*args, **kwargs):
Example: Example:
# Load the tokenizer # Load the tokenizer
>>> import torch >>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlnetTokenizer', 'xlnet-large-cased') >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
# Prepare tokenized input # Prepare tokenized input
>>> text_1 = "Who was Jim Henson ?" >>> text_1 = "Who was Jim Henson ?"
...@@ -118,7 +118,7 @@ def xlnetLMHeadModel(*args, **kwargs): ...@@ -118,7 +118,7 @@ def xlnetLMHeadModel(*args, **kwargs):
>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2]) >>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load xlnetLMHeadModel # Load xlnetLMHeadModel
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlnetLMHeadModel', 'xlnet-large-cased') >>> model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetLMHeadModel', 'xlnet-large-cased')
>>> model.eval() >>> model.eval()
# Predict hidden states features for each layer # Predict hidden states features for each layer
...@@ -145,7 +145,7 @@ def xlnetLMHeadModel(*args, **kwargs): ...@@ -145,7 +145,7 @@ def xlnetLMHeadModel(*args, **kwargs):
# Example: # Example:
# # Load the tokenizer # # Load the tokenizer
# >>> import torch # >>> import torch
# >>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlnetTokenizer', 'xlnet-large-cased') # >>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
# # Prepare tokenized input # # Prepare tokenized input
# >>> text1 = "Who was Jim Henson ? Jim Henson was a puppeteer" # >>> text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
...@@ -158,7 +158,7 @@ def xlnetLMHeadModel(*args, **kwargs): ...@@ -158,7 +158,7 @@ def xlnetLMHeadModel(*args, **kwargs):
# >>> mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]]) # >>> mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
# # Load xlnetForSequenceClassification # # Load xlnetForSequenceClassification
# >>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlnetForSequenceClassification', 'xlnet-large-cased') # >>> model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetForSequenceClassification', 'xlnet-large-cased')
# >>> model.eval() # >>> model.eval()
# # Predict sequence classes logits # # Predict sequence classes logits
......
notebooks/Comparing-TF-and-PT-models-MLM-NSP.ipynb
View file @ e468192e
...@@ -78,7 +78,7 @@ ...@@ -78,7 +78,7 @@
"import importlib.util\n", "import importlib.util\n",
"import sys\n", "import sys\n",
"import tensorflow as tf\n", "import tensorflow as tf\n",
"import pytorch_pretrained_bert as ppb\n", "import pytorch_transformers as ppb\n",
"\n", "\n",
"def del_all_flags(FLAGS):\n", "def del_all_flags(FLAGS):\n",
" flags_dict = FLAGS._flags() \n", " flags_dict = FLAGS._flags() \n",
...@@ -3997,9 +3997,9 @@ ...@@ -3997,9 +3997,9 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"11/16/2018 11:03:05 - INFO - pytorch_pretrained_bert.modeling_bert - loading archive file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz from cache at /Users/thomaswolf/.pytorch_pretrained_bert/9c41111e2de84547a463fd39217199738d1e3deb72d4fec4399e6e241983c6f0.ae3cef932725ca7a30cdcb93fc6e09150a55e2a130ec7af63975a16c153ae2ba\n", "11/16/2018 11:03:05 - INFO - pytorch_transformers.modeling_bert - loading archive file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz from cache at /Users/thomaswolf/.pytorch_transformers/9c41111e2de84547a463fd39217199738d1e3deb72d4fec4399e6e241983c6f0.ae3cef932725ca7a30cdcb93fc6e09150a55e2a130ec7af63975a16c153ae2ba\n",
"11/16/2018 11:03:05 - INFO - pytorch_pretrained_bert.modeling_bert - extracting archive file /Users/thomaswolf/.pytorch_pretrained_bert/9c41111e2de84547a463fd39217199738d1e3deb72d4fec4399e6e241983c6f0.ae3cef932725ca7a30cdcb93fc6e09150a55e2a130ec7af63975a16c153ae2ba to temp dir /var/folders/yx/cw8n_njx3js5jksyw_qlp8p00000gn/T/tmpaqgsm566\n", "11/16/2018 11:03:05 - INFO - pytorch_transformers.modeling_bert - extracting archive file /Users/thomaswolf/.pytorch_transformers/9c41111e2de84547a463fd39217199738d1e3deb72d4fec4399e6e241983c6f0.ae3cef932725ca7a30cdcb93fc6e09150a55e2a130ec7af63975a16c153ae2ba to temp dir /var/folders/yx/cw8n_njx3js5jksyw_qlp8p00000gn/T/tmpaqgsm566\n",
"11/16/2018 11:03:08 - INFO - pytorch_pretrained_bert.modeling_bert - Model config {\n", "11/16/2018 11:03:08 - INFO - pytorch_transformers.modeling_bert - Model config {\n",
" \"attention_probs_dropout_prob\": 0.1,\n", " \"attention_probs_dropout_prob\": 0.1,\n",
" \"hidden_act\": \"gelu\",\n", " \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n", " \"hidden_dropout_prob\": 0.1,\n",
......
