text stringlengths 7 324k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 463 |
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# 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 requir... | transformers/tests/models/gpt2/test_tokenization_gpt2.py/0 | {
"file_path": "transformers/tests/models/gpt2/test_tokenization_gpt2.py",
"repo_id": "transformers",
"token_count": 6979
} | 380 |
# coding=utf-8
# 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 requir... | transformers/tests/models/gptj/test_modeling_gptj.py/0 | {
"file_path": "transformers/tests/models/gptj/test_modeling_gptj.py",
"repo_id": "transformers",
"token_count": 13288
} | 381 |
# 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 requir... | transformers/tests/models/ibert/test_modeling_ibert.py/0 | {
"file_path": "transformers/tests/models/ibert/test_modeling_ibert.py",
"repo_id": "transformers",
"token_count": 14932
} | 382 |
# 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/LICENSE-2.0
#
# Unless r... | transformers/tests/models/llama/test_modeling_llama.py/0 | {
"file_path": "transformers/tests/models/llama/test_modeling_llama.py",
"repo_id": "transformers",
"token_count": 15787
} | 383 |
# 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 requir... | transformers/tests/models/lxmert/test_modeling_tf_lxmert.py/0 | {
"file_path": "transformers/tests/models/lxmert/test_modeling_tf_lxmert.py",
"repo_id": "transformers",
"token_count": 10963
} | 384 |
# coding=utf-8
# Copyright 2023 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 requir... | transformers/tests/models/mega/test_modeling_mega.py/0 | {
"file_path": "transformers/tests/models/mega/test_modeling_mega.py",
"repo_id": "transformers",
"token_count": 13179
} | 385 |
# 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/LICENSE-2.0
#
# Unless ... | transformers/tests/models/mvp/test_modeling_mvp.py/0 | {
"file_path": "transformers/tests/models/mvp/test_modeling_mvp.py",
"repo_id": "transformers",
"token_count": 15208
} | 386 |
# coding=utf-8
# Copyright 2022 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... | transformers/tests/models/oneformer/test_image_processing_oneformer.py/0 | {
"file_path": "transformers/tests/models/oneformer/test_image_processing_oneformer.py",
"repo_id": "transformers",
"token_count": 6432
} | 387 |
# 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/LICENSE-2.0
#
# Unless r... | transformers/tests/models/owlvit/test_modeling_owlvit.py/0 | {
"file_path": "transformers/tests/models/owlvit/test_modeling_owlvit.py",
"repo_id": "transformers",
"token_count": 15371
} | 388 |
# coding=utf-8
# Copyright 2024 The Qwen team, Alibaba Group 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/... | transformers/tests/models/qwen2/test_modeling_qwen2.py/0 | {
"file_path": "transformers/tests/models/qwen2/test_modeling_qwen2.py",
"repo_id": "transformers",
"token_count": 11541
} | 389 |
# 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/LICENSE-2.0
#
# Unless r... | transformers/tests/models/regnet/test_modeling_regnet.py/0 | {
"file_path": "transformers/tests/models/regnet/test_modeling_regnet.py",
"repo_id": "transformers",
"token_count": 4167
} | 390 |
# Copyright 2022 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... | transformers/tests/models/roberta_prelayernorm/test_modeling_flax_roberta_prelayernorm.py/0 | {
"file_path": "transformers/tests/models/roberta_prelayernorm/test_modeling_flax_roberta_prelayernorm.py",
"repo_id": "transformers",
"token_count": 3449
} | 391 |
# Copyright 2023 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... | transformers/tests/models/sam/test_processor_sam.py/0 | {
"file_path": "transformers/tests/models/sam/test_processor_sam.py",
"repo_id": "transformers",
"token_count": 4944
} | 392 |
# 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/LICENSE-2.0
#
# Unless r... | transformers/tests/models/sew/test_modeling_sew.py/0 | {
"file_path": "transformers/tests/models/sew/test_modeling_sew.py",
"repo_id": "transformers",
"token_count": 10134
} | 393 |
# coding=utf-8
# Copyright 2024 BigCode 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
... | transformers/tests/models/starcoder2/test_modeling_starcoder2.py/0 | {
"file_path": "transformers/tests/models/starcoder2/test_modeling_starcoder2.py",
"repo_id": "transformers",
"token_count": 10451
} | 394 |
# coding=utf-8
# Copyright 2018 Google T5 Authors and 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 requ... | transformers/tests/models/t5/test_modeling_tf_t5.py/0 | {
"file_path": "transformers/tests/models/t5/test_modeling_tf_t5.py",
"repo_id": "transformers",
"token_count": 21625
} | 395 |
# coding=utf-8
# Copyright 2021 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... | transformers/tests/models/vision_encoder_decoder/test_modeling_flax_vision_encoder_decoder.py/0 | {
"file_path": "transformers/tests/models/vision_encoder_decoder/test_modeling_flax_vision_encoder_decoder.py",
