id int64 0 328k | repository_name stringlengths 7 58 | file_path stringlengths 9 302 | class_name stringlengths 5 256 | human_written_code stringlengths 16 2.16M | class_skeleton stringlengths 18 1.49M ⌀ | total_program_units int64 1 1.76k | total_doc_str int64 0 771 | AvgCountLine float64 0 7.89k | AvgCountLineBlank float64 0 297 | AvgCountLineCode float64 0 7.89k | AvgCountLineComment float64 0 7.89k | AvgCyclomatic float64 0 130 | CommentToCodeRatio float64 0 168 | CountClassBase float64 0 40 | CountClassCoupled float64 0 583 | CountClassCoupledModified float64 0 575 | CountClassDerived float64 0 5.35k | CountDeclInstanceMethod float64 0 529 | CountDeclInstanceVariable float64 0 296 | CountDeclMethod float64 0 599 | CountDeclMethodAll float64 0 1.12k | CountLine float64 1 40.4k | CountLineBlank float64 0 8.16k | CountLineCode float64 1 25.7k | CountLineCodeDecl float64 1 8.15k | CountLineCodeExe float64 0 24.2k | CountLineComment float64 0 16.5k | CountStmt float64 1 9.71k | CountStmtDecl float64 1 8.15k | CountStmtExe float64 0 9.69k | MaxCyclomatic float64 0 759 | MaxInheritanceTree float64 0 16 | MaxNesting float64 0 34 | SumCyclomatic float64 0 2.9k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
400 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/ggml.py | transformers.integrations.ggml.GGUFLlamaConverter | from .. import AddedToken
from ..convert_slow_tokenizer import GemmaConverter, GPT2Converter, LlamaConverter, Qwen2Converter, T5Converter
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
import numpy as np
from tokenizers.models import BPE, Unigram
class GGUFLlamaConverter(LlamaConve... |
class GGUFLlamaConverter(LlamaConverter):
def __init__(self, tokenizer_dict):
pass
def vocab(self, proto):
pass
def merges(self, proto):
pass
def tokenizer(self, proto):
pass
def decoder(self, replacement, add_prefix_space):
pass
def converted(self)... | 7 | 0 | 19 | 4 | 14 | 1 | 4 | 0.08 | 1 | 4 | 1 | 0 | 6 | 4 | 6 | 23 | 119 | 26 | 86 | 28 | 79 | 7 | 69 | 28 | 62 | 12 | 3 | 2 | 24 |
401 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/ggml.py | transformers.integrations.ggml.GGUFPhi3Converter | from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from ..convert_slow_tokenizer import GemmaConverter, GPT2Converter, LlamaConverter, Qwen2Converter, T5Converter
from tokenizers.models import BPE, Unigram
from .. import AddedToken
class GGUFPhi3Converter(LlamaConverter):
def __in... |
class GGUFPhi3Converter(LlamaConverter):
def __init__(self, tokenizer_dict):
pass
def vocab(self, proto):
pass
def merges(self, proto):
pass
def tokenizer(self, proto):
pass
def decoder(self, replacement, add_prefix_space):
pass
def converted(self) ... | 7 | 0 | 11 | 1 | 10 | 0 | 2 | 0.02 | 1 | 4 | 1 | 0 | 6 | 3 | 6 | 23 | 73 | 12 | 60 | 18 | 53 | 1 | 33 | 18 | 26 | 5 | 3 | 1 | 12 |
402 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/ggml.py | transformers.integrations.ggml.GGUFQwen2Converter | from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from ..convert_slow_tokenizer import GemmaConverter, GPT2Converter, LlamaConverter, Qwen2Converter, T5Converter
from .. import AddedToken
class GGUFQwen2Converter(Qwen2Converter):
def __init__(self, tokenizer_dict):
self.o... |
class GGUFQwen2Converter(Qwen2Converter):
def __init__(self, tokenizer_dict):
pass
def converted(self) -> Tokenizer:
pass | 3 | 0 | 8 | 1 | 8 | 0 | 1 | 0 | 1 | 3 | 1 | 0 | 2 | 2 | 2 | 5 | 18 | 2 | 16 | 8 | 13 | 0 | 10 | 8 | 7 | 1 | 2 | 0 | 2 |
403 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/ggml.py | transformers.integrations.ggml.GGUFT5Converter | from tokenizers.models import BPE, Unigram
from ..convert_slow_tokenizer import GemmaConverter, GPT2Converter, LlamaConverter, Qwen2Converter, T5Converter
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
class GGUFT5Converter(T5Converter):
def __init__(self, tokenizer_dict):
... |
class GGUFT5Converter(T5Converter):
def __init__(self, tokenizer_dict):
pass
def vocab(self, proto):
pass
def normalizer(self, proto):
pass
def post_processor(self):
pass
def converted(self) -> Tokenizer:
pass | 6 | 0 | 11 | 1 | 10 | 1 | 2 | 0.06 | 1 | 4 | 1 | 0 | 5 | 4 | 5 | 18 | 61 | 10 | 49 | 18 | 43 | 3 | 37 | 18 | 31 | 5 | 3 | 2 | 11 |
404 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/ggml.py | transformers.integrations.ggml.GGUFTokenizerSkeleton | from ..utils.logging import tqdm
class GGUFTokenizerSkeleton:
def __init__(self, dict_):
for k, v in dict_.items():
setattr(self, k, v)
if not hasattr(self, 'merges'):
if not hasattr(self, 'tokens') or not hasattr(self, 'scores'):
raise ValueError('tokens an... |
class GGUFTokenizerSkeleton:
def __init__(self, dict_):
pass | 2 | 0 | 40 | 5 | 34 | 1 | 11 | 0.03 | 0 | 4 | 0 | 0 | 1 | 4 | 1 | 1 | 41 | 5 | 35 | 15 | 33 | 1 | 32 | 15 | 30 | 11 | 0 | 4 | 11 |
405 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/higgs.py | transformers.integrations.higgs.HiggsLinear | from typing import Optional
class HiggsLinear(torch.nn.Module):
def __init__(self, in_features: int, out_features: int, num_bits: int, bias=True, dtype: Optional[torch.dtype]=None, device: Optional[torch.device]=None, group_size: int=256, hadamard_size: int=1024):
super().__init__()
self.in_featur... |
class HiggsLinear(torch.nn.Module):
def __init__(self, in_features: int, out_features: int, num_bits: int, bias=True, dtype: Optional[torch.dtype]=None, device: Optional[torch.device]=None, group_size: int=256, hadamard_size: int=1024):
pass
def forward(self, x):
pass | 3 | 0 | 29 | 3 | 26 | 1 | 2 | 0.02 | 1 | 3 | 0 | 0 | 2 | 12 | 2 | 12 | 59 | 7 | 52 | 25 | 39 | 1 | 24 | 15 | 21 | 2 | 1 | 1 | 4 |
406 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/integration_utils.py | transformers.integrations.integration_utils.AzureMLCallback | from ..trainer_callback import ProgressCallback, TrainerCallback
class AzureMLCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [AzureML](https://pypi.org/project/azureml-sdk/).
