repo_id stringlengths 15 86 | file_path stringlengths 28 180 | content stringlengths 1 1.75M | __index_level_0__ int64 0 0 |
|---|---|---|---|
hf_public_repos/peft/src/peft | hf_public_repos/peft/src/peft/utils/hub_utils.py | # coding=utf-8
# Copyright 2023-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 ap... | 0 |
hf_public_repos | hf_public_repos/transformers/README.md | <!---
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 required by applicable law or ... | 0 |
hf_public_repos | hf_public_repos/transformers/ISSUES.md | <!---
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 required by applicable law or ... | 0 |
hf_public_repos | hf_public_repos/transformers/awesome-transformers.md | # Awesome projects built with Transformers
This page lists awesome projects built on top of Transformers. Transformers is more than a toolkit to use pretrained
models: it's a community of projects built around it and the Hugging Face Hub. We want Transformers to enable
developers, researchers, students, professors, en... | 0 |
hf_public_repos | hf_public_repos/transformers/LICENSE | Copyright 2018- The Hugging Face team. All rights reserved.
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" ... | 0 |
hf_public_repos | hf_public_repos/transformers/setup.py | # 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 applicabl... | 0 |
hf_public_repos | hf_public_repos/transformers/README_zh-hans.md | <!---
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 required by applicable law or ... | 0 |
hf_public_repos | hf_public_repos/transformers/pyproject.toml | [tool.black]
line-length = 119
target-version = ['py37']
[tool.ruff]
# Never enforce `E501` (line length violations).
ignore = ["C901", "E501", "E741"]
select = ["C", "E", "F", "I", "W"]
line-length = 119
# Ignore import violations in all `__init__.py` files.
[tool.ruff.per-file-ignores]
"__init__.py" = ["E402", "F40... | 0 |
hf_public_repos | hf_public_repos/transformers/conftest.py | # 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 required by applicabl... | 0 |
hf_public_repos | hf_public_repos/transformers/setup.cfg | [tool:pytest]
doctest_optionflags=NUMBER NORMALIZE_WHITESPACE ELLIPSIS
doctest_glob=**/*.md | 0 |
hf_public_repos | hf_public_repos/transformers/CITATION.cff | cff-version: "1.2.0"
date-released: 2020-10
message: "If you use this software, please cite it using these metadata."
title: "Transformers: State-of-the-Art Natural Language Processing"
url: "https://github.com/huggingface/transformers"
authors:
- family-names: Wolf
given-names: Thomas
- family-names: Debut
... | 0 |
hf_public_repos | hf_public_repos/transformers/README_zh-hant.md | <!---
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 required by applicable law or ... | 0 |
hf_public_repos | hf_public_repos/transformers/README_es.md | <!---
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 required by applicable law or ... | 0 |
hf_public_repos | hf_public_repos/transformers/CODE_OF_CONDUCT.md |
# Contributor Covenant Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level o... | 0 |
hf_public_repos | hf_public_repos/transformers/README_hd.md | <!---
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 required by applicable law or ... | 0 |
hf_public_repos | hf_public_repos/transformers/hubconf.py | # 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 required by applicabl... | 0 |
hf_public_repos | hf_public_repos/transformers/README_ja.md | <!---
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 required by applicable law or ... | 0 |
hf_public_repos | hf_public_repos/transformers/README_ko.md | <!---
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 required by applicable law or ... | 0 |
hf_public_repos | hf_public_repos/transformers/CONTRIBUTING.md | <!---
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 required by applicable law or ... | 0 |
hf_public_repos | hf_public_repos/transformers/.coveragerc | [run]
source=transformers
omit =
# skip convertion scripts from testing for now
*/convert_*
*/__main__.py
[report]
exclude_lines =
pragma: no cover
raise
except
register_parameter | 0 |
hf_public_repos | hf_public_repos/transformers/Makefile | .PHONY: deps_table_update modified_only_fixup extra_style_checks quality style fixup fix-copies test test-examples
# make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
export PYTHONPATH = src
check_dirs := examples tests src utils
modified_only_fixup:
$(eval modified_p... | 0 |
hf_public_repos/transformers | hf_public_repos/transformers/examples/README.md | <!---
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 required by applicable law or a... | 0 |
hf_public_repos/transformers | hf_public_repos/transformers/examples/run_on_remote.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2021 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 | hf_public_repos/transformers/examples/research_projects/README.md | <!---
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 required by applicable law or ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bertology/requirements.txt | transformers == 3.5.1
| 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bertology/run_bertology.py | #!/usr/bin/env python3
# Copyright 2018 CMU and 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 requir... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/bertology/run_prune_gpt.py | #!/usr/bin/env python3
""" This script is adapted from the Bertology pruning code (https://github.com/huggingface/transformers/blob/783d7d2629e97c5f0c5f9ef01b8c66410275c204/examples/research_projects/bertology/run_bertology.py)
to prune GPT-like models. The author is @altsoph.