"repo_id": "transformers",
"token_count": 9445
} | 396 |
# 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/LICENSE-2.0
#
# Unless r... | transformers/tests/models/vit_hybrid/test_modeling_vit_hybrid.py/0 | {
"file_path": "transformers/tests/models/vit_hybrid/test_modeling_vit_hybrid.py",
"repo_id": "transformers",
"token_count": 4698
} | 397 |
# 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... | transformers/tests/models/wav2vec2_with_lm/test_processor_wav2vec2_with_lm.py/0 | {
"file_path": "transformers/tests/models/wav2vec2_with_lm/test_processor_wav2vec2_with_lm.py",
"repo_id": "transformers",
"token_count": 8780
} | 398 |
# coding=utf-8
# 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 requir... | transformers/tests/models/xglm/test_tokenization_xglm.py/0 | {
"file_path": "transformers/tests/models/xglm/test_tokenization_xglm.py",
"repo_id": "transformers",
"token_count": 4221
} | 399 |
# 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... | transformers/tests/pipelines/test_pipelines_table_question_answering.py/0 | {
"file_path": "transformers/tests/pipelines/test_pipelines_table_question_answering.py",
"repo_id": "transformers",
"token_count": 14810
} | 400 |
# coding=utf-8
# Copyright 2023 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 requir... | transformers/tests/quantization/autoawq/test_awq.py/0 | {
"file_path": "transformers/tests/quantization/autoawq/test_awq.py",
"repo_id": "transformers",
"token_count": 8024
} | 401 |
import json
import logging
import os
import subprocess
from argparse import ArgumentParser
logger = logging.getLogger(__name__)
def parse_args():
parser = ArgumentParser()
parsed, unknown = parser.parse_known_args()
for arg in unknown:
if arg.startswith(("-", "--")):
parser.add_argum... | transformers/tests/sagemaker/scripts/pytorch/run_ddp.py/0 | {
"file_path": "transformers/tests/sagemaker/scripts/pytorch/run_ddp.py",
"repo_id": "transformers",
"token_count": 694
} | 402 |
# coding=utf-8
# Copyright 2022 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... | transformers/tests/test_image_transforms.py/0 | {
"file_path": "transformers/tests/test_image_transforms.py",
"repo_id": "transformers",
"token_count": 12379
} | 403 |
# coding=utf-8
# Copyright 2023 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... | transformers/tests/tools/test_agent_types.py/0 | {
"file_path": "transformers/tests/tools/test_agent_types.py",
"repo_id": "transformers",
"token_count": 1583
} | 404 |
# 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... | transformers/tests/trainer/test_trainer_callback.py/0 | {
"file_path": "transformers/tests/trainer/test_trainer_callback.py",
"repo_id": "transformers",
"token_count": 4143
} | 405 |
# 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... | transformers/tests/utils/test_generic.py/0 | {
"file_path": "transformers/tests/utils/test_generic.py",
"repo_id": "transformers",
"token_count": 3485
} | 406 |
# coding=utf-8
# Copyright 2022 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... | transformers/utils/check_config_docstrings.py/0 | {
"file_path": "transformers/utils/check_config_docstrings.py",
"repo_id": "transformers",
"token_count": 1293
} | 407 |
""" Script for downloading all GLUE data.
Original source: https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e
Note: for legal reasons, we are unable to host MRPC.
You can either use the version hosted by the SentEval team, which is already tokenized,
or you can download the original data from (https://... | transformers/utils/download_glue_data.py/0 | {
"file_path": "transformers/utils/download_glue_data.py",
"repo_id": "transformers",
"token_count": 3917
} | 408 |
# compound experiments: gpt2xl + grad_accu
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --exp_name ppo_gpt2xl_grad_accu --model_name gpt2-xl --mini_batch_size 16 --gradient_accumulation_steps 8 --log_with wandb" \
--num-seeds 3 \
--start-seed 1 \
--workers 10 \
--slurm-n... | trl/benchmark/benchmark_level2.sh/0 | {
"file_path": "trl/benchmark/benchmark_level2.sh",
"repo_id": "trl",
"token_count": 479
} | 409 |
# Denoising Diffusion Policy Optimization
## The why
| Before | After DDPO finetuning |
| --- | --- |
| <div style="text-align: center"><img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/pre_squirrel.png"/></div> | <div style="text-align: center"><img src="https://huggin... | trl/docs/source/ddpo_trainer.mdx/0 | {
"file_path": "trl/docs/source/ddpo_trainer.mdx",
"repo_id": "trl",
"token_count": 2407
} | 410 |
# Reward Modeling
TRL supports custom reward modeling for anyone to perform reward modeling on their dataset and model.
Check out a complete flexible example at [`examples/scripts/reward_modeling.py`](https://github.com/huggingface/trl/tree/main/examples/scripts/reward_modeling.py).