"""
def __init__(self, azureml_run=None):
if not is_azureml_available():
raise... |
class AzureMLCallback(TrainerCallback):
'''
A [`TrainerCallback`] that sends the logs to [AzureML](https://pypi.org/project/azureml-sdk/).
'''
def __init__(self, azureml_run=None):
pass
def on_init_end(self, args, state, control, **kwargs):
pass
def on_log(self, args, state, ... | 4 | 1 | 5 | 0 | 4 | 0 | 3 | 0.21 | 1 | 3 | 0 | 0 | 3 | 1 | 3 | 18 | 21 | 4 | 14 | 7 | 9 | 3 | 14 | 7 | 9 | 4 | 1 | 3 | 8 |
407 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/integration_utils.py | transformers.integrations.integration_utils.ClearMLCallback | import os
from ..utils import ENV_VARS_TRUE_VALUES, is_torch_xla_available
from ..trainer_callback import ProgressCallback, TrainerCallback
from dataclasses import asdict, fields
class ClearMLCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [ClearML](https://clear.ml/).
Environm... |
class ClearMLCallback(TrainerCallback):
'''
A [`TrainerCallback`] that sends the logs to [ClearML](https://clear.ml/).
Environment:
- **CLEARML_PROJECT** (`str`, *optional*, defaults to `HuggingFace Transformers`):
ClearML project name.
- **CLEARML_TASK** (`str`, *optional*, defaults to `Tr... | 8 | 1 | 31 | 1 | 29 | 0 | 5 | 0.05 | 1 | 7 | 0 | 0 | 7 | 5 | 7 | 22 | 251 | 16 | 223 | 42 | 214 | 12 | 114 | 41 | 105 | 16 | 1 | 4 | 38 |
408 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/integration_utils.py | transformers.integrations.integration_utils.CodeCarbonCallback | from ..trainer_callback import ProgressCallback, TrainerCallback
class CodeCarbonCallback(TrainerCallback):
"""
A [`TrainerCallback`] that tracks the CO2 emission of training.
"""
def __init__(self):
if not is_codecarbon_available():
raise RuntimeError('CodeCarbonCallback requires ... |
class CodeCarbonCallback(TrainerCallback):
'''
A [`TrainerCallback`] that tracks the CO2 emission of training.
'''
def __init__(self):
pass
def on_init_end(self, args, state, control, **kwargs):
pass
def on_train_begin(self, args, state, control, model=None, **kwargs):
... | 5 | 1 | 6 | 1 | 5 | 1 | 2 | 0.23 | 1 | 1 | 0 | 0 | 4 | 2 | 4 | 19 | 32 | 6 | 22 | 8 | 16 | 5 | 17 | 8 | 11 | 3 | 1 | 1 | 9 |
409 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/integration_utils.py | transformers.integrations.integration_utils.CometCallback | import os
from ..trainer_callback import ProgressCallback, TrainerCallback
class CometCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [Comet ML](https://www.comet.com/site/).
"""
def __init__(self):
if _is_comet_installed is False or _is_comet_recent_enough is False... |
class CometCallback(TrainerCallback):
'''
A [`TrainerCallback`] that sends the logs to [Comet ML](https://www.comet.com/site/).
'''
def __init__(self):
pass
def setup(self, args, state, model):
'''
Setup the optional Comet integration.
Environment:
- **COME... | 7 | 2 | 22 | 3 | 15 | 5 | 5 | 0.38 | 1 | 1 | 0 | 0 | 6 | 3 | 6 | 21 | 142 | 21 | 88 | 23 | 80 | 33 | 71 | 23 | 63 | 14 | 1 | 3 | 30 |
410 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/integration_utils.py | transformers.integrations.integration_utils.DVCLiveCallback | from typing import TYPE_CHECKING, Any, Literal, Optional, Union
import os
from ..utils import ENV_VARS_TRUE_VALUES, is_torch_xla_available
from ..trainer_callback import ProgressCallback, TrainerCallback
class DVCLiveCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [DVCLive](https://... |
class DVCLiveCallback(TrainerCallback):
'''
A [`TrainerCallback`] that sends the logs to [DVCLive](https://www.dvc.org/doc/dvclive).
Use the environment variables below in `setup` to configure the integration. To customize this callback beyond
those environment variables, see [here](https://dvc.org/doc... | 7 | 2 | 14 | 1 | 11 | 2 | 4 | 0.31 | 1 | 5 | 0 | 0 | 6 | 3 | 6 | 21 | 103 | 14 | 68 | 25 | 51 | 21 | 50 | 20 | 38 | 7 | 1 | 3 | 23 |
411 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/integration_utils.py | transformers.integrations.integration_utils.DagsHubCallback | from pathlib import Path
import os
from ..utils import ENV_VARS_TRUE_VALUES, is_torch_xla_available
class DagsHubCallback(MLflowCallback):
"""
A [`TrainerCallback`] that logs to [DagsHub](https://dagshub.com/). Extends [`MLflowCallback`]
"""
def __init__(self):
super().__init__()
if no... |
class DagsHubCallback(MLflowCallback):
'''
A [`TrainerCallback`] that logs to [DagsHub](https://dagshub.com/). Extends [`MLflowCallback`]
'''
def __init__(self):
pass
def setup(self, *args, **kwargs):
'''
Setup the DagsHub's Logging integration.
