repo_id stringlengths 15 86 | file_path stringlengths 28 180 | content stringlengths 1 1.75M | __index_level_0__ int64 0 0 |
|---|---|---|---|
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/quantization-qdqbert/quant_trainer.py | # coding=utf-8
# Copyright 2021 NVIDIA Corporation. All rights reserved.
#
# 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... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/quantization-qdqbert/Dockerfile | # coding=utf-8
# Copyright 2021 NVIDIA Corporation. All rights reserved.
#
# 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... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/quantization-qdqbert/utils_qa.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team All rights reserved.
#
# 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 require... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/quantization-qdqbert/trainer_quant_qa.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team All rights reserved.
# Copyright 2021 NVIDIA Corporation. All rights reserved.
#
# 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
#
# htt... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/quantization-qdqbert/run_quant_qa.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Team All rights reserved.
# Copyright 2021 NVIDIA Corporation. All rights reserved.
#
# 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 ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/quantization-qdqbert/ort-infer-benchmark.py | import os
import time
import numpy as np
import onnxruntime as ort
os.environ["ORT_TENSORRT_INT8_ENABLE"] = "1"
os.environ["ORT_TENSORRT_INT8_USE_NATIVE_CALIBRATION_TABLE"] = "0"
os.environ["ORT_TENSORRT_ENGINE_CACHE_ENABLE"] = "1"
sess_opt = ort.SessionOptions()
sess_opt.graph_optimization_level = ort.GraphOptimiz... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bertabs/requirements.txt | transformers == 3.5.1
# For ROUGE
nltk
py-rouge
| 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bertabs/README.md | # Text Summarization with Pretrained Encoders
This folder contains part of the code necessary to reproduce the results on abstractive summarization from the article [Text Summarization with Pretrained Encoders](https://arxiv.org/pdf/1908.08345.pdf) by [Yang Liu](https://nlp-yang.github.io/) and [Mirella Lapata](https:... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bertabs/test_utils_summarization.py | # coding=utf-8
# Copyright 2019 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 ag... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bertabs/run_summarization.py | #! /usr/bin/python3
import argparse
import logging
import os
import sys
from collections import namedtuple
import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm
from transformers import BertTokenizer
from .utils_summarizati... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bertabs/convert_bertabs_original_pytorch_checkpoint.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bertabs/utils_summarization.py | import os
from collections import deque
import torch
from torch.utils.data import Dataset
# ------------
# Data loading
# ------------
class CNNDMDataset(Dataset):
"""Abstracts the dataset used to train seq2seq models.
The class will process the documents that are located in the specified
folder. The ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bertabs/configuration_bertabs.py | # coding=utf-8
# Copyright 2019 The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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.a... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bertabs/modeling_bertabs.py | # MIT License
# Copyright (c) 2019 Yang Liu and the HuggingFace team
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, c... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/pplm/requirements.txt | tensorboard
scikit-learn
seqeval
psutil
sacrebleu
rouge-score
tensorflow_datasets
pytorch-lightning
matplotlib
git-python==1.0.3
faiss-cpu
streamlit
elasticsearch
nltk
pandas
datasets >= 1.1.3
fire
pytest
conllu
sentencepiece != 0.1.92
protobuf
transformers==3.5.1
| 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/pplm/README.md | # Plug and Play Language Models: a Simple Approach to Controlled Text Generation
Authors: [Sumanth Dathathri](https://dathath.github.io/), [Andrea Madotto](https://andreamad8.github.io/), Janice Lan, Jane Hung, Eric Frank, [Piero Molino](https://w4nderlu.st/), [Jason Yosinski](http://yosinski.com/), and [Rosanne Liu](... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/pplm/run_pplm_discrim_train.py | #! /usr/bin/env python3
# coding=utf-8
# Copyright (c) 2019 Uber Technologies, 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 ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/pplm/run_pplm.py | #! /usr/bin/env python3
# coding=utf-8
# Copyright (c) 2019 Uber Technologies, 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 ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/pplm/pplm_classification_head.py | from torch import nn
class ClassificationHead(nn.Module):
"""Classification Head for transformer encoders"""
def __init__(self, class_size, embed_size):
super().__init__()
self.class_size = class_size
self.embed_size = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/visual_bert/extracting_data.py | import getopt
import json
import os
# import numpy as np
import sys
from collections import OrderedDict
import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess
from utils import Config
"""
USAGE:
``python extracting_data.py -i <img_dir> -o ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/visual_bert/requirements.txt | appdirs==1.4.3
argon2-cffi==20.1.0
async-generator==1.10
attrs==20.2.0
backcall==0.2.0
CacheControl==0.12.6
certifi==2023.7.22
cffi==1.14.2
chardet==3.0.4
click==7.1.2
colorama==0.4.3
contextlib2==0.6.0
cycler==0.10.0
datasets==1.0.0
decorator==4.4.2
defusedxml==0.6.0
dill==0.3.2
distlib==0.3.0
distro==1.4.0
entrypoint... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/visual_bert/README.md | # VisualBERT Demo
This demo shows usage of VisualBERT VQA model and is adapted from LXMERT demo present [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/lxmert/demo.ipynb).