## Expected dataset format
The [... | trl/docs/source/reward_trainer.mdx/0 | {
"file_path": "trl/docs/source/reward_trainer.mdx",
"repo_id": "trl",
"token_count": 877
} | 411 |
# 0. imports
import torch
from transformers import GPT2Tokenizer
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer
# 1. load a pretrained model
model = AutoModelForCausalLMWithValueHead.from_pretrained("gpt2")
model_ref = AutoModelForCausalLMWithValueHead.from_pretrained("gpt2")
tokenizer = GP... | trl/examples/hello_world.py/0 | {
"file_path": "trl/examples/hello_world.py",
"repo_id": "trl",
"token_count": 501
} | 412 |
# Copyright 2023 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 required by appl... | trl/examples/research_projects/tools/python_interpreter.py/0 | {
"file_path": "trl/examples/research_projects/tools/python_interpreter.py",
"repo_id": "trl",
"token_count": 2604
} | 413 |
# Copyright 2023 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... | trl/tests/test_iterative_sft_trainer.py/0 | {
"file_path": "trl/tests/test_iterative_sft_trainer.py",
"repo_id": "trl",
"token_count": 2176
} | 414 |
import logging
from typing import Callable, Literal, Optional, Union
from datasets import Dataset, Value
from transformers import AutoTokenizer
from ..trainer.utils import ConstantLengthDataset
FORMAT_MAPPING = {
"chatml": [{"content": Value(dtype="string", id=None), "role": Value(dtype="string", id=None)}],
... | trl/trl/extras/dataset_formatting.py/0 | {
"file_path": "trl/trl/extras/dataset_formatting.py",
"repo_id": "trl",
"token_count": 1400
} | 415 |
from dataclasses import dataclass, field
from typing import List, Optional
from ..core import flatten_dict
@dataclass
class ModelConfig:
"""
Arguments which define the model and tokenizer to load.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={"help": ("The mo... | trl/trl/trainer/model_config.py/0 | {
"file_path": "trl/trl/trainer/model_config.py",
"repo_id": "trl",
"token_count": 1309
} | 416 |
<!--Copyright 2024 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 agreed... | accelerate/docs/source/basic_tutorials/execution.md/0 | {
"file_path": "accelerate/docs/source/basic_tutorials/execution.md",
"repo_id": "accelerate",
"token_count": 1307
} | 0 |
<!--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 agreed... | accelerate/docs/source/quicktour.md/0 | {
"file_path": "accelerate/docs/source/quicktour.md",
"repo_id": "accelerate",
"token_count": 2783
} | 1 |
<!--Copyright 2022 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 agreed... | accelerate/docs/source/usage_guides/tracking.md/0 | {
"file_path": "accelerate/docs/source/usage_guides/tracking.md",
"repo_id": "accelerate",
"token_count": 2703
} | 2 |
# 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/LICENSE-2.0
#
# Unless required by appl... | accelerate/examples/complete_cv_example.py/0 | {
"file_path": "accelerate/examples/complete_cv_example.py",
"repo_id": "accelerate",
"token_count": 5356
} | 3 |
# Copyright 2022 The HuggingFace Team and Brian Chao. 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 requir... | accelerate/src/accelerate/commands/menu/selection_menu.py/0 | {
"file_path": "accelerate/src/accelerate/commands/menu/selection_menu.py",
"repo_id": "accelerate",
"token_count": 2187
} | 4 |
# Copyright 2022 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... | accelerate/src/accelerate/utils/__init__.py/0 | {
"file_path": "accelerate/src/accelerate/utils/__init__.py",
"repo_id": "accelerate",
"token_count": 2408
} | 5 |
- title: Unit 0. Welcome to the RLHF Handbook!
sections:
- local: chapter0/introduction
title: What is this about? | alignment-handbook/chapters/en/_toctree.yml/0 | {
"file_path": "alignment-handbook/chapters/en/_toctree.yml",
"repo_id": "alignment-handbook",
"token_count": 38
} | 6 |
#!/bin/bash
# Define an array containing the base configs we wish to fine tune
configs=("zephyr" "openhermes")
# Define an array of loss types
loss_types=("sigmoid" "kto_pair" "ipo")
# Define an array of beta values
betas=("0.01" "0.1" "0.2" "0.3" "0.4" "0.5" "0.6" "0.7" "0.8" "0.9")
# Outer loop for loss types
for co... | alignment-handbook/recipes/pref_align_scan/launch_scan.sh/0 | {
"file_path": "alignment-handbook/recipes/pref_align_scan/launch_scan.sh",
"repo_id": "alignment-handbook",
"token_count": 430
} | 7 |
[isort]
default_section = FIRSTPARTY
ensure_newline_before_comments = True
force_grid_wrap = 0
include_trailing_comma = True
known_first_party = alignment
known_third_party =
transformers
datasets
fugashi
git
h5py
matplotlib
nltk
numpy
packaging
pandas
psutil
pytest
r... | alignment-handbook/setup.cfg/0 | {
"file_path": "alignment-handbook/setup.cfg",
"repo_id": "alignment-handbook",
"token_count": 297
} | 8 |
# Summary
[Introduction](README.md)
# User Guide
- [Installation](guide/installation.md)
- [Hello World - MNIST](guide/hello_world.md)
- [PyTorch cheatsheet](guide/cheatsheet.md)
# Reference Guide
- [Running a model](inference/inference.md)
- [Using the hub](inference/hub.md)
- [Error management](error_manage.... | candle/candle-book/src/SUMMARY.md/0 | {
"file_path": "candle/candle-book/src/SUMMARY.md",
"repo_id": "candle",
"token_count": 274
} | 9 |
# Writing a custom kernel
| candle/candle-book/src/inference/cuda/writing.md/0 | {
"file_path": "candle/candle-book/src/inference/cuda/writing.md",
"repo_id": "candle",
"token_count": 6
} | 10 |
pub(crate) mod affine;
pub(crate) mod matmul;
pub(crate) mod random;
pub(crate) mod where_cond;
use candle_core::{Device, Result};
pub(crate) trait BenchDevice {
fn sync(&self) -> Result<()>;
fn bench_name<S: Into<String>>(&self, name: S) -> String;
}
impl BenchDevice for Device {
fn sync(&self) -> Resu... | candle/candle-core/benches/benchmarks/mod.rs/0 | {
"file_path": "candle/candle-core/benches/benchmarks/mod.rs",
"repo_id": "candle",
"token_count": 1019
} | 11 |
use super::Cpu;
#[cfg(target_arch = "arm")]
use core::arch::arm::*;
#[cfg(target_arch = "aarch64")]
use core::arch::aarch64::*;
pub struct CurrentCpu {}
const STEP: usize = 16;
const EPR: usize = 4;
const ARR: usize = STEP / EPR;
impl CurrentCpu {
#[cfg(target_arch = "aarch64")]
unsafe fn reduce_one(x: floa... | candle/candle-core/src/cpu/neon.rs/0 | {
"file_path": "candle/candle-core/src/cpu/neon.rs",
"repo_id": "candle",
"token_count": 897
} | 12 |
//! Numpy support for tensors.