Environment:
... | 4 | 2 | 13 | 2 | 9 | 2 | 2 | 0.32 | 1 | 5 | 0 | 0 | 3 | 6 | 3 | 25 | 47 | 10 | 28 | 11 | 23 | 9 | 21 | 11 | 16 | 3 | 2 | 2 | 7 |
412 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/integration_utils.py | transformers.integrations.integration_utils.FlyteCallback | from ..utils import PushToHubMixin, flatten_dict, is_datasets_available, is_pandas_available, is_torch_available, logging
import os
from ..trainer_callback import ProgressCallback, TrainerCallback
class FlyteCallback(TrainerCallback):
"""A [`TrainerCallback`] that sends the logs to [Flyte](https://flyte.org/).
... |
class FlyteCallback(TrainerCallback):
'''A [`TrainerCallback`] that sends the logs to [Flyte](https://flyte.org/).
NOTE: This callback only works within a Flyte task.
Args:
save_log_history (`bool`, *optional*, defaults to `True`):
When set to True, the training logs are saved as a Flyt... | 4 | 1 | 11 | 2 | 9 | 0 | 2 | 0.68 | 1 | 3 | 0 | 0 | 3 | 3 | 3 | 18 | 61 | 14 | 28 | 14 | 20 | 19 | 25 | 14 | 17 | 3 | 1 | 1 | 7 |
413 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/integration_utils.py | transformers.integrations.integration_utils.MLflowCallback | import json
import packaging.version
from ..utils import PushToHubMixin, flatten_dict, is_datasets_available, is_pandas_available, is_torch_available, logging
import re
from ..trainer_callback import ProgressCallback, TrainerCallback
import os
from ..utils import ENV_VARS_TRUE_VALUES, is_torch_xla_available
class MLfl... |
class MLflowCallback(TrainerCallback):
'''
A [`TrainerCallback`] that sends the logs to [MLflow](https://www.mlflow.org/). Can be disabled by setting
environment variable `DISABLE_MLFLOW_INTEGRATION = TRUE`.
'''
def __init__(self):
pass
def setup(self, args, state, model):
'''... | 8 | 2 | 23 | 1 | 17 | 6 | 5 | 0.36 | 1 | 9 | 0 | 1 | 7 | 13 | 7 | 22 | 176 | 14 | 119 | 33 | 110 | 43 | 87 | 33 | 78 | 15 | 1 | 3 | 33 |
414 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/integration_utils.py | transformers.integrations.integration_utils.NeptuneCallback | from .. import __version__ as version
from ..trainer_callback import ProgressCallback, TrainerCallback
import os
import tempfile
from typing import TYPE_CHECKING, Any, Literal, Optional, Union
import shutil
import numpy as np
class NeptuneCallback(TrainerCallback):
"""TrainerCallback that sends the logs to [Neptun... |
class NeptuneCallback(TrainerCallback):
'''TrainerCallback that sends the logs to [Neptune](https://app.neptune.ai).
Args:
api_token (`str`, *optional*): Neptune API token obtained upon registration.
You can leave this argument out if you have saved your token to the `NEPTUNE_API_TOKEN` env... | 25 | 1 | 10 | 1 | 9 | 0 | 3 | 0.13 | 1 | 10 | 1 | 0 | 20 | 14 | 21 | 36 | 271 | 49 | 196 | 77 | 151 | 26 | 163 | 61 | 132 | 6 | 1 | 4 | 56 |
415 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/integration_utils.py | transformers.integrations.integration_utils.NeptuneMissingConfiguration | class NeptuneMissingConfiguration(Exception):
def __init__(self):
super().__init__('\n ------ Unsupported ---- We were not able to create new runs. You provided a custom Neptune run to\n `NeptuneCallback` with the `run` argument. For the integration to work fully, provide your `api_token` and... | class NeptuneMissingConfiguration(Exception):
def __init__(self):
pass | 2 | 0 | 8 | 0 | 8 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 11 | 9 | 0 | 9 | 2 | 7 | 0 | 3 | 2 | 1 | 1 | 3 | 0 | 1 |
416 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/integration_utils.py | transformers.integrations.integration_utils.TensorBoardCallback | import os
from ..trainer_callback import ProgressCallback, TrainerCallback
class TensorBoardCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [TensorBoard](https://www.tensorflow.org/tensorboard).
Args:
tb_writer (`SummaryWriter`, *optional*):
The writer to us... |
class TensorBoardCallback(TrainerCallback):
'''
A [`TrainerCallback`] that sends the logs to [TensorBoard](https://www.tensorflow.org/tensorboard).
Args:
tb_writer (`SummaryWriter`, *optional*):
The writer to use. Will instantiate one if not set.
'''
def __init__(self, tb_write... | 6 | 1 | 15 | 2 | 13 | 0 | 5 | 0.11 | 1 | 7 | 0 | 0 | 5 | 2 | 5 | 20 | 86 | 14 | 66 | 16 | 58 | 7 | 55 | 16 | 47 | 8 | 1 | 3 | 24 |
417 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/integration_utils.py | transformers.integrations.integration_utils.WandbCallback | import importlib.util
import importlib.metadata
from ..trainer_callback import ProgressCallback, TrainerCallback
from .. import PreTrainedModel, TrainingArguments
import os
from ..utils import ENV_VARS_TRUE_VALUES, is_torch_xla_available
import tempfile
from .. import modelcard
import copy
import numbers
from pathlib i... |
class WandbCallback(TrainerCallback):
'''
A [`TrainerCallback`] that logs metrics, media, model checkpoints to [Weight and Biases](https://www.wandb.com/).
'''
def __init__(self):
pass
def setup(self, args, state, model, **kwargs):
'''
Setup the optional Weights & Biases (... | 8 | 2 | 33 | 3 | 26 | 4 | 7 | 0.18 | 1 | 8 | 2 | 0 | 7 | 3 | 7 | 22 | 244 | 28 | 183 | 43 | 172 | 33 | 121 | 39 | 110 | 19 | 1 | 6 | 46 |
418 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/integration_utils.py | transformers.integrations.integration_utils.WandbLogModel | from typing import TYPE_CHECKING, Any, Literal, Optional, Union
import os
from ..utils import ENV_VARS_TRUE_VALUES, is_torch_xla_available
from enum import Enum
class WandbLogModel(str, Enum):
"""Enum of possible log model values in W&B."""