notebooks/Comparing-TF-and-PT-models.ipynb
View file @ e468192e
...@@ -342,7 +342,7 @@ ...@@ -342,7 +342,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"import extract_features\n", "import extract_features\n",
"import pytorch_pretrained_bert as ppb\n", "import pytorch_transformers as ppb\n",
"from extract_features import *" "from extract_features import *"
] ]
}, },
...@@ -375,8 +375,8 @@ ...@@ -375,8 +375,8 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"11/15/2018 16:21:18 - INFO - pytorch_pretrained_bert.modeling_bert - loading archive file ../../google_models/uncased_L-12_H-768_A-12/\n", "11/15/2018 16:21:18 - INFO - pytorch_transformers.modeling_bert - loading archive file ../../google_models/uncased_L-12_H-768_A-12/\n",
"11/15/2018 16:21:18 - INFO - pytorch_pretrained_bert.modeling_bert - Model config {\n", "11/15/2018 16:21:18 - INFO - pytorch_transformers.modeling_bert - Model config {\n",
" \"attention_probs_dropout_prob\": 0.1,\n", " \"attention_probs_dropout_prob\": 0.1,\n",
" \"hidden_act\": \"gelu\",\n", " \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n", " \"hidden_dropout_prob\": 0.1,\n",
......
pytorch_pretrained_bert/__init__.py → pytorch_transformers/__init__.py
View file @ e468192e
__version__ = "0.6.2" __version__ = "0.7.0"
from .tokenization_bert import BertTokenizer, BasicTokenizer, WordpieceTokenizer from .tokenization_bert import BertTokenizer, BasicTokenizer, WordpieceTokenizer
from .tokenization_openai import OpenAIGPTTokenizer from .tokenization_openai import OpenAIGPTTokenizer
from .tokenization_transfo_xl import (TransfoXLTokenizer, TransfoXLCorpus) from .tokenization_transfo_xl import (TransfoXLTokenizer, TransfoXLCorpus)
from .tokenization_gpt2 import GPT2Tokenizer from .tokenization_gpt2 import GPT2Tokenizer
from .tokenization_xlnet import XLNetTokenizer, SPIECE_UNDERLINE from .tokenization_xlnet import XLNetTokenizer, SPIECE_UNDERLINE
from .tokenization_xlm import XLMTokenizer from .tokenization_xlm import XLMTokenizer
from .tokenization_utils import (PreTrainedTokenizer, clean_up_tokenization)
from .modeling_bert import (BertConfig, BertModel, BertForPreTraining, from .modeling_bert import (BertConfig, BertModel, BertForPreTraining,
BertForMaskedLM, BertForNextSentencePrediction, BertForMaskedLM, BertForNextSentencePrediction,
BertForSequenceClassification, BertForMultipleChoice, BertForSequenceClassification, BertForMultipleChoice,
BertForTokenClassification, BertForQuestionAnswering, BertForTokenClassification, BertForQuestionAnswering,
load_tf_weights_in_bert) load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP)
from .modeling_openai import (OpenAIGPTConfig, OpenAIGPTModel, from .modeling_openai import (OpenAIGPTConfig, OpenAIGPTModel,
OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel,
load_tf_weights_in_openai_gpt) load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_transfo_xl import (TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel, from .modeling_transfo_xl import (TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel,
load_tf_weights_in_transfo_xl) load_tf_weights_in_transfo_xl, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_gpt2 import (GPT2Config, GPT2Model, from .modeling_gpt2 import (GPT2Config, GPT2Model,
GPT2LMHeadModel, GPT2DoubleHeadsModel, GPT2LMHeadModel, GPT2DoubleHeadsModel,