//!
//! The spec for the npy format can be found in
//! [npy-format](https://docs.scipy.org/doc/numpy-1.14.2/neps/npy-format.html).
//! The functions from this module can be used to read tensors from npy/npz files
//! or write tensors to these files. A npy file contains a single tensor (u... | candle/candle-core/src/npy.rs/0 | {
"file_path": "candle/candle-core/src/npy.rs",
"repo_id": "candle",
"token_count": 8717
} | 13 |
use crate::{Result, Tensor, WithDType};
pub enum TensorScalar {
Tensor(Tensor),
Scalar(Tensor),
}
pub trait TensorOrScalar {
fn to_tensor_scalar(self) -> Result<TensorScalar>;
}
impl TensorOrScalar for &Tensor {
fn to_tensor_scalar(self) -> Result<TensorScalar> {
Ok(TensorScalar::Tensor(self.... | candle/candle-core/src/scalar.rs/0 | {
"file_path": "candle/candle-core/src/scalar.rs",
"repo_id": "candle",
"token_count": 261
} | 14 |
use candle_core::{test_device, test_utils, Device, IndexOp, Result, Tensor};
// https://github.com/huggingface/candle/issues/364
fn avg_pool2d(dev: &Device) -> Result<()> {
let data: Vec<f32> = vec![
1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
];
let t = Tensor::from_vec(data, (... | candle/candle-core/tests/pool_tests.rs/0 | {
"file_path": "candle/candle-core/tests/pool_tests.rs",
"repo_id": "candle",
"token_count": 2083
} | 15 |
//! Helper functions for the tinystories dataset. This uses the pre-tokenized version as generated
//! by the tools from https://github.com/karpathy/llama2.c
use candle::{Device, Result, Tensor};
pub struct Dataset {
valid_tokens: Vec<memmap2::Mmap>,
train_tokens: Vec<memmap2::Mmap>,
}
fn mmap_file(p: &std::p... | candle/candle-datasets/src/nlp/tinystories.rs/0 | {
"file_path": "candle/candle-datasets/src/nlp/tinystories.rs",
"repo_id": "candle",
"token_count": 2097
} | 16 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::{Parser, ValueEnum};
use candle::{DType, IndexOp, D};
use candle_nn::{Module, VarBuilder};
use candle_transformers::models::convnext;
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
At... | candle/candle-examples/examples/convnext/main.rs/0 | {
"file_path": "candle/candle-examples/examples/convnext/main.rs",
"repo_id": "candle",
"token_count": 1919
} | 17 |
// TODO: Add an offline mode.
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::{Error as E, Result};
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use clap::Parser;
use h... | candle/candle-examples/examples/falcon/main.rs/0 | {
"file_path": "candle/candle-examples/examples/falcon/main.rs",
"repo_id": "candle",
"token_count": 2723
} | 18 |
# candle-phi: 1.3b and 2.7b LLM with state of the art performance for <10b models.
[Phi-1.5](https://huggingface.co/microsoft/phi-1_5) and
[Phi-2](https://huggingface.co/microsoft/phi-2) are language models using
only 1.3 and 2.7 billion parameters but with state of the art performance compared to
models with up to 10... | candle/candle-examples/examples/phi/README.md/0 | {
"file_path": "candle/candle-examples/examples/phi/README.md",
"repo_id": "candle",
"token_count": 1011
} | 19 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::mpt::{Config, Model as M};
use candle_transformers::models::quantized_mpt::Model as Q;
use candle::{DType, Device, Tens... | candle/candle-examples/examples/replit-code/main.rs/0 | {
"file_path": "candle/candle-examples/examples/replit-code/main.rs",
"repo_id": "candle",
"token_count": 3752
} | 20 |
# candle-stable-diffusion: A Diffusers API in Rust/Candle
_A rusty robot holding a fire torch in its hand_, generated by Stable Diffusion
XL using Rust and [candle](https://github.com/huggingface/candle).