"""
import argparse
import logging
import... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/longform-qa/requirements.txt | datasets >= 1.1.3
faiss-cpu
streamlit
elasticsearch
| 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/longform-qa/README.md | # Long Form Question Answering
Author: @yjernite
This folder contains the code for the Long Form Question answering [demo](http://35.226.96.115:8080/) as well as methods to train and use a fully end-to-end Long Form Question Answering system using the [🤗transformers](https://github.com/huggingface/transformers) and ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/longform-qa/eli5_utils.py | import functools
import math
import os # noqa: F401
from random import choice, randint
from time import time
import datasets # noqa: F401
import faiss # noqa: F401
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint as checkpoint
from elasticsearch import Elasticsearch # noqa: F401
fr... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/longform-qa/eli5_app.py | import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from eli5_utils import (
embed_questions_for_retrieval,
make_qa_s2s_model,
qa_s2s_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers impor... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/movement-pruning/requirements.txt | torch>=1.4.0
-e git+https://github.com/huggingface/transformers.git@352d5472b0c1dec0f420d606d16747d851b4bda8#egg=transformers
knockknock>=0.1.8.1
h5py>=2.10.0
numpy>=1.18.2
scipy>=1.4.1
| 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/movement-pruning/masked_run_glue.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/movement-pruning/README.md | # Movement Pruning: Adaptive Sparsity by Fine-Tuning
Author: @VictorSanh
*Magnitude pruning is a widely used strategy for reducing model size in pure supervised learning; however, it is less effective in the transfer learning regime that has become standard for state-of-the-art natural language processing application... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/movement-pruning/Saving_PruneBERT.ipynb | # Includes
import h5py
import os
import json
from collections import OrderedDict
from scipy import sparse
import numpy as np
import torch
from torch import nn
from transformers import *
os.chdir("../../")# Load fine-pruned model and quantize the model
model = BertForQuestionAnswering.from_pretrained("huggingface/... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/movement-pruning/bertarize.py | # Copyright 2020-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 applicable law o... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/movement-pruning/counts_parameters.py | # Copyright 2020-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 applicable law o... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/movement-pruning/masked_run_squad.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/movement-pruning | hf_public_repos/transformers/examples/research_projects/movement-pruning/emmental/__init__.py | from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 0 |
hf_public_repos/transformers/examples/research_projects/movement-pruning | hf_public_repos/transformers/examples/research_projects/movement-pruning/emmental/configuration_bert_masked.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/movement-pruning | hf_public_repos/transformers/examples/research_projects/movement-pruning/emmental/modeling_bert_masked.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/movement-pruning/emmental | hf_public_repos/transformers/examples/research_projects/movement-pruning/emmental/modules/__init__.py | from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
from .masked_nn import MaskedLinear
| 0 |
hf_public_repos/transformers/examples/research_projects/movement-pruning/emmental | hf_public_repos/transformers/examples/research_projects/movement-pruning/emmental/modules/binarizer.py | # coding=utf-8
# Copyright 2020-present, AllenAI Authors, University of Illinois Urbana-Champaign,
# Intel Nervana Systems and 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 ... | 0 |
hf_public_repos/transformers/examples/research_projects/movement-pruning/emmental | hf_public_repos/transformers/examples/research_projects/movement-pruning/emmental/modules/masked_nn.py | # coding=utf-8
# Copyright 2020-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/seq2seq-distillation/requirements.txt | tensorboard
scikit-learn
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
| 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/run_eval.py | #!/usr/bin/env python
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from utils import calculate_bleu, calcula... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/distil_marian_enro_teacher.sh | #!/usr/bin/env bash
export PYTHONPATH="../":"${PYTHONPATH}"
export WANDB_PROJECT=dmar
# export MAX_LEN=128
python distillation.py \
--learning_rate=3e-4 \
--do_train \
--fp16 \
--val_check_interval 0.25 \
--teacher Helsinki-NLP/opus-mt-en-ro \
--max_source_length $MAX_LEN --max_target_length $MAX_LEN --val_... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/finetune_bart_tiny.sh | # Script for verifying that run_bart_sum can be invoked from its directory
# Get tiny dataset with cnn_dm format (4 examples for train, val, test)
wget https://cdn-datasets.huggingface.co/summarization/cnn_tiny.tgz
tar -xzvf cnn_tiny.tgz
rm cnn_tiny.tgz
export OUTPUT_DIR_NAME=bart_utest_output
export CURRENT_DIR=${PW... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/README.md | ## Sequence to Sequence Training and Evaluation
This directory contains examples for finetuning and evaluating transformers on summarization and translation tasks.
