Merge pull request from microsoft/hlu/remove_pytorch_transformers_dependency

Remove pytorch-transformers from dependencies.
This commit is contained in:
Miguel González-Fierro 2019-11-08 20:33:42 +00:00 коммит произвёл GitHub
Родитель fdabbe8004 f362d15566
Коммит c556e978f7
Не найден ключ, соответствующий данной подписи
Идентификатор ключа GPG: 4AEE18F83AFDEB23
12 изменённых файлов: 20 добавлений и 30 удалений

Просмотреть файл

@ -18,7 +18,7 @@ General Public License.
--
https://github.com/huggingface/pytorch-transformers
https://github.com/huggingface/transformers
Apache License
Version 2.0, January 2004
@ -664,3 +664,5 @@ https://github.com/allenai/bi-att-flow
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.

Просмотреть файл

@ -80,7 +80,7 @@ The following is a list of related repositories that we like and think are usefu
|||
|---|---|
|[pytorch-transformers](https://github.com/huggingface/pytorch-transformers)|A great PyTorch library from Hugging Face with implementations of popular transformer-based models. We've been using their package extensively in this repo and greatly appreciate their effort.|
|[transformers](https://github.com/huggingface/transformers)|A great PyTorch library from Hugging Face with implementations of popular transformer-based models. We've been using their package extensively in this repo and greatly appreciate their effort.|
|[Azure Machine Learning Notebooks](https://github.com/Azure/MachineLearningNotebooks/)|ML and deep learning examples with Azure Machine Learning.|
|[AzureML-BERT](https://github.com/Microsoft/AzureML-BERT)|End-to-end recipes for pre-training and fine-tuning BERT using Azure Machine Learning service.|
|[MASS](https://github.com/microsoft/MASS)|MASS: Masked Sequence to Sequence Pre-training for Language Generation.|

Просмотреть файл

@ -17,7 +17,7 @@
"**BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding** [\\[1\\]](#References)"
]
},
{
{
"cell_type": "markdown",
"metadata": {},
"source": [
@ -188,7 +188,7 @@
"source": [
"# Model configuration\n",
"DATA_FOLDER = './squad'\n",
"PROJECT_FOLDER = './pytorch-transformers'\n",
"PROJECT_FOLDER = './transformers'\n",
"EXPERIMENT_NAME = 'NLP-QA-BERT-deepdive'\n",
"BERT_MODEL = 'bert-large-uncased'\n",
"TARGET_GRADIENT_STEPS = 16\n",
@ -713,7 +713,7 @@
},
"outputs": [],
"source": [
"!git clone -b v0.4.0 https://github.com/huggingface/pytorch-transformers.git"
"!git clone -b v0.4.0 https://github.com/huggingface/transformers.git"
]
},
{
@ -735,20 +735,9 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'./pytorch-transformers\\\\bert_run_squad_azureml.py'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"shutil.copy(EVALUATE_SQAD_PATH, project_folder)\n",
"shutil.copy(BERT_UTIL_PATH, project_folder)\n",

Просмотреть файл

@ -71,7 +71,7 @@
"\n",
"Using a pre-trained XLNet model, we can fine-tune the model for text classification by training it on the MNLI dataset [\\[4\\]](#References). The Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. \n",
"\n",
"This notebook contains an end-to-end walkthrough of a pipeline to run PyTorch-Transformer's reimplementation [\\[5\\]](#References) of the XLNet model."
"This notebook contains an end-to-end walkthrough of a pipeline to run Transformer's reimplementation [\\[5\\]](#References) of the XLNet model."
]
},
{
@ -572,7 +572,7 @@
"**See figure below for the step-by-step tokenization process** \n",
"<img src=\"https://i.imgur.com/o6ewGgd.jpg\" width=\"1000\">\n",
"\n",
"*For more information on XLNet's input format, see pytorch-transformer [implementation](https://github.com/huggingface/pytorch-transformers/blob/master/examples/utils_glue.py)*"
"*For more information on XLNet's input format, see transformer [implementation](https://github.com/huggingface/pytorch-transformers/blob/master/examples/utils_glue.py)*"
]
},
{
@ -946,7 +946,7 @@
"2. Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina, [*BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding*](https://arxiv.org/abs/1810.04805), ACL, 2018.\n",
"3. Dai, Zihang, Zhilin Yang, Yiming Yang, William W. Cohen, Jaime Carbonell, Quoc V. Le, and Ruslan Salakhutdinov. [*Transformer-xl: Attentive language models beyond a fixed-length context.*](https://arxiv.org/pdf/1901.02860), 2019.\n",
"4. Adina Williams, Nikita Nangia, Samuel R. Bowman. [*A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference*](https://www.nyu.edu/projects/bowman/multinli/paper.pdf), 2016. Dataset available at (https://www.nyu.edu/projects/bowman/multinli/).\n",
"5. PyTorch-Transformers: a library of state-of-the-art pre-trained models for Natural Language Processing (NLP). Repository available at (https://github.com/huggingface/pytorch-transformers)."
"5. Transformers: a library of state-of-the-art pre-trained models for Natural Language Processing (NLP). Repository available at (https://github.com/huggingface/transformers)."
]
}
],