load_tf_weights_in_gpt2) load_tf_weights_in_gpt2, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_xlnet import (XLNetConfig, from .modeling_xlnet import (XLNetConfig,
XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel, XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
XLNetForSequenceClassification, XLNetForQuestionAnswering, XLNetForSequenceClassification, XLNetForQuestionAnswering,
load_tf_weights_in_xlnet) load_tf_weights_in_xlnet, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_xlm import (XLMConfig, XLMModel, from .modeling_xlm import (XLMConfig, XLMModel,
XLMWithLMHeadModel, XLMForSequenceClassification, XLMWithLMHeadModel, XLMForSequenceClassification,
XLMForQuestionAnswering) XLMForQuestionAnswering, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_utils import (WEIGHTS_NAME, CONFIG_NAME, TF_WEIGHTS_NAME,
PretrainedConfig, PreTrainedModel, prune_layer, Conv1D)
from .optimization import BertAdam from .optimization import BertAdam
from .optimization_openai import OpenAIAdam from .optimization_openai import OpenAIAdam
from .file_utils import (PYTORCH_PRETRAINED_BERT_CACHE, cached_path) from .file_utils import (PYTORCH_PRETRAINED_BERT_CACHE, cached_path)
from .model_utils import (WEIGHTS_NAME, CONFIG_NAME, TF_WEIGHTS_NAME,
PretrainedConfig, PreTrainedModel, prune_layer, Conv1D)
pytorch_pretrained_bert/__main__.py → pytorch_transformers/__main__.py
View file @ e468192e
...@@ -4,24 +4,24 @@ def main(): ...@@ -4,24 +4,24 @@ def main():
if (len(sys.argv) < 4 or len(sys.argv) > 6) or sys.argv[1] not in ["bert", "gpt", "transfo_xl", "gpt2", "xlnet"]: if (len(sys.argv) < 4 or len(sys.argv) > 6) or sys.argv[1] not in ["bert", "gpt", "transfo_xl", "gpt2", "xlnet"]:
print( print(
"Should be used as one of: \n" "Should be used as one of: \n"
">> `pytorch_pretrained_bert bert TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT`, \n" ">> `pytorch_transformers bert TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT`, \n"
">> `pytorch_pretrained_bert gpt OPENAI_GPT_CHECKPOINT_FOLDER_PATH PYTORCH_DUMP_OUTPUT [OPENAI_GPT_CONFIG]`, \n" ">> `pytorch_transformers gpt OPENAI_GPT_CHECKPOINT_FOLDER_PATH PYTORCH_DUMP_OUTPUT [OPENAI_GPT_CONFIG]`, \n"
">> `pytorch_pretrained_bert transfo_xl TF_CHECKPOINT_OR_DATASET PYTORCH_DUMP_OUTPUT [TF_CONFIG]` or \n" ">> `pytorch_transformers transfo_xl TF_CHECKPOINT_OR_DATASET PYTORCH_DUMP_OUTPUT [TF_CONFIG]` or \n"
">> `pytorch_pretrained_bert gpt2 TF_CHECKPOINT PYTORCH_DUMP_OUTPUT [GPT2_CONFIG]` or \n" ">> `pytorch_transformers gpt2 TF_CHECKPOINT PYTORCH_DUMP_OUTPUT [GPT2_CONFIG]` or \n"
">> `pytorch_pretrained_bert xlnet TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT [FINETUNING_TASK_NAME]`") ">> `pytorch_transformers xlnet TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT [FINETUNING_TASK_NAME]`")
else: else:
if sys.argv[1] == "bert": if sys.argv[1] == "bert":
try: try:
from .convert_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch from .convert_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError: except ImportError:
print("pytorch_pretrained_bert can only be used from the commandline to convert TensorFlow models in PyTorch, " print("pytorch_transformers can only be used from the commandline to convert TensorFlow models in PyTorch, "
"In that case, it requires TensorFlow to be installed. Please see " "In that case, it requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions.") "https://www.tensorflow.org/install/ for installation instructions.")