Author: Sam Shleifer (https://github.com/sshleifer)
### Supported Architectures
- `BartForConditionalGeneration` (and anything that inherits from it)
- ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/distillation.py | #!/usr/bin/env python
import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List # noqa: F401
import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_co... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/finetune_pegasus_xsum.sh | #!/usr/bin/env bash
export PYTHONPATH="../":"${PYTHONPATH}"
# From appendix C of paper https://arxiv.org/abs/1912.08777
# Set --gradient_accumulation_steps so that effective batch size is 256 (2*128, 4*64, 8*32, 16*16)
python finetune.py \
--learning_rate=1e-4 \
--do_train \
--do_predict \
--n_val 100... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/distil_marian_no_teacher.sh | #!/usr/bin/env bash
export PYTHONPATH="../":"${PYTHONPATH}"
export WANDB_PROJECT=dmar
export MAX_LEN=128
python finetune.py \
--learning_rate=3e-4 \
--do_train \
--do_predict \
--fp16 \
--val_check_interval 0.25 \
--data_dir $ENRO_DIR \
--max_source_length $MAX_LEN --max_target_length $MAX_LEN --val_max_t... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/precomputed_pseudo_labels.md | ### Saved Pseudo-Labels
These are the generations of various large models on various large **training** sets. All in all they took about 200 GPU hours to produce.
### Available Pseudo-labels
| Dataset | Model | Link ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/make_student.py | import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
logger = logging.get_logger(__name__)
def copy_layers(src_layers: nn.ModuleList, des... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/train_mbart_cc25_enro.sh | #!/usr/bin/env bash
export PYTHONPATH="../":"${PYTHONPATH}"
python finetune.py \
--learning_rate=3e-5 \
--fp16 \
--do_train \
--val_check_interval=0.25 \
--adam_eps 1e-06 \
--num_train_epochs 6 --src_lang en_XX --tgt_lang ro_RO \
--data_dir $ENRO_DIR \
--max_source_length $MAX_LEN --max... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/_test_bash_script.py | #!/usr/bin/env python
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from tran... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/train_distilbart_cnn.sh | #!/usr/bin/env bash
export PYTHONPATH="../":"${PYTHONPATH}"
export BS=32
export GAS=1
python finetune.py \
--learning_rate=3e-5 \
--fp16 \
--gpus 1 \
--do_train \
--do_predict \
--val_check_interval 0.25 \
--n_val 500 \
--num_train_epochs 2 \
--freeze_encoder --freeze_embeds --data... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/finetune_t5.sh | # Add parent directory to python path to access lightning_base.py
export PYTHONPATH="../":"${PYTHONPATH}"
python finetune.py \
--data_dir=$CNN_DIR \
--learning_rate=3e-5 \
--train_batch_size=$BS \
--eval_batch_size=$BS \
--output_dir=$OUTPUT_DIR \
--max_source_length=512 \
--max_target_length=56 \
--val_check_interval... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/_test_make_student.py | import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
TINY_BART = "sshleifer/bart-tiny-random"
TINY_T5 = "patrickvonplaten/t5-t... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/callbacks.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 import save_json
def count_trainable_parameters(model):
model_parameters... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/finetune.sh | # the proper usage is documented in the README, you need to specify data_dir, output_dir and model_name_or_path
# run ./finetune.sh --help to see all the possible options
python finetune.py \
--learning_rate=3e-5 \
--fp16 \
--gpus 1 \
--do_train \
--do_predict \
--n_val 1000 \
--val_check_in... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/sentence_splitter.py | import re
from filelock import FileLock
try:
import nltk
NLTK_AVAILABLE = True
except (ImportError, ModuleNotFoundError):
NLTK_AVAILABLE = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def add_newline_to_end_of_each_sentence(x: str) -> str:
... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/_test_seq2seq_examples.py | import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path
import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/convert_pl_checkpoint_to_hf.py | #!/usr/bin/env python
import os
from pathlib import Path
from typing import Dict, List
import fire
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers.utils.logging import get_logger
logger = get_logger(__name__)
def remove_prefix(text: str, prefix: str):
if text.star... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/utils.py | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import roug... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/train_distilbart_xsum.sh | #!/usr/bin/env bash
export PYTHONPATH="../":"${PYTHONPATH}"
python distillation.py \
--teacher facebook/bart-large-xsum --data_dir xsum \
--tokenizer_name facebook/bart-large-xsum \
--student_decoder_layers 6 --student_encoder_layers 12 \
--freeze_encoder --freeze_embeds \
--learning_rate=3e-4 \
--do_train ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/_test_seq2seq_examples_multi_gpu.py | # as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.