CHECKPOINT = 'checkpoint'
END = 'end'
FALSE = 'false'
@pr... |
class WandbLogModel(str, Enum):
'''Enum of possible log model values in W&B.'''
@property
def is_enabled(self) -> bool:
'''Check if the value corresponds to a state where the `WANDB_LOG_MODEL` setting is enabled.'''
pass
@classmethod
def _missing_(cls, value: Any) -> 'WandbLogModel'... | 5 | 2 | 9 | 0 | 8 | 1 | 2 | 0.09 | 2 | 5 | 0 | 0 | 1 | 0 | 2 | 117 | 27 | 3 | 22 | 8 | 17 | 2 | 15 | 6 | 12 | 3 | 4 | 1 | 4 |
419 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/mistral.py | transformers.integrations.mistral.MistralConverter | from transformers.convert_slow_tokenizer import bytes_to_unicode
from tokenizers import Regex, Tokenizer, decoders, pre_tokenizers, processors
from tokenizers.models import BPE
class MistralConverter:
"""
A general tiktoken converter.
"""
def __init__(self, vocab=None, pattern="(?i:'s|'t|'re|'ve|'m|'l... |
class MistralConverter:
'''
A general tiktoken converter.
'''
def __init__(self, vocab=None, pattern="(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", add_prefix_space=False, additional_special_tokens=None, *args, **... | 6 | 1 | 12 | 1 | 12 | 0 | 2 | 0.05 | 0 | 4 | 0 | 0 | 4 | 4 | 4 | 4 | 68 | 8 | 57 | 28 | 43 | 3 | 43 | 20 | 37 | 6 | 0 | 4 | 11 |
420 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/integrations/peft.py | transformers.integrations.peft.PeftAdapterMixin | import re
from typing import Any, Optional, Union
import warnings
import importlib
from packaging import version
from ..utils import check_peft_version, find_adapter_config_file, is_accelerate_available, is_peft_available, is_torch_available, logging
import inspect
class PeftAdapterMixin:
"""
A class containin... |
class PeftAdapterMixin:
'''
A class containing all functions for loading and using adapters weights that are supported in PEFT library. For
more details about adapters and injecting them on a transformer-based model, check out the documentation of PEFT
library: https://huggingface.co/docs/peft/index
... | 11 | 10 | 51 | 8 | 26 | 16 | 7 | 0.68 | 0 | 12 | 0 | 0 | 10 | 0 | 10 | 10 | 544 | 97 | 266 | 70 | 223 | 181 | 173 | 49 | 151 | 27 | 0 | 4 | 74 |
421 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py | transformers.kernels.falcon_mamba.selective_scan_with_ln_interface.MambaInnerFn | from einops import rearrange, repeat
from torch.cuda.amp import custom_bwd, custom_fwd
import torch
import torch.nn.functional as F
import selective_scan_cuda
class MambaInnerFn(torch.autograd.Function):
@staticmethod
@custom_fwd
def forward(ctx, xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_w... |
class MambaInnerFn(torch.autograd.Function):
@staticmethod
@custom_fwd
def forward(ctx, xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight, out_proj_weight, out_proj_bias, A, B=None, C=None, D=None, delta_bias=None, B_proj_bias=None, C_proj_bias=None, delta_softplus=True, checkpoint_lvl=1, b... | 7 | 1 | 128 | 2 | 119 | 13 | 18 | 0.11 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 32 | 262 | 4 | 242 | 70 | 213 | 26 | 128 | 28 | 125 | 19 | 5 | 2 | 36 |
422 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py | transformers.kernels.falcon_mamba.selective_scan_with_ln_interface.SelectiveScanFn | import torch.nn.functional as F
import torch
from einops import rearrange, repeat
import selective_scan_cuda
class SelectiveScanFn(torch.autograd.Function):
@staticmethod
def forward(ctx, u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False, return_last_state=False):
if u.stride(-1... |
class SelectiveScanFn(torch.autograd.Function):
@staticmethod
def forward(ctx, u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False, return_last_state=False):
pass
@staticmethod
def backward(ctx, dout, *args):
pass | 5 | 0 | 38 | 0 | 36 | 3 | 10 | 0.07 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 32 | 79 | 1 | 75 | 15 | 68 | 5 | 43 | 11 | 40 | 12 | 5 | 1 | 20 |
423 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/loss/loss_deformable_detr.py | transformers.loss.loss_deformable_detr.DeformableDetrHungarianMatcher | import torch.nn as nn
from ..image_transforms import center_to_corners_format
import torch
from .loss_for_object_detection import HungarianMatcher, ImageLoss, _set_aux_loss, generalized_box_iou, sigmoid_focal_loss
class DeformableDetrHungarianMatcher(HungarianMatcher):
@torch.no_grad()
def forward(self, outpu... |
class DeformableDetrHungarianMatcher(HungarianMatcher):
@torch.no_grad()
def forward(self, outputs, targets):
'''
Differences:
- out_prob = outputs["logits"].flatten(0, 1).sigmoid() instead of softmax
- class_cost uses alpha and gamma
'''
pass | 3 | 1 | 36 | 7 | 18 | 13 | 1 | 0.65 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 13 | 38 | 7 | 20 | 18 | 17 | 13 | 19 | 17 | 17 | 1 | 2 | 0 | 1 |
424 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/loss/loss_deformable_detr.py | transformers.loss.loss_deformable_detr.DeformableDetrImageLoss | import torch.nn as nn
import torch
from .loss_for_object_detection import HungarianMatcher, ImageLoss, _set_aux_loss, generalized_box_iou, sigmoid_focal_loss
class DeformableDetrImageLoss(ImageLoss):
def __init__(self, matcher, num_classes, focal_alpha, losses):
nn.Module.__init__(self)
self.match... |
class DeformableDetrImageLoss(ImageLoss):
def __init__(self, matcher, num_classes, focal_alpha, losses):
pass
def loss_labels(self, outputs, targets, indices, num_boxes):
'''
Classification loss (Binary focal loss) targets dicts must contain the key "class_labels" containing a tensor
... | 3 | 1 | 19 | 2 | 15 | 2 | 2 | 0.16 | 1 | 2 | 0 | 0 | 2 | 4 | 2 | 21 | 41 | 5 | 31 | 14 | 28 | 5 | 21 | 14 | 18 | 2 | 2 | 1 | 3 |
425 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/loss/loss_for_object_detection.py | transformers.loss.loss_for_object_detection.HungarianMatcher | import torch.nn as nn
import torch
from ..utils import is_accelerate_available, is_scipy_available, is_vision_available, requires_backends
class HungarianMatcher(nn.Module):
"""
This class computes an assignment between the targets and the predictions of the network.