raise raise
if len(sys.argv) != 5: if len(sys.argv) != 5:
# pylint: disable=line-too-long # pylint: disable=line-too-long
print("Should be used as `pytorch_pretrained_bert bert TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT`") print("Should be used as `pytorch_transformers bert TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT`")
else: else:
PYTORCH_DUMP_OUTPUT = sys.argv.pop() PYTORCH_DUMP_OUTPUT = sys.argv.pop()
TF_CONFIG = sys.argv.pop() TF_CONFIG = sys.argv.pop()
...@@ -31,7 +31,7 @@ def main(): ...@@ -31,7 +31,7 @@ def main():
from .convert_openai_checkpoint_to_pytorch import convert_openai_checkpoint_to_pytorch from .convert_openai_checkpoint_to_pytorch import convert_openai_checkpoint_to_pytorch
if len(sys.argv) < 4 or len(sys.argv) > 5: if len(sys.argv) < 4 or len(sys.argv) > 5:
# pylint: disable=line-too-long # pylint: disable=line-too-long
print("Should be used as `pytorch_pretrained_bert gpt OPENAI_GPT_CHECKPOINT_FOLDER_PATH PYTORCH_DUMP_OUTPUT [OPENAI_GPT_CONFIG]`") print("Should be used as `pytorch_transformers gpt OPENAI_GPT_CHECKPOINT_FOLDER_PATH PYTORCH_DUMP_OUTPUT [OPENAI_GPT_CONFIG]`")
else: else:
OPENAI_GPT_CHECKPOINT_FOLDER_PATH = sys.argv[2] OPENAI_GPT_CHECKPOINT_FOLDER_PATH = sys.argv[2]
PYTORCH_DUMP_OUTPUT = sys.argv[3] PYTORCH_DUMP_OUTPUT = sys.argv[3]
...@@ -46,13 +46,13 @@ def main(): ...@@ -46,13 +46,13 @@ def main():
try: try:
from .convert_transfo_xl_checkpoint_to_pytorch import convert_transfo_xl_checkpoint_to_pytorch from .convert_transfo_xl_checkpoint_to_pytorch import convert_transfo_xl_checkpoint_to_pytorch
except ImportError: except ImportError:
print("pytorch_pretrained_bert can only be used from the commandline to convert TensorFlow models in PyTorch, " print("pytorch_transformers can only be used from the commandline to convert TensorFlow models in PyTorch, "
"In that case, it requires TensorFlow to be installed. Please see " "In that case, it requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions.") "https://www.tensorflow.org/install/ for installation instructions.")
raise raise
if len(sys.argv) < 4 or len(sys.argv) > 5: if len(sys.argv) < 4 or len(sys.argv) > 5:
# pylint: disable=line-too-long # pylint: disable=line-too-long
print("Should be used as `pytorch_pretrained_bert transfo_xl TF_CHECKPOINT/TF_DATASET_FILE PYTORCH_DUMP_OUTPUT [TF_CONFIG]`") print("Should be used as `pytorch_transformers transfo_xl TF_CHECKPOINT/TF_DATASET_FILE PYTORCH_DUMP_OUTPUT [TF_CONFIG]`")
else: else:
if 'ckpt' in sys.argv[2].lower(): if 'ckpt' in sys.argv[2].lower():
TF_CHECKPOINT = sys.argv[2] TF_CHECKPOINT = sys.argv[2]
...@@ -70,14 +70,14 @@ def main(): ...@@ -70,14 +70,14 @@ def main():
try: try:
from .convert_gpt2_checkpoint_to_pytorch import convert_gpt2_checkpoint_to_pytorch from .convert_gpt2_checkpoint_to_pytorch import convert_gpt2_checkpoint_to_pytorch
except ImportError: except ImportError:
print("pytorch_pretrained_bert can only be used from the commandline to convert TensorFlow models in PyTorch, " print("pytorch_transformers can only be used from the commandline to convert TensorFlow models in PyTorch, "
"In that case, it requires TensorFlow to be installed. Please see " "In that case, it requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions.") "https://www.tensorflow.org/install/ for installation instructions.")