The `stable-diffusion` example is a conversion... | candle/candle-examples/examples/stable-diffusion/README.md/0 | {
"file_path": "candle/candle-examples/examples/stable-diffusion/README.md",
"repo_id": "candle",
"token_count": 917
} | 21 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::object_detection::{non_maximum_suppression, Bbox};
mod darknet;
use anyhow::Result;
use candle::{DType, Device, Tensor};
use candle_nn::{Module, VarBuilder};
use clap::Parser;
use ... | candle/candle-examples/examples/yolo-v3/main.rs/0 | {
"file_path": "candle/candle-examples/examples/yolo-v3/main.rs",
"repo_id": "candle",
"token_count": 3180
} | 22 |
[package]
name = "candle-flash-attn"
version = "0.4.2"
edition = "2021"
description = "Flash attention layer for the candle ML framework."
repository = "https://github.com/huggingface/candle"
keywords = ["blas", "tensor", "machine-learning"]
categories = ["science"]
license = "MIT OR Apache-2.0"
readme = "README.md"
... | candle/candle-flash-attn/Cargo.toml/0 | {
"file_path": "candle/candle-flash-attn/Cargo.toml",
"repo_id": "candle",
"token_count": 266
} | 23 |
mod ffi;
use candle::backend::BackendStorage;
use candle::cuda_backend::cudarc::driver::DevicePtr;
use candle::cuda_backend::WrapErr;
use candle::{CpuStorage, DType, Layout, Result, Shape, Tensor};
use half::{bf16, f16};
pub struct FlashAttn {
pub softmax_scale: f32,
pub alibi_slopes: Option<Tensor>,
pub ... | candle/candle-flash-attn/src/lib.rs/0 | {
"file_path": "candle/candle-flash-attn/src/lib.rs",
"repo_id": "candle",
"token_count": 15951
} | 24 |
#include "cuda_utils.cuh"
#include <cmath>
#include <stdint.h>
const int BLOCK_SIZE = 1024;
// TODO: Maybe add some fast_sum_f16_f32 variant that not only accumulate in f32
// but also expect a f32 output so that this can be used for normalization e.g.
// in softmax.
// Fast reduce sum kernel, this assumes that the ... | candle/candle-kernels/src/reduce.cu/0 | {
"file_path": "candle/candle-kernels/src/reduce.cu",
"repo_id": "candle",
"token_count": 8419
} | 25 |
use super::*;
use half::{bf16, f16};
use metal::{Buffer, Device, MTLResourceOptions};
fn read_to_vec<T: Clone>(buffer: &Buffer, n: usize) -> Vec<T> {
let ptr = buffer.contents() as *const T;
assert!(!ptr.is_null());
let slice = unsafe { std::slice::from_raw_parts(ptr, n) };
slice.to_vec()
}
fn new_buf... | candle/candle-metal-kernels/src/tests.rs/0 | {
"file_path": "candle/candle-metal-kernels/src/tests.rs",
"repo_id": "candle",
"token_count": 15812
} | 26 |
//! Group Normalization.
//!
//! This layer applies Group Normalization over a mini-batch of inputs.
use candle::{DType, Result, Tensor};
// This group norm version handles both weight and bias so removes the mean.
#[derive(Clone, Debug)]
pub struct GroupNorm {
weight: Tensor,
bias: Tensor,
eps: f64,
n... | candle/candle-nn/src/group_norm.rs/0 | {
"file_path": "candle/candle-nn/src/group_norm.rs",
"repo_id": "candle",
"token_count": 1372
} | 27 |
use candle::{Result, Shape, Tensor};
use candle_nn::encoding::one_hot;
#[test]
fn test_i64_one_hot() -> Result<()> {
let device = candle::Device::Cpu;
let indices = Tensor::new(vec![vec![0i64, 2], vec![1, -1]], &device)?;
let depth = 4;
let on_value = 1.0;
let off_value = 0.0;
let one_hot = ... | candle/candle-nn/tests/one_hot.rs/0 | {
"file_path": "candle/candle-nn/tests/one_hot.rs",
"repo_id": "candle",
"token_count": 1592
} | 28 |
fn main() {
pyo3_build_config::add_extension_module_link_args();
}
| candle/candle-pyo3/build.rs/0 | {
"file_path": "candle/candle-pyo3/build.rs",
"repo_id": "candle",
"token_count": 30
} | 29 |
import candle
from candle import Tensor
_UNSIGNED_DTYPES = set([str(candle.u8), str(candle.u32)])
def _assert_tensor_metadata(
actual: Tensor,
expected: Tensor,
check_device: bool = True,
check_dtype: bool = True,
check_layout: bool = True,
check_stride: bool = False,
):
if check_device:... | candle/candle-pyo3/py_src/candle/testing/__init__.py/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/testing/__init__.py",
"repo_id": "candle",
"token_count": 854
} | 30 |
import candle
from candle import Tensor
from candle.testing import assert_equal, assert_almost_equal
import pytest
@pytest.mark.parametrize("dtype", [candle.f32, candle.f64, candle.f16, candle.u32, candle.u8, candle.i64])
def test_assert_equal_asserts_correctly(dtype: candle.DType):
a = Tensor([1, 2, 3]).to(dtype... | candle/candle-pyo3/tests/bindings/test_testing.py/0 | {
"file_path": "candle/candle-pyo3/tests/bindings/test_testing.py",
"repo_id": "candle",
"token_count": 476
} | 31 |
use super::with_tracing::{layer_norm, linear, LayerNorm, Linear};
use candle::{DType, Device, Result, Tensor};
use candle_nn::{Embedding, Module, VarBuilder};
use serde::Deserialize;
pub const DTYPE: DType = DType::F32;
fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: f32) -> Result<Tensor> {
let shape =... | candle/candle-transformers/src/models/distilbert.rs/0 | {
"file_path": "candle/candle-transformers/src/models/distilbert.rs",
"repo_id": "candle",
"token_count": 5381
} | 32 |
//! MobileOne inference implementation based on timm and candle-repvgg
//!