import os
import sys
from pathlib import Path
import torch
from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json
C... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/dynamic_bs_example.sh | #!/usr/bin/env bash
export PYTHONPATH="../":"${PYTHONPATH}"
export WANDB_PROJECT=dmar
export MAX_LEN=128
export m=sshleifer/student_marian_en_ro_6_1
python finetune.py \
--learning_rate=3e-4 \
--do_train \
--fp16 \
--data_dir wmt_en_ro \
--max_source_length $MAX_LEN --max_target_length $MAX_LEN --val_max_targ... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/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 | hf_public_repos/transformers/examples/research_projects/seq2seq-distillation/finetune.py | #!/usr/bin/env python
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import Seq2SeqLoggingCallback, get_checkpoin... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/deebert/requirements.txt | transformers == 3.5.1
| 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/deebert/README.md | # DeeBERT: Early Exiting for *BERT
This is the code base for the paper [DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference](https://www.aclweb.org/anthology/2020.acl-main.204/), modified from its [original code base](https://github.com/castorini/deebert).
The original code base also has information for do... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/deebert/test_glue_deebert.py | import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
def get_setup_file():
parser = argparse... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/deebert/train_deebert.sh | #!/bin/bash
export CUDA_VISIBLE_DEVICES=0
PATH_TO_DATA=/h/xinji/projects/GLUE
MODEL_TYPE=bert # bert or roberta
MODEL_SIZE=base # base or large
DATASET=MRPC # SST-2, MRPC, RTE, QNLI, QQP, or MNLI
MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE}
EPOCHS=10
if [ $MODEL_TYPE = 'bert' ]
then
EPOCHS=3
MODEL_NAME=${MODEL_NAME... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/deebert/run_glue_deebert.py | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
import time
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distribute... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/deebert/eval_deebert.sh | #!/bin/bash
export CUDA_VISIBLE_DEVICES=0
PATH_TO_DATA=/h/xinji/projects/GLUE
MODEL_TYPE=bert # bert or roberta
MODEL_SIZE=base # base or large
DATASET=MRPC # SST-2, MRPC, RTE, QNLI, QQP, or MNLI
MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE}
if [ $MODEL_TYPE = 'bert' ]
then
MODEL_NAME=${MODEL_NAME}-uncased
fi
python... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/deebert/entropy_eval.sh | #!/bin/bash
export CUDA_VISIBLE_DEVICES=0
PATH_TO_DATA=/h/xinji/projects/GLUE
MODEL_TYPE=bert # bert or roberta
MODEL_SIZE=base # base or large
DATASET=MRPC # SST-2, MRPC, RTE, QNLI, QQP, or MNLI
MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE}
if [ $MODEL_TYPE = 'bert' ]
then
MODEL_NAME=${MODEL_NAME}-uncased
fi
ENTROPI... | 0 |
hf_public_repos/transformers/examples/research_projects/deebert | hf_public_repos/transformers/examples/research_projects/deebert/src/modeling_highway_bert.py | import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
... | 0 |
hf_public_repos/transformers/examples/research_projects/deebert | hf_public_repos/transformers/examples/research_projects/deebert/src/modeling_highway_roberta.py | from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/codeparrot/requirements.txt | transformers==4.19.0
datasets==1.16.0
wandb==0.12.0
tensorboard==2.6.0
torch==1.11.0
huggingface-hub==0.1.0
git+https://github.com/huggingface/accelerate.git@3c45b6f760ad8745be9ebc9bbb26f5b04dea4abe
datasketch==1.5.7
dpu_utils | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/codeparrot/README.md | # CodeParrot 🦜
<p align="center">
<img src="https://huggingface.co/datasets/lvwerra/repo-images/raw/main/code-highlighting-streamlit.png" alt="drawing" width="350"/>
</p>
## What is this about?
This is an open-source effort to train and evaluate code generation models. CodeParrot 🦜 is a GPT-2 model trained from ... | 0 |
hf_public_repos/transformers/examples/research_projects/codeparrot | hf_public_repos/transformers/examples/research_projects/codeparrot/examples/requirements.txt | datasets==2.3.2
transformers==4.21.1
wandb==0.13.1
evaluate==0.2.2
scikit-learn==1.1.2 | 0 |
hf_public_repos/transformers/examples/research_projects/codeparrot | hf_public_repos/transformers/examples/research_projects/codeparrot/examples/README.md | # Examples
In this folder we showcase some examples to use code models for downstream tasks.