Просмотреть файл

@ -93,7 +93,7 @@ def test_bert_qa_runs(notebooks, subscription_id, resource_group, workspace_name
parameters=dict(
AZUREML_CONFIG_PATH=".",
DATA_FOLDER="./tests/integration/squad",
PROJECT_FOLDER="./tests/integration/pytorch-transformers",
PROJECT_FOLDER="./tests/integration/transformers",
EXPERIMENT_NAME="NLP-QA-BERT-deepdive",
BERT_UTIL_PATH="./utils_nlp/azureml/azureml_bert_util.py",
EVALUATE_SQAD_PATH="./utils_nlp/eval/evaluate_squad.py",

Просмотреть файл

@ -70,7 +70,6 @@ PIP_BASE = {
"nteract-scrapbook": "nteract-scrapbook>=0.2.1",
"pydocumentdb": "pydocumentdb>=2.3.3",
"pytorch-pretrained-bert": "pytorch-pretrained-bert>=0.6",
"pytorch-transformers": "pytorch-transformers>=1.2.0",
"tqdm": "tqdm==4.31.1",
"pyemd": "pyemd==0.5.1",
"ipywebrtc": "ipywebrtc==0.4.3",

Просмотреть файл

@ -1,6 +1,6 @@
# BERT-based Classes
This folder contains utility functions and classes based on the implementation of [PyTorch-Transformers](https://github.com/huggingface/pytorch-transformers).
This folder contains utility functions and classes based on the implementation of [Transformers](https://github.com/huggingface/transformers).
## Summary

Просмотреть файл

@ -3,7 +3,7 @@
# This script reuses some code from
# https://github.com/huggingface/pytorch-transformers/blob/master/examples
# https://github.com/huggingface/transformers/blob/master/examples
# /run_glue.py
import csv

Просмотреть файл

@ -3,7 +3,7 @@
# This script reuses some code from
# https://github.com/huggingface/pytorch-transformers/blob/master/examples
# https://github.com/huggingface/transformers/blob/master/examples
# /run_glue.py
from collections import namedtuple

Просмотреть файл

@ -1,6 +1,6 @@
# XLNet-based Classes
This folder contains utility functions and classes based on the implementation of [PyTorch-Transformers](https://github.com/huggingface/pytorch-transformers).
This folder contains utility functions and classes based on the implementation of [Transformers](https://github.com/huggingface/transformers).
## Summary

Просмотреть файл

@ -3,9 +3,9 @@
# This script reuses some code from
# https://github.com/huggingface/pytorch-transformers/blob/master/examples/utils_glue.py
# https://github.com/huggingface/transformers/blob/master/examples/utils_glue.py
from enum import Enum
from pytorch_transformers import XLNetTokenizer
from transformers import XLNetTokenizer
from mlflow import log_metric, log_param, log_artifact

Просмотреть файл

@ -6,7 +6,7 @@ import numpy as np
from collections import namedtuple
import torch
import torch.nn as nn
from pytorch_transformers import (
from transformers import (
XLNetConfig,
XLNetForSequenceClassification,
AdamW,