For efficiency reasons, the targets do... |
class HungarianMatcher(nn.Module):
'''
This class computes an assignment between the targets and the predictions of the network.
For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more
predictions than targets. In this case, we do a 1-to-1 matching o... | 4 | 2 | 29 | 5 | 11 | 14 | 2 | 1.71 | 1 | 4 | 0 | 1 | 2 | 3 | 2 | 12 | 76 | 13 | 24 | 18 | 20 | 41 | 23 | 17 | 20 | 2 | 1 | 1 | 3 |
426 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/loss/loss_for_object_detection.py | transformers.loss.loss_for_object_detection.ImageLoss | import torch.nn as nn
import torch
from ..utils import is_accelerate_available, is_scipy_available, is_vision_available, requires_backends
class ImageLoss(nn.Module):
"""
This class computes the losses for DetrForObjectDetection/DetrForSegmentation. The process happens in two steps: 1)
we compute hungarian... | null | 11 | 6 | 18 | 2 | 12 | 4 | 2 | 0.52 | 1 | 7 | 0 | 1 | 9 | 4 | 9 | 19 | 197 | 30 | 110 | 53 | 99 | 57 | 95 | 52 | 85 | 8 | 1 | 4 | 20 |
427 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/loss/loss_for_object_detection.py | transformers.loss.loss_for_object_detection.NestedTensor | from torch import Tensor
from typing import Optional
class NestedTensor:
def __init__(self, tensors, mask: Optional[Tensor]):
self.tensors = tensors
self.mask = mask
def to(self, device):
cast_tensor = self.tensors.to(device)
mask = self.mask
if mask is not None:
... |
class NestedTensor:
def __init__(self, tensors, mask: Optional[Tensor]):
pass
def to(self, device):
pass
def decompose(self):
pass
def __repr__(self):
pass | 5 | 0 | 4 | 0 | 4 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 4 | 2 | 4 | 4 | 19 | 3 | 16 | 10 | 11 | 0 | 15 | 10 | 10 | 2 | 0 | 1 | 5 |
428 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/loss/loss_rt_detr.py | transformers.loss.loss_rt_detr.RTDetrHungarianMatcher | import torch.nn as nn
import torch
from ..utils import is_scipy_available, is_vision_available, requires_backends
import torch.nn.functional as F
from .loss_for_object_detection import box_iou, dice_loss, generalized_box_iou, nested_tensor_from_tensor_list, sigmoid_focal_loss
class RTDetrHungarianMatcher(nn.Module):
... |
class RTDetrHungarianMatcher(nn.Module):
'''This class computes an assignment between the targets and the predictions of the network
For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more
predictions than targets. In this case, we do a 1-to-1 matching o... | 4 | 2 | 33 | 5 | 16 | 13 | 2 | 0.97 | 1 | 3 | 0 | 0 | 2 | 6 | 2 | 12 | 79 | 14 | 34 | 23 | 30 | 33 | 32 | 22 | 29 | 2 | 1 | 1 | 4 |
429 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/loss/loss_rt_detr.py | transformers.loss.loss_rt_detr.RTDetrLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
from .loss_for_object_detection import box_iou, dice_loss, generalized_box_iou, nested_tensor_from_tensor_list, sigmoid_focal_loss
class RTDetrLoss(nn.Module):
"""
This class computes the losses for RTDetr. The process happens in two steps: 1) ... |
class RTDetrLoss(nn.Module):
'''
This class computes the losses for RTDetr. The process happens in two steps: 1) we compute hungarian assignment
between ground truth boxes and the outputs of the model 2) we supervise each pair of matched ground-truth /
prediction (supervise class and box).
Args:
... | 16 | 6 | 21 | 2 | 16 | 3 | 2 | 0.28 | 1 | 7 | 1 | 0 | 12 | 7 | 13 | 23 | 306 | 43 | 205 | 92 | 189 | 58 | 168 | 90 | 154 | 11 | 1 | 4 | 32 |
430 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modelcard.py | transformers.modelcard.ModelCard | import os
import json
import warnings
from .utils import MODEL_CARD_NAME, cached_file, is_datasets_available, is_offline_mode, is_tokenizers_available, is_torch_available, logging
import copy
class ModelCard:
"""
Structured Model Card class. Store model card as well as methods for loading/downloading/saving mo... |
class ModelCard:
'''
Structured Model Card class. Store model card as well as methods for loading/downloading/saving model cards.
Please read the following paper for details and explanation on the sections: "Model Cards for Model Reporting" by
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barne... | 14 | 8 | 15 | 2 | 9 | 5 | 2 | 0.6 | 0 | 4 | 0 | 0 | 7 | 9 | 10 | 10 | 171 | 29 | 89 | 41 | 75 | 53 | 74 | 35 | 63 | 9 | 0 | 3 | 21 |
431 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modelcard.py | transformers.modelcard.TrainingSummary | from .utils import MODEL_CARD_NAME, cached_file, is_datasets_available, is_offline_mode, is_tokenizers_available, is_torch_available, logging
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from dataclasses import dataclass
import yaml
import os
from ... | @dataclass
class TrainingSummary:
def __post_init__(self):
pass
def create_model_index(self, metric_mapping):
pass
def create_metadata(self):
pass
def to_model_card(self):
pass
@classmethod
def from_trainer(cls, trainer, language=None, license=None, tags=None,... | 8 | 0 | 53 | 7 | 44 | 4 | 11 | 0.08 | 0 | 7 | 0 | 0 | 4 | 0 | 6 | 6 | 343 | 46 | 282 | 82 | 243 | 23 | 183 | 54 | 172 | 17 | 0 | 4 | 65 |
432 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_attn_mask_utils.py | transformers.modeling_attn_mask_utils.AttentionMaskConverter | import torch
from dataclasses import dataclass
from .utils.import_utils import is_torchdynamo_compiling
from typing import Optional, Union
@dataclass
class AttentionMaskConverter:
"""
A utility attention mask class that allows one to:
- Create a causal 4d mask
- Create a causal 4d mask with sli... | @dataclass
class AttentionMaskConverter:
'''
A utility attention mask class that allows one to:
- Create a causal 4d mask
- Create a causal 4d mask with slided window
- Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length,
k... | 13 | 7 | 33 | 4 | 17 | 12 | 4 | 0.84 | 0 | 7 | 0 | 0 | 3 | 0 | 7 | 7 | 277 | 42 | 128 | 60 | 88 | 107 | 69 | 28 | 61 | 7 | 0 | 3 | 25 |
433 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_flash_attention_utils.py | transformers.modeling_flash_attention_utils.FlashAttentionKwargs | import torch.nn.functional as F
import torch
from typing import Optional, TypedDict
class FlashAttentionKwargs(TypedDict, total=False):
"""
Keyword arguments for Flash Attention with Compile.