1. make a virtualenv: ``virtualenv venv`` and activate ``source venv/bin/activate``
2. install reqs: ``pip ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/visual_bert/processing_image.py | """
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal
Adapted From Facebook Inc, Detectron2
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/license... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/visual_bert/demo.ipynb | # %pip install-r requirements.txtfrom IPython.display import Image, display
import PIL.Image
import io
import torch
import numpy as np
from processing_image import Preprocess
from visualizing_image import SingleImageViz
from modeling_frcnn import GeneralizedRCNN
from utils import Config
import utils
from transformers i... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/visual_bert/modeling_frcnn.py | """
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal
Adapted From Facebook Inc, Detectron2 && Huggingface Co.
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... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/visual_bert/visualizing_image.py | """
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal
Adapted From Facebook Inc, Detectron2
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/license... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/visual_bert/utils.py | """
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal, Huggingface team :)
Adapted From Facebook Inc, Detectron2
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://w... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/self-training-text-classification/selftraining.py | # coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# 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 applicab... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/self-training-text-classification/requirements.txt | accelerate
datasets >= 1.8.0
protobuf
scikit-learn
scipy
sentencepiece != 0.1.92
torch >= 1.3
| 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/self-training-text-classification/README.md | # Self-training
This is an implementation of the self-training algorithm (without task augmentation) in the [EMNLP 2021](https://2021.emnlp.org/) paper: [STraTA: Self-Training with Task Augmentation for Better Few-shot Learning](https://arxiv.org/abs/2109.06270). Please check out https://github.com/google-research/goo... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/self-training-text-classification/run.sh | # Copyright 2022 The Google Research Authors.
#
# 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 agree... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/self-training-text-classification/finetuning.py | # coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# 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 applicab... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bert-loses-patience/requirements.txt | transformers == 3.5.1 | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bert-loses-patience/README.md | # Patience-based Early Exit
Patience-based Early Exit (PABEE) is a plug-and-play inference method for pretrained language models.
We have already implemented it on BERT and ALBERT. Basically, you can make your LM faster and more robust with PABEE. It can even improve the performance of ALBERT on GLUE. The only sacrifi... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bert-loses-patience/run_glue_with_pabee.py | # coding=utf-8
# Copyright 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and Microsoft Corporation.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bert-loses-patience/test_run_glue_with_pabee.py | import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_with_pabee
from transformers.testing_utils import TestCasePlus
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
def get_setup_file():
parser = argparse.ArgumentParser()
parser.add_argument("-f")... | 0 |
hf_public_repos/transformers/examples/research_projects/bert-loses-patience | hf_public_repos/transformers/examples/research_projects/bert-loses-patience/pabee/modeling_pabee_bert.py | # coding=utf-8
# Copyright 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and Microsoft Corporation.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.... | 0 |
hf_public_repos/transformers/examples/research_projects/bert-loses-patience | hf_public_repos/transformers/examples/research_projects/bert-loses-patience/pabee/modeling_pabee_albert.py | # coding=utf-8