//! See "MobileOne: An Improved One millisecond Mobile Backbone"
//! https://arxiv.org/abs/2206.04040
use candle::{DType, Result, Tensor, D};
use candle_nn::{
batch_norm, conv2d, conv2d_no_bias, linear, ops::sigmoid, BatchNorm, Conv2d, Conv... | candle/candle-transformers/src/models/mobileone.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mobileone.rs",
"repo_id": "candle",
"token_count": 4721
} | 33 |
// T5 Text Model, quantized version
// https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py
use crate::models::t5::{deserialize_feed_forward_proj_activation, ActivationWithOptionalGating};
use crate::models::with_tracing::QMatMul;
use crate::quantized_nn::Embedding;
pub use c... | candle/candle-transformers/src/models/quantized_t5.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_t5.rs",
"repo_id": "candle",
"token_count": 13996
} | 34 |
//! # Denoising Diffusion Implicit Models
//!
//! The Denoising Diffusion Implicit Models (DDIM) is a simple scheduler
//! similar to Denoising Diffusion Probabilistic Models (DDPM). The DDPM
//! generative process is the reverse of a Markovian process, DDIM generalizes
//! this to non-Markovian guidance.
//!
//! Denoi... | candle/candle-transformers/src/models/stable_diffusion/ddim.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/ddim.rs",
"repo_id": "candle",
"token_count": 3953
} | 35 |
use crate::models::with_tracing::{conv2d, linear, linear_no_bias, Conv2d, Linear};
use candle::{IndexOp, Module, Result, Tensor, D};
use candle_nn::{layer_norm, LayerNorm, VarBuilder};
// https://github.com/huggingface/transformers/blob/main/src/transformers/models/vit/configuration_vit.py
#[derive(Debug, Clone, serde... | candle/candle-transformers/src/models/vit.rs/0 | {
"file_path": "candle/candle-transformers/src/models/vit.rs",
"repo_id": "candle",
"token_count": 5870
} | 36 |
use candle::{DType, Device, IndexOp, Result, Tensor, D};
use candle_nn::{
embedding, linear_no_bias as linear, rms_norm, Embedding, Linear, Module, RmsNorm, VarBuilder,
};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
#[derive(Debug, Clone)]
pub struct Config {
pub dim: usize, // transform... | candle/candle-wasm-examples/llama2-c/src/model.rs/0 | {
"file_path": "candle/candle-wasm-examples/llama2-c/src/model.rs",
"repo_id": "candle",
"token_count": 5272
} | 37 |
[package]
name = "candle-wasm-example-t5"
version.workspace = true
edition.workspace = true
description.workspace = true
repository.workspace = true
keywords.workspace = true
categories.workspace = true
license.workspace = true
[dependencies]
candle = { workspace = true }
candle-nn = { workspace = true }
candle-transf... | candle/candle-wasm-examples/t5/Cargo.toml/0 | {
"file_path": "candle/candle-wasm-examples/t5/Cargo.toml",
"repo_id": "candle",
"token_count": 305
} | 38 |
use crate::console_log;
use crate::worker::{ModelData, Segment, Worker, WorkerInput, WorkerOutput};
use js_sys::Date;
use wasm_bindgen::prelude::*;
use wasm_bindgen_futures::JsFuture;
use yew::{html, Component, Context, Html};
use yew_agent::{Bridge, Bridged};
const SAMPLE_NAMES: [&str; 6] = [
"audios/samples_jfk.... | candle/candle-wasm-examples/whisper/src/app.rs/0 | {
"file_path": "candle/candle-wasm-examples/whisper/src/app.rs",
"repo_id": "candle",
"token_count": 5679
} | 39 |
use candle_wasm_example_yolo::coco_classes;
use candle_wasm_example_yolo::model::Bbox;
use candle_wasm_example_yolo::worker::Model as M;
use candle_wasm_example_yolo::worker::ModelPose as P;
use wasm_bindgen::prelude::*;
#[wasm_bindgen]
pub struct Model {
inner: M,
}
#[wasm_bindgen]