Attributes:
cu_seq_lens_q (`torch.LongTensor`, *optional*)
Gets cumulative sequence length for... |
class FlashAttentionKwargs(TypedDict, total=False):
'''
Keyword arguments for Flash Attention with Compile.
Attributes:
cu_seq_lens_q (`torch.LongTensor`, *optional*)
Gets cumulative sequence length for query state.
cu_seq_lens_k (`torch.LongTensor`, *optional*)
Gets... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 2.4 | 2 | 0 | 0 | 23 | 0 | 0 | 0 | 0 | 19 | 2 | 5 | 1 | 4 | 12 | 5 | 1 | 4 | 0 | 1 | 0 | 0 |
434 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_gguf_pytorch_utils.py | transformers.modeling_gguf_pytorch_utils.BloomTensorProcessor | import numpy as np
class BloomTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if 'attn_qkv' in name:
num_heads = self.config['n_head']
n_embed = self.config['hidden_size']
... |
class BloomTensorProcessor(TensorProcessor):
def __init__(self, config=None):
pass
def process(self, weights, name, **kwargs):
pass
def _reverse_reshape_weights(self, weights: np.ndarray, n_head: int, n_embed: int):
pass
def _reverse_reshape_bias(self, weights: np.ndarray, n... | 5 | 0 | 8 | 1 | 6 | 1 | 2 | 0.15 | 1 | 3 | 1 | 0 | 4 | 0 | 4 | 6 | 37 | 7 | 26 | 11 | 21 | 4 | 25 | 11 | 20 | 3 | 1 | 2 | 6 |
435 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_gguf_pytorch_utils.py | transformers.modeling_gguf_pytorch_utils.GGUFTensor | import numpy as np
from typing import NamedTuple, Optional
class GGUFTensor(NamedTuple):
weights: np.ndarray
name: str
metadata: dict |
class GGUFTensor(NamedTuple):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 4 | 1 | 3 | 0 | 4 | 1 | 3 | 0 | 1 | 0 | 0 |
436 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_gguf_pytorch_utils.py | transformers.modeling_gguf_pytorch_utils.GPT2TensorProcessor | import numpy as np
class GPT2TensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if 'attn_qkv.weight' in name or 'ffn_down.weight' in name or 'ffn_up.weight' in name or ('attn_output.weight' in name):
... |
class GPT2TensorProcessor(TensorProcessor):
def __init__(self, config=None):
pass
def process(self, weights, name, **kwargs):
pass | 3 | 0 | 11 | 1 | 8 | 3 | 2 | 0.35 | 1 | 2 | 1 | 0 | 2 | 0 | 2 | 4 | 24 | 2 | 17 | 4 | 14 | 6 | 12 | 4 | 9 | 3 | 1 | 1 | 4 |
437 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_gguf_pytorch_utils.py | transformers.modeling_gguf_pytorch_utils.Gemma2TensorProcessor | class Gemma2TensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if 'norm.weight' in name:
weights = weights - 1
return GGUFTensor(weights, name, {}) | class Gemma2TensorProcessor(TensorProcessor):
def __init__(self, config=None):
pass
def process(self, weights, name, **kwargs):
pass | 3 | 0 | 3 | 0 | 3 | 0 | 2 | 0.29 | 1 | 2 | 1 | 0 | 2 | 0 | 2 | 4 | 10 | 1 | 7 | 3 | 4 | 2 | 7 | 3 | 4 | 2 | 1 | 1 | 3 |
438 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_gguf_pytorch_utils.py | transformers.modeling_gguf_pytorch_utils.LlamaTensorProcessor | from typing import NamedTuple, Optional
import numpy as np
class LlamaTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if '.attn_k.' in name or '.attn_q.' in name:
num_heads = self.config... |
class LlamaTensorProcessor(TensorProcessor):
def __init__(self, config=None):
pass
def process(self, weights, name, **kwargs):
pass
def _reverse_permute_weights(self, weights: np.ndarray, n_head: int, num_kv_heads: Optional[int]=None) -> np.ndarray:
pass | 4 | 0 | 8 | 1 | 7 | 1 | 3 | 0.09 | 1 | 3 | 1 | 0 | 3 | 0 | 3 | 5 | 28 | 4 | 22 | 10 | 16 | 2 | 19 | 8 | 15 | 5 | 1 | 2 | 8 |
439 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_gguf_pytorch_utils.py | transformers.modeling_gguf_pytorch_utils.MambaTensorProcessor | import numpy as np
class MambaTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if 'ssm_conv1d.weight' in name:
weights = np.expand_dims(weights, axis=1)
if 'ssm_a' in name:
... |
class MambaTensorProcessor(TensorProcessor):
def __init__(self, config=None):
pass
def process(self, weights, name, **kwargs):
pass | 3 | 0 | 6 | 0 | 4 | 2 | 2 | 0.44 | 1 | 2 | 1 | 0 | 2 | 0 | 2 | 4 | 14 | 1 | 9 | 3 | 6 | 4 | 9 | 3 | 6 | 3 | 1 | 1 | 4 |
440 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_gguf_pytorch_utils.py | transformers.modeling_gguf_pytorch_utils.NemotronTensorProcessor | class NemotronTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if 'norm.weight' in name:
weights = weights - 1
return GGUFTensor(weights, name, {}) | class NemotronTensorProcessor(TensorProcessor):
def __init__(self, config=None):
pass
def process(self, weights, name, **kwargs):
pass | 3 | 0 | 3 | 0 | 3 | 0 | 2 | 0.14 | 1 | 2 | 1 | 0 | 2 | 0 | 2 | 4 | 9 | 1 | 7 | 3 | 4 | 1 | 7 | 3 | 4 | 2 | 1 | 1 | 3 |
441 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_gguf_pytorch_utils.py | transformers.modeling_gguf_pytorch_utils.Qwen2MoeTensorProcessor | import numpy as np
class Qwen2MoeTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if '_exp' in name:
tensor_key_mapping = kwargs.get('tensor_key_mapping')
parsed_parameters = ... |
class Qwen2MoeTensorProcessor(TensorProcessor):
def __init__(self, config=None):
pass
def process(self, weights, name, **kwargs):
pass
def _split_moe_expert_tensor(self, weights: np.ndarray, parsed_parameters: dict[str, dict], name: str, tensor_key_mapping: dict):
pass | 4 | 0 | 8 | 0 | 7 | 1 | 2 | 0.18 | 1 | 5 | 1 | 0 | 3 | 0 | 3 | 5 | 28 | 2 | 22 | 12 | 16 | 4 | 20 | 10 | 16 | 4 | 1 | 2 | 7 |
442 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_gguf_pytorch_utils.py | transformers.modeling_gguf_pytorch_utils.T5TensorProcessor | class T5TensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
bid = None
for chunk in name.split('.'):
if chunk.isdigit():
bid = int(chunk)
break
... | class T5TensorProcessor(TensorProcessor):
def __init__(self, config=None):
pass
def process(self, weights, name, **kwargs):
pass | 3 | 0 | 5 | 0 | 5 | 0 | 2 | 0 | 1 | 3 | 1 | 0 | 2 | 0 | 2 | 4 | 11 | 1 | 10 | 5 | 7 | 0 | 10 | 5 | 7 | 3 | 1 | 2 | 4 |
443 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_gguf_pytorch_utils.py | transformers.modeling_gguf_pytorch_utils.TensorProcessor | class TensorProcessor:
def __init__(self, config=None):
self.config = config or {}
def process(self, weights, name, **kwargs):
return GGUFTensor(weights, name, {}) | class TensorProcessor:
def __init__(self, config=None):