# Copyright 2020 Google AI, Google Brain, the HuggingFace Inc. team and Microsoft Corporation.
#
# 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/lic... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/adversarial/requirements.txt | transformers == 3.5.1
| 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/adversarial/README.md | ## Adversarial evaluation of model performances
Here is an example on evaluating a model using adversarial evaluation of natural language inference with the Heuristic Analysis for NLI Systems (HANS) dataset [McCoy et al., 2019](https://arxiv.org/abs/1902.01007). The example was gracefully provided by [Nafise Sadat Moo... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/adversarial/run_hans.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/adversarial/utils_hans.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/luke/README.md | # Token classification
## PyTorch version, no Trainer
Fine-tuning (m)LUKE for token classification task such as Named Entity Recognition (NER), Parts-of-speech
tagging (POS) or phrase extraction (CHUNKS). You can easily
customize it to your needs if you need extra processing on your datasets.
It will either run on a... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/luke/run_luke_ner_no_trainer.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# 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/LI... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/luke/luke_utils.py | import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def padding_tensor(seq... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/distillation/requirements.txt | transformers
gitpython==3.1.30
tensorboard>=1.14.0
tensorboardX==1.8
psutil==5.6.6
scipy>=1.4.1
| 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/distillation/README.md | # Distil*
Author: @VictorSanh
This folder contains the original code used to train Distil* as well as examples showcasing how to use DistilBERT, DistilRoBERTa and DistilGPT2.
**January 20, 2020 - Bug fixing** We have recently discovered and fixed [a bug](https://github.com/huggingface/transformers/commit/48cbf267c98... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/distillation/run_squad_w_distillation.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/distillation/distiller.py | # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team and Facebook, 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
#
# Un... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/distillation/grouped_batch_sampler.py | # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team and Facebook, 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
#
# Un... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/distillation/lm_seqs_dataset.py | # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team and Facebook, 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
#
# Un... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/distillation/train.py | # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# 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 a... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/distillation/utils.py | # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team and Facebook, 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
#
# Un... | 0 |
hf_public_repos/transformers/examples/research_projects/distillation | hf_public_repos/transformers/examples/research_projects/distillation/training_configs/distilroberta-base.json | {
"vocab_size": 50265,
"hidden_size": 768,
"num_hidden_layers": 6,
"num_attention_heads": 12,
"intermediate_size": 3072,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"attention_probs_dropout_prob": 0.1,
"max_position_embeddings": 514,
"type_vocab_size": 1,
"initializer_r... | 0 |
hf_public_repos/transformers/examples/research_projects/distillation | hf_public_repos/transformers/examples/research_projects/distillation/training_configs/distilbert-base-cased.json | {
"activation": "gelu",
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"n_heads": 12,
"n_layers": 6,
"sinusoidal_pos_embds": true,
"tie_weights_": true,
"vocab_size": 28996
}
| 0 |
hf_public_repos/transformers/examples/research_projects/distillation | hf_public_repos/transformers/examples/research_projects/distillation/training_configs/distilbert-base-multilingual-cased.json | {
"activation": "gelu",
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"n_heads": 12,
"n_layers": 6,
"sinusoidal_pos_embds": true,
"tie_weights_": true,
"vocab_size": 119547
}
| 0 |
hf_public_repos/transformers/examples/research_projects/distillation | hf_public_repos/transformers/examples/research_projects/distillation/training_configs/distilgpt2.json | {
"initializer_range": 0.02,
"layer_norm_epsilon": 0.00001,
"n_embd": 768,
"n_head": 12,
"n_layer": 6,
"n_positions": 1024,
"vocab_size": 50257
} | 0 |
hf_public_repos/transformers/examples/research_projects/distillation | hf_public_repos/transformers/examples/research_projects/distillation/training_configs/distilbert-base-uncased.json | {
"activation": "gelu",
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"n_heads": 12,
"n_layers": 6,
"sinusoidal_pos_embds": true,
"tie_weights_": true,
"vocab_size": 30522
}
| 0 |
hf_public_repos/transformers/examples/research_projects/distillation | hf_public_repos/transformers/examples/research_projects/distillation/scripts/binarized_data.py | # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# 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 a... | 0 |
hf_public_repos/transformers/examples/research_projects/distillation | hf_public_repos/transformers/examples/research_projects/distillation/scripts/token_counts.py | # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# 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 a... | 0 |
hf_public_repos/transformers/examples/research_projects/distillation | hf_public_repos/transformers/examples/research_projects/distillation/scripts/extract_distilbert.py | # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# 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 a... | 0 |
hf_public_repos/transformers/examples/research_projects/distillation | hf_public_repos/transformers/examples/research_projects/distillation/scripts/extract.py | # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# 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 a... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/requirements.txt | faiss-cpu >= 1.6.3
datasets >= 1.0.1
psutil >= 5.7.0
torch >= 1.4.0
ray >= 1.10.0
pytorch-lightning >= 1.5.10, <=1.6.0