impl Model {
#[wasm_bindge... | candle/candle-wasm-examples/yolo/src/bin/m.rs/0 | {
"file_path": "candle/candle-wasm-examples/yolo/src/bin/m.rs",
"repo_id": "candle",
"token_count": 840
} | 40 |
# template used in production for HuggingChat.
MODELS=`[
{
"name" : "mistralai/Mixtral-8x7B-Instruct-v0.1",
"description" : "The latest MoE model from Mistral AI! 8x7B and outperforms Llama 2 70B in most benchmarks.",
"logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/mist... | chat-ui/.env.template/0 | {
"file_path": "chat-ui/.env.template",
"repo_id": "chat-ui",
"token_count": 5813
} | 41 |
export default {
plugins: {
tailwindcss: {},
autoprefixer: {},
},
};
| chat-ui/postcss.config.js/0 | {
"file_path": "chat-ui/postcss.config.js",
"repo_id": "chat-ui",
"token_count": 34
} | 42 |
<script lang="ts">
import { base } from "$app/paths";
import { page } from "$app/stores";
import {
PUBLIC_APP_DESCRIPTION,
PUBLIC_APP_NAME,
PUBLIC_APP_DISCLAIMER_MESSAGE,
} from "$env/static/public";
import LogoHuggingFaceBorderless from "$lib/components/icons/LogoHuggingFaceBorderless.svelte";
import Modal... | chat-ui/src/lib/components/DisclaimerModal.svelte/0 | {
"file_path": "chat-ui/src/lib/components/DisclaimerModal.svelte",
"repo_id": "chat-ui",
"token_count": 1051
} | 43 |
<script lang="ts">
import Modal from "./Modal.svelte";
import CarbonClose from "~icons/carbon/close";
import CarbonBlockchain from "~icons/carbon/blockchain";
export let preprompt: string;
let isOpen = false;
</script>
<button
type="button"
class="mx-auto flex items-center gap-1.5 rounded-full border border-g... | chat-ui/src/lib/components/SystemPromptModal.svelte/0 | {
"file_path": "chat-ui/src/lib/components/SystemPromptModal.svelte",
"repo_id": "chat-ui",
"token_count": 508
} | 44 |
<script lang="ts">
export let classNames = "";
</script>
<svg
xmlns="http://www.w3.org/2000/svg"
class={classNames}
width="1em"
height="1em"
fill="none"
viewBox="0 0 32 32"
><path
fill="currentColor"
fill-rule="evenodd"
d="M3.143 20.286h4.286v2.142H3.143A2.143 2.143 0 0 1 1 20.287V3.143A2.143 2.143 0 0 1... | chat-ui/src/lib/components/icons/IconNew.svelte/0 | {
"file_path": "chat-ui/src/lib/components/icons/IconNew.svelte",
"repo_id": "chat-ui",
"token_count": 426
} | 45 |
import { TEXT_EMBEDDING_MODELS } from "$env/static/private";
import { z } from "zod";
import { sum } from "$lib/utils/sum";
import {
embeddingEndpoints,
embeddingEndpointSchema,
type EmbeddingEndpoint,
} from "$lib/server/embeddingEndpoints/embeddingEndpoints";
import { embeddingEndpointTransformersJS } from "$lib/... | chat-ui/src/lib/server/embeddingModels.ts/0 | {
"file_path": "chat-ui/src/lib/server/embeddingModels.ts",
"repo_id": "chat-ui",
"token_count": 1061
} | 46 |
import { z } from "zod";
import { USAGE_LIMITS, RATE_LIMIT } from "$env/static/private";
import JSON5 from "json5";
// RATE_LIMIT is the legacy way to define messages per minute limit
export const usageLimitsSchema = z
.object({
conversations: z.coerce.number().optional(), // how many conversations
messages: z.co... | chat-ui/src/lib/server/usageLimits.ts/0 | {
"file_path": "chat-ui/src/lib/server/usageLimits.ts",
"repo_id": "chat-ui",
"token_count": 297
} | 47 |
// Ideally shouldn't be needed, see https://github.com/huggingface/chat-ui/pull/88#issuecomment-1523173850
import type { Conversation } from "./Conversation";
import type { Timestamps } from "./Timestamps";
export interface AbortedGeneration extends Timestamps {
conversationId: Conversation["_id"];
}
| chat-ui/src/lib/types/AbortedGeneration.ts/0 | {
"file_path": "chat-ui/src/lib/types/AbortedGeneration.ts",
"repo_id": "chat-ui",
"token_count": 93
} | 48 |
export interface Timestamps {
createdAt: Date;
updatedAt: Date;
}
| chat-ui/src/lib/types/Timestamps.ts/0 | {
"file_path": "chat-ui/src/lib/types/Timestamps.ts",
"repo_id": "chat-ui",
"token_count": 23
} | 49 |
import { PUBLIC_APP_ASSETS } from "$env/static/public";
export const isHuggingChat = PUBLIC_APP_ASSETS === "huggingchat";
| chat-ui/src/lib/utils/isHuggingChat.ts/0 | {
"file_path": "chat-ui/src/lib/utils/isHuggingChat.ts",
"repo_id": "chat-ui",
"token_count": 40
} | 50 |
import type { Conversation } from "$lib/types/Conversation";
import type { Message } from "$lib/types/Message";
export function buildSubtree(
conv: Pick<Conversation, "messages" | "rootMessageId">,
id: Message["id"]
): Message[] {
if (!conv.rootMessageId) {
if (conv.messages.length === 0) return [];
// legacy c... | chat-ui/src/lib/utils/tree/buildSubtree.ts/0 | {
"file_path": "chat-ui/src/lib/utils/tree/buildSubtree.ts",
"repo_id": "chat-ui",
"token_count": 329
} | 51 |
import { base } from "$app/paths";
import { collections } from "$lib/server/database.js";
import { redirect } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
export const load = async ({ params }) => {
try {
const assistant = await collections.assistants.findOne({