pass
def process(self, weights, name, **kwargs):
pass | 3 | 0 | 2 | 0 | 2 | 0 | 1 | 0 | 0 | 1 | 1 | 8 | 2 | 1 | 2 | 2 | 6 | 1 | 5 | 4 | 2 | 0 | 5 | 4 | 2 | 1 | 0 | 0 | 2 |
444 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.BackboneOutput | import torch
from typing import Optional
from dataclasses import dataclass
from .utils import ModelOutput
@dataclass
class BackboneOutput(ModelOutput):
"""
Base class for outputs of backbones.
Args:
feature_maps (`tuple(torch.FloatTensor)` of shape `(batch_size, num_channels, height, width)`):
... | @dataclass
class BackboneOutput(ModelOutput):
'''
Base class for outputs of backbones.
Args:
feature_maps (`tuple(torch.FloatTensor)` of shape `(batch_size, num_channels, height, width)`):
Feature maps of the stages.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 4 | 4 | 4 | 3 | 16 | 4 | 4 | 3 | 0 | 1 | 0 | 0 |
445 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.BaseModelOutput | from .utils import ModelOutput
from typing import Optional
import torch
from dataclasses import dataclass
@dataclass
class BaseModelOutput(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_si... | @dataclass
class BaseModelOutput(ModelOutput):
'''
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer o... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.75 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 23 | 4 | 4 | 4 | 3 | 15 | 4 | 4 | 3 | 0 | 1 | 0 | 0 |
446 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.BaseModelOutputWithCrossAttentions | from dataclasses import dataclass
from .utils import ModelOutput
import torch
from typing import Optional
@dataclass
class BaseModelOutputWithCrossAttentions(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` ... | @dataclass
class BaseModelOutputWithCrossAttentions(ModelOutput):
'''
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output ... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 30 | 5 | 5 | 5 | 4 | 20 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
447 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.BaseModelOutputWithNoAttention | from .utils import ModelOutput
from dataclasses import dataclass
from typing import Optional
import torch
@dataclass
class BaseModelOutputWithNoAttention(ModelOutput):
"""
Base class for model's outputs, with potential hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_si... | @dataclass
class BaseModelOutputWithNoAttention(ModelOutput):
'''
Base class for model's outputs, with potential hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Sequence of hidden-states at the output of the last layer of... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.33 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 3 | 3 | 3 | 2 | 10 | 3 | 3 | 2 | 0 | 1 | 0 | 0 |
448 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.BaseModelOutputWithPast | from typing import Optional
from .cache_utils import Cache, EncoderDecoderCache
from .utils import ModelOutput
import torch
from dataclasses import dataclass
@dataclass
class BaseModelOutputWithPast(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential... | @dataclass
class BaseModelOutputWithPast(ModelOutput):
'''
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-sta... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 | 6 | 5 | 5 | 4 | 25 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
449 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions | from dataclasses import dataclass
from .cache_utils import Cache, EncoderDecoderCache
from .utils import ModelOutput
from typing import Optional
import torch
@dataclass
class BaseModelOutputWithPastAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to s... | @dataclass
class BaseModelOutputWithPastAndCrossAttentions(ModelOutput):
'''
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequ... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 43 | 7 | 6 | 6 | 5 | 30 | 6 | 6 | 5 | 0 | 1 | 0 | 0 |
450 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.BaseModelOutputWithPooling | from typing import Optional
from dataclasses import dataclass
import torch
from .utils import ModelOutput
@dataclass
class BaseModelOutputWithPooling(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor... | @dataclass
class BaseModelOutputWithPooling(ModelOutput):
'''
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the outpu... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 29 | 4 | 5 | 5 | 4 | 20 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
451 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions | from .cache_utils import Cache, EncoderDecoderCache
from typing import Optional
from dataclasses import dataclass
import torch
from .utils import ModelOutput
@dataclass
class BaseModelOutputWithPoolingAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last h... | @dataclass
class BaseModelOutputWithPoolingAndCrossAttentions(ModelOutput):
'''
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-s... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 4.71 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 46 | 6 | 7 | 7 | 6 | 33 | 7 | 7 | 6 | 0 | 1 | 0 | 0 |
452 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention | from dataclasses import dataclass
from .utils import ModelOutput
from typing import Optional
import torch
@dataclass
class BaseModelOutputWithPoolingAndNoAttention(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`tor... | @dataclass
class BaseModelOutputWithPoolingAndNoAttention(ModelOutput):
'''
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Sequence of hidden-states... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 | 3 | 4 | 4 | 3 | 12 | 4 | 4 | 3 | 0 | 1 | 0 | 0 |
453 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.BaseModelOutputWithPoolingAndProjection | from .utils import ModelOutput
from typing import Optional
from dataclasses import dataclass
import torch
@dataclass
class BaseModelOutputWithPoolingAndProjection(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torc... | @dataclass
class BaseModelOutputWithPoolingAndProjection(ModelOutput):
'''
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.83 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 34 | 5 | 6 | 6 | 5 | 23 | 6 | 6 | 5 | 0 | 1 | 0 | 0 |
454 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.CausalLMOutput | from typing import Optional