raise raise
if len(sys.argv) < 4 or len(sys.argv) > 5: if len(sys.argv) < 4 or len(sys.argv) > 5:
# pylint: disable=line-too-long # pylint: disable=line-too-long
print("Should be used as `pytorch_pretrained_bert gpt2 TF_CHECKPOINT PYTORCH_DUMP_OUTPUT [TF_CONFIG]`") print("Should be used as `pytorch_transformers gpt2 TF_CHECKPOINT PYTORCH_DUMP_OUTPUT [TF_CONFIG]`")
else: else:
TF_CHECKPOINT = sys.argv[2] TF_CHECKPOINT = sys.argv[2]
PYTORCH_DUMP_OUTPUT = sys.argv[3] PYTORCH_DUMP_OUTPUT = sys.argv[3]
...@@ -90,14 +90,14 @@ def main(): ...@@ -90,14 +90,14 @@ def main():
try: try:
from .convert_xlnet_checkpoint_to_pytorch import convert_xlnet_checkpoint_to_pytorch from .convert_xlnet_checkpoint_to_pytorch import convert_xlnet_checkpoint_to_pytorch
except ImportError: except ImportError:
print("pytorch_pretrained_bert can only be used from the commandline to convert TensorFlow models in PyTorch, " print("pytorch_transformers can only be used from the commandline to convert TensorFlow models in PyTorch, "
"In that case, it requires TensorFlow to be installed. Please see " "In that case, it requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions.") "https://www.tensorflow.org/install/ for installation instructions.")
raise raise
if len(sys.argv) < 5 or len(sys.argv) > 6: if len(sys.argv) < 5 or len(sys.argv) > 6:
# pylint: disable=line-too-long # pylint: disable=line-too-long
print("Should be used as `pytorch_pretrained_bert xlnet TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT [FINETUNING_TASK_NAME]`") print("Should be used as `pytorch_transformers xlnet TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT [FINETUNING_TASK_NAME]`")
else: else:
TF_CHECKPOINT = sys.argv[2] TF_CHECKPOINT = sys.argv[2]
TF_CONFIG = sys.argv[3] TF_CONFIG = sys.argv[3]
......
pytorch_pretrained_bert/convert_gpt2_checkpoint_to_pytorch.py → pytorch_transformers/convert_gpt2_checkpoint_to_pytorch.py
View file @ e468192e
...@@ -21,7 +21,7 @@ from io import open ...@@ -21,7 +21,7 @@ from io import open
import torch import torch
from pytorch_pretrained_bert.modeling_gpt2 import (CONFIG_NAME, WEIGHTS_NAME, from pytorch_transformers.modeling_gpt2 import (CONFIG_NAME, WEIGHTS_NAME,
GPT2Config, GPT2Config,
GPT2Model, GPT2Model,
load_tf_weights_in_gpt2) load_tf_weights_in_gpt2)
......
pytorch_pretrained_bert/convert_openai_checkpoint_to_pytorch.py → pytorch_transformers/convert_openai_checkpoint_to_pytorch.py
View file @ e468192e
...@@ -21,7 +21,7 @@ from io import open ...@@ -21,7 +21,7 @@ from io import open
import torch import torch
from pytorch_pretrained_bert.modeling_openai import (CONFIG_NAME, WEIGHTS_NAME, from pytorch_transformers.modeling_openai import (CONFIG_NAME, WEIGHTS_NAME,
OpenAIGPTConfig, OpenAIGPTConfig,
OpenAIGPTModel, OpenAIGPTModel,
load_tf_weights_in_openai_gpt) load_tf_weights_in_openai_gpt)
......
pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py → pytorch_transformers/convert_tf_checkpoint_to_pytorch.py
View file @ e468192e
...@@ -25,7 +25,7 @@ import tensorflow as tf ...@@ -25,7 +25,7 @@ import tensorflow as tf
import torch import torch
import numpy as np import numpy as np
from pytorch_pretrained_bert.modeling_bert import BertConfig, BertForPreTraining, load_tf_weights_in_bert from pytorch_transformers.modeling_bert import BertConfig, BertForPreTraining, load_tf_weights_in_bert
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path): def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
# Initialise PyTorch model # Initialise PyTorch model
......
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