_id: new ObjectId(params.assistantId),... | chat-ui/src/routes/assistant/[assistantId]/+page.server.ts/0 | {
"file_path": "chat-ui/src/routes/assistant/[assistantId]/+page.server.ts",
"repo_id": "chat-ui",
"token_count": 178
} | 52 |
import { redirect } from "@sveltejs/kit";
import { getOIDCAuthorizationUrl } from "$lib/server/auth";
import { base } from "$app/paths";
import { ALTERNATIVE_REDIRECT_URLS } from "$env/static/private";
export const actions = {
async default({ url, locals, request }) {
const referer = request.headers.get("referer");... | chat-ui/src/routes/login/+page.server.ts/0 | {
"file_path": "chat-ui/src/routes/login/+page.server.ts",
"repo_id": "chat-ui",
"token_count": 286
} | 53 |
import { base } from "$app/paths";
import { redirect } from "@sveltejs/kit";
export async function load({ parent, params }) {
const data = await parent();
const model = data.models.find((m: { id: string }) => m.id === params.model);
if (!model || model.unlisted) {
throw redirect(302, `${base}/settings`);
}
r... | chat-ui/src/routes/settings/(nav)/[...model]/+page.ts/0 | {
"file_path": "chat-ui/src/routes/settings/(nav)/[...model]/+page.ts",
"repo_id": "chat-ui",
"token_count": 111
} | 54 |
# How to add one new datasets
Add datasets directly to the 🤗 Hugging Face Hub!
You can share your dataset on https://huggingface.co/datasets directly using your account, see the documentation:
* [Create a dataset and upload files on the website](https://huggingface.co/docs/datasets/upload_dataset)
* [Advanced guide... | datasets/ADD_NEW_DATASET.md/0 | {
"file_path": "datasets/ADD_NEW_DATASET.md",
"repo_id": "datasets",
"token_count": 113
} | 55 |
# Differences between Dataset and IterableDataset
There are two types of dataset objects, a [`Dataset`] and an [`IterableDataset`].
Whichever type of dataset you choose to use or create depends on the size of the dataset.
In general, an [`IterableDataset`] is ideal for big datasets (think hundreds of GBs!) due to its ... | datasets/docs/source/about_mapstyle_vs_iterable.mdx/0 | {
"file_path": "datasets/docs/source/about_mapstyle_vs_iterable.mdx",
"repo_id": "datasets",
"token_count": 3261
} | 56 |
# Image classification
Image classification datasets are used to train a model to classify an entire image. There are a wide variety of applications enabled by these datasets such as identifying endangered wildlife species or screening for disease in medical images. This guide will show you how to apply transformation... | datasets/docs/source/image_classification.mdx/0 | {
"file_path": "datasets/docs/source/image_classification.mdx",
"repo_id": "datasets",
"token_count": 1043
} | 57 |
# Main classes
## DatasetInfo
[[autodoc]] datasets.DatasetInfo
## Dataset
The base class [`Dataset`] implements a Dataset backed by an Apache Arrow table.
[[autodoc]] datasets.Dataset
- add_column
- add_item
- from_file
- from_buffer
- from_pandas
- from_dict
- from_generator
- dat... | datasets/docs/source/package_reference/main_classes.mdx/0 | {
"file_path": "datasets/docs/source/package_reference/main_classes.mdx",
"repo_id": "datasets",
"token_count": 1908
} | 58 |
# Use with PyTorch
This document is a quick introduction to using `datasets` with PyTorch, with a particular focus on how to get
`torch.Tensor` objects out of our datasets, and how to use a PyTorch `DataLoader` and a Hugging Face `Dataset`
with the best performance.
## Dataset format
By default, datasets return regu... | datasets/docs/source/use_with_pytorch.mdx/0 | {
"file_path": "datasets/docs/source/use_with_pytorch.mdx",
"repo_id": "datasets",
"token_count": 3104
} | 59 |
# Metric Card for Code Eval
## Metric description
The CodeEval metric estimates the pass@k metric for code synthesis.
It implements the evaluation harness for the HumanEval problem solving dataset described in the paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374).
## How... | datasets/metrics/code_eval/README.md/0 | {
"file_path": "datasets/metrics/code_eval/README.md",
"repo_id": "datasets",
"token_count": 1698
} | 60 |
# Metric Card for FrugalScore
## Metric Description
FrugalScore is a reference-based metric for Natural Language Generation (NLG) model evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
The ... | datasets/metrics/frugalscore/README.md/0 | {
"file_path": "datasets/metrics/frugalscore/README.md",
"repo_id": "datasets",
"token_count": 2127
} | 61 |
# Metric Card for Mean IoU
## Metric Description
IoU (Intersection over Union) is the area of overlap between the predicted segmentation and the ground truth divided by the area of union between the predicted segmentation and the ground truth.
For binary (two classes) or multi-class segmentation, the *mean IoU* o... | datasets/metrics/mean_iou/README.md/0 | {
"file_path": "datasets/metrics/mean_iou/README.md",
"repo_id": "datasets",
"token_count": 1803
} | 62 |
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