transformers
GitPython | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/distributed_pytorch_retriever.py | import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
logger = logging.getLogger(__name__)
class RagPyTorchDistributedRetriever(RagRetriever):
"""
A distributed retriever built on top of ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/README.md | # Intro
Authors: @patrickvonplaten and @lhoestq
Aimed at tackling the knowledge-intensive NLP tasks (think tasks a human wouldn't be expected to solve without access to external knowledge sources), RAG models are seq2seq models with access to a retrieval mechanism providing relevant context documents at training and ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/__init__.py | import os
import sys
sys.path.insert(1, os.path.dirname(os.path.realpath(__file__)))
| 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/distributed_ray_retriever.py | import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
logger = logging.getLogger(__name__)
class RayRetriever:
def __init__(self):
self.initialized = False
def create_rag_retriever(sel... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/finetune_rag.sh | # Add parent directory to python path to access lightning_base.py
export PYTHONPATH="../":"${PYTHONPATH}"
# A sample finetuning run, you need to specify data_dir, output_dir and model_name_or_path
# run ./examples/rag/finetune_rag.sh --help to see all the possible options
python examples/rag/finetune_rag.py \
--d... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/eval_rag.py | """ Evaluation script for RAG models."""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as trans... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/consolidate_rag_checkpoint.py | """
A script creating a RAG checkpoint from a generator and a question encoder checkpoints.
"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def consolidate(
model_type,
generator_name_or_path: str,
... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/callbacks_rag.py | import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def count_trainable_parameters(model):
model_paramet... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/finetune_rag_ray.sh | # Sample script to finetune RAG using Ray for distributed retrieval.
# Add parent directory to python path to access lightning_base.py
export PYTHONPATH="../":"${PYTHONPATH}"
# Start a single-node Ray cluster.
ray start --head
# A sample finetuning run, you need to specify data_dir, output_dir and model_name_or_path... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/use_own_knowledge_dataset.py | import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import (
DPRCo... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/test_distributed_retriever.py | import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch
import faiss
import numpy as np
from datasets import Dataset
from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transform... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/utils_rag.py | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transfo... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/parse_dpr_relevance_data.py | """
This script reads DPR retriever training data and parses each datapoint. We save a line per datapoint.
Each line consists of the query followed by a tab-separated list of Wikipedia page titles constituting
positive contexts for a given query.
"""
import argparse
import json
from tqdm import tqdm
def main():
... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/_test_finetune_rag.py | import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.b... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/finetune_rag.py | """Finetuning script for RAG models. Adapted from examples.seq2seq.finetune.py"""
import argparse
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Any, Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
import... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag/lightning_base.py | import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
... | 0 |
hf_public_repos/transformers/examples/research_projects/rag | hf_public_repos/transformers/examples/research_projects/rag/test_data/my_knowledge_dataset.csv | Aaron Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/vqgan-clip/requirements.txt | einops
gradio
icecream
imageio
lpips
matplotlib
more_itertools
numpy
omegaconf
opencv_python_headless
Pillow
pudb
pytorch_lightning
PyYAML
requests
scikit_image
scipy
setuptools
streamlit
taming-transformers
torch
torchvision
tqdm
transformers==4.26.0
tokenizers==0.13.2
typing_extensions
wandb
| 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/vqgan-clip/README.md | # Simple VQGAN CLIP
Author: @ErwannMillon
This is a very simple VQGAN-CLIP implementation that was built as a part of the <a href= "https://github.com/ErwannMillon/face-editor"> Face Editor project </a> . This simplified version allows you to generate or edit images using text with just three lines of code. For a mo... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/vqgan-clip/VQGAN_CLIP.py | import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from uti... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/vqgan-clip/loaders.py | import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def load_config(config_path, display=False):
config = OmegaConf.load(config_path)
if display:
print(yaml.dump(OmegaConf.to_container(config)))
return config
def load_vqgan(device, c... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/vqgan-clip/utils.py | from datetime import datetime
import matplotlib.pyplot as plt
import torch
def freeze_module(module):
for param in module.parameters():
param.requires_grad = False
def get_device():
device = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available() and torch.backends.m... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/vqgan-clip/img_processing.py | import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def preprocess(img, target_image_size=256):
s = min(img.size)
if s < target_image_size:
raise ValueError(f"min dim for image {s} < {target_image_size}")
... | 0 |
hf_public_repos/transformers/examples/research_projects/onnx | hf_public_repos/transformers/examples/research_projects/onnx/summarization/requirements.txt | torch >= 1.10 | 0 |
hf_public_repos/transformers/examples/research_projects/onnx | hf_public_repos/transformers/examples/research_projects/onnx/summarization/README.md | <!---
Copyright 2021 The HuggingFace Team. All rights reserved.