## Complexity prediction
In this task we want to predict the complexity of Java programs in [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex) dataset. Using Hugging Face `trainer`, we finetuned [multilingua... | 0 |
hf_public_repos/transformers/examples/research_projects/codeparrot | hf_public_repos/transformers/examples/research_projects/codeparrot/examples/train_complexity_predictor.py | import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArguments,
... | 0 |
hf_public_repos/transformers/examples/research_projects/codeparrot | hf_public_repos/transformers/examples/research_projects/codeparrot/scripts/preprocessing.py | import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoToke... | 0 |
hf_public_repos/transformers/examples/research_projects/codeparrot | hf_public_repos/transformers/examples/research_projects/codeparrot/scripts/codeparrot_training.py | import logging
import os
import time
from argparse import Namespace
from pathlib import Path
import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub ... | 0 |
hf_public_repos/transformers/examples/research_projects/codeparrot | hf_public_repos/transformers/examples/research_projects/codeparrot/scripts/bpe_training.py | from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm
from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
# Iterator for Training
def batch_iterator(batch_size=10):
for _ in tqdm(range(... | 0 |
hf_public_repos/transformers/examples/research_projects/codeparrot | hf_public_repos/transformers/examples/research_projects/codeparrot/scripts/arguments.py | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class TrainingArguments:
"""
Configuration for training model.
"""
model_ckpt: Optional[str] = field(
default="codeparrot/codeparrot", metadata={"help": "Model name or path of model to be trained."}
)
save... | 0 |
hf_public_repos/transformers/examples/research_projects/codeparrot | hf_public_repos/transformers/examples/research_projects/codeparrot/scripts/validation_loss.py | import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set... | 0 |
hf_public_repos/transformers/examples/research_projects/codeparrot | hf_public_repos/transformers/examples/research_projects/codeparrot/scripts/pretokenizing.py | import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def tokenize(example):
output = {}
output["input_ids"] = tokenizer(example["content"], truncation=False)["input_ids"]
output["ratio... | 0 |
hf_public_repos/transformers/examples/research_projects/codeparrot | hf_public_repos/transformers/examples/research_projects/codeparrot/scripts/human_eval.py | import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torc... | 0 |
hf_public_repos/transformers/examples/research_projects/codeparrot | hf_public_repos/transformers/examples/research_projects/codeparrot/scripts/initialize_model.py | from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
parser = HfArgumentParser(InitializationArguments)
args = parser.parse_args()
# Load codeparrot tokenizer trained for Python code tokenization
tokenizer = AutoToke... | 0 |
hf_public_repos/transformers/examples/research_projects/codeparrot | hf_public_repos/transformers/examples/research_projects/codeparrot/scripts/minhash_deduplication.py | import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import ... | 0 |
hf_public_repos/transformers/examples/research_projects/codeparrot/scripts | hf_public_repos/transformers/examples/research_projects/codeparrot/scripts/tests/test_deduplicate.py | from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def get_dataset():
data_dict = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"path": ["test_1.py", "test_2.py", "unit_test.py"],
"content"... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/robust-speech-event/README.md | # Robust Speech Challenge 🤗
Welcome to the robust speech recognition challenge 🎙️ !
The goal of this event is to build **robust**, **real-world** speech recognition (ASR) systems in as many languages as possible 🌏🌍🌎.
If necessary and available, free access to a V100S 32 GB GPU will kindly be provided by the [OVH... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/robust-speech-event/eval.py | #!/usr/bin/env python3
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def log_results(result: Dataset, args: Dict[str, str]):
"""DO NOT CHANGE. This function computes and logs t... | 0 |
hf_public_repos/transformers/examples/research_projects | hf_public_repos/transformers/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2021 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/robust-speech-event/run_speech_recognition_ctc_streaming.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/quantization-qdqbert/evaluate-hf-trt-qa.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/README.md | <!---
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 by applicable law or agr... | 0 |
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