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 a... | 0 |
hf_public_repos/transformers/examples/research_projects/onnx | hf_public_repos/transformers/examples/research_projects/onnx/summarization/run_onnx_exporter.py | #!/usr/bin/env python
# coding=utf-8
# Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
#
# 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.ap... | 0 |
hf_public_repos/transformers/examples/research_projects/onnx/summarization | hf_public_repos/transformers/examples/research_projects/onnx/summarization/bart_onnx/reduce_onnx_size.py | """
Code to remove duplicate initializers to reduce ONNX model size.
"""
import os
import numpy
import onnx
def _is_equal_tensor_proto(a, b):
name_a = a.name
name_b = b.name
a.name = ""
b.name = ""
res = a == b
a.name = name_a
b.name = name_b
return res
def _node_replace_input_... | 0 |
hf_public_repos/transformers/examples/research_projects/onnx/summarization | hf_public_repos/transformers/examples/research_projects/onnx/summarization/bart_onnx/generation_onnx.py | import copy
import itertools
from typing import List, Optional, Tuple
import torch
import torch.nn.functional as F
from transformers import BartConfig
from transformers.generation import GenerationMixin
def _convert_past_list_to_tuple(past_key_values):
"""
In Bart model, the type of past_key_values is tuple... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/tapex/requirements.txt | numpy
datasets
pandas
nltk | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/tapex/README.md | <!---
Copyright 2022 The Microsoft Inc. and The HuggingFace Inc. Team. All rights reserved.
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 re... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/tapex/run_wikisql_with_tapex.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2022 The Microsoft and The HuggingFace Inc. team. All rights reserved.
#
# 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.apac... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/tapex/wikisql_utils.py | # coding=utf-8
# Copyright 2022 The Microsoft, The Google and The HuggingFace Inc. team. All rights reserved.
#
# 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/lic... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/tapex/run_wikitablequestions_with_tapex.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2022 The Microsoft and The HuggingFace Inc. team. All rights reserved.
#
# 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.apac... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/tapex/run_tabfact_with_tapex.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2022 The Microsoft and The HuggingFace Inc. team. All rights reserved.
#
# 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.apac... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag-end2end-retriever/requirements.txt | faiss-cpu >= 1.7.2
datasets
psutil >= 5.9.1
torch >= 1.11.0
pytorch-lightning == 1.6.4
nvidia-ml-py3 == 7.352.0
ray >= 1.13.0 | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag-end2end-retriever/README.md | # End-to-End finetuning of RAG (including DPR retriever) for Question Answering.
This finetuning script is actively maintained by [Shamane Siri](https://github.com/shamanez). Feel free to ask questions on the [Forum](https://discuss.huggingface.co/) or post an issue on [GitHub](https://github.com/huggingface/transform... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/rag-end2end-retriever/distributed_ray_retriever.py | import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
logger = logging.getLogger(__name__)
class RayRetriever:
def __init__(self):
self.initialized = False
def create_rag_retriever(sel... | 0 |
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