from dataclasses import dataclass
from .utils import ModelOutput
import torch
@dataclass
class CausalLMOutput(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `... | @dataclass
class CausalLMOutput(ModelOutput):
'''
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 | 4 | 5 | 5 | 4 | 17 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
455 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.CausalLMOutputWithCrossAttentions | from .utils import ModelOutput
from .cache_utils import Cache, EncoderDecoderCache
from dataclasses import dataclass
import torch
from typing import Optional
@dataclass
class CausalLMOutputWithCrossAttentions(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
... | @dataclass
class CausalLMOutputWithCrossAttentions(ModelOutput):
'''
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41 | 6 | 7 | 7 | 6 | 28 | 7 | 7 | 6 | 0 | 1 | 0 | 0 |
456 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.CausalLMOutputWithPast | from .utils import ModelOutput
import torch
from typing import Optional
from .cache_utils import Cache, EncoderDecoderCache
from dataclasses import dataclass
@dataclass
class CausalLMOutputWithPast(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`tor... | @dataclass
class CausalLMOutputWithPast(ModelOutput):
'''
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.67 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 33 | 5 | 6 | 6 | 5 | 22 | 6 | 6 | 5 | 0 | 1 | 0 | 0 |
457 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.DepthEstimatorOutput | import torch
from typing import Optional
from dataclasses import dataclass
from .utils import ModelOutput
@dataclass
class DepthEstimatorOutput(ModelOutput):
"""
Base class for outputs of depth estimation models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` i... | @dataclass
class DepthEstimatorOutput(ModelOutput):
'''
Base class for outputs of depth estimation models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
predicted_dept... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 | 5 | 5 | 5 | 4 | 17 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
458 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.ImageClassifierOutput | import torch
from .utils import ModelOutput
from typing import Optional
from dataclasses import dataclass
@dataclass
class ImageClassifierOutput(ModelOutput):
"""
Base class for outputs of image classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labe... | @dataclass
class ImageClassifierOutput(ModelOutput):
'''
Base class for outputs of image classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.4 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 25 | 3 | 5 | 5 | 4 | 17 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
459 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.ImageClassifierOutputWithNoAttention | from typing import Optional
from dataclasses import dataclass
import torch
from .utils import ModelOutput
@dataclass
class ImageClassifierOutputWithNoAttention(ModelOutput):
"""
Base class for outputs of image classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, retu... | @dataclass
class ImageClassifierOutputWithNoAttention(ModelOutput):
'''
Base class for outputs of image classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 2 | 4 | 4 | 3 | 12 | 4 | 4 | 3 | 0 | 1 | 0 | 0 |
460 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.ImageSuperResolutionOutput | from dataclasses import dataclass
import torch
from .utils import ModelOutput
from typing import Optional
@dataclass
class ImageSuperResolutionOutput(ModelOutput):
"""
Base class for outputs of image super resolution models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned whe... | @dataclass
class ImageSuperResolutionOutput(ModelOutput):
'''
Base class for outputs of image super resolution models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Reconstruction loss.
reconstruction (`torch.FloatTensor` of sha... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 | 3 | 5 | 5 | 4 | 17 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
461 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.MaskedImageModelingOutput | import torch
import warnings
from dataclasses import dataclass
from typing import Optional
from .utils import ModelOutput
@dataclass
class MaskedImageModelingOutput(ModelOutput):
"""
Base class for outputs of masked image completion / in-painting models.
Args:
loss (`torch.FloatTensor` of shape `(... | @dataclass
class MaskedImageModelingOutput(ModelOutput):
'''
Base class for outputs of masked image completion / in-painting models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
Reconstruction loss.
reconstruction (`to... | 4 | 1 | 7 | 0 | 7 | 0 | 1 | 1.38 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 34 | 3 | 13 | 7 | 10 | 18 | 8 | 6 | 6 | 1 | 1 | 0 | 1 |
462 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.MaskedLMOutput | from .utils import ModelOutput
from dataclasses import dataclass
from typing import Optional
import torch
@dataclass
class MaskedLMOutput(ModelOutput):
"""
Base class for masked language models outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided... | @dataclass
class MaskedLMOutput(ModelOutput):
'''
Base class for masked language models outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Masked language modeling (MLM) loss.
logits (`torch.FloatTensor` of shape `(batch_siz... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 | 4 | 5 | 5 | 4 | 17 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
463 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.MoECausalLMOutputWithPast | from .utils import ModelOutput
from dataclasses import dataclass
import torch
from typing import Optional
from .cache_utils import Cache, EncoderDecoderCache
@dataclass
class MoECausalLMOutputWithPast(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs as well as Mixture of Exper... | @dataclass
class MoECausalLMOutputWithPast(ModelOutput):
'''
Base class for causal language model (or autoregressive) outputs as well as Mixture of Expert's router hidden
states terms, to train a MoE model.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is pr... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.44 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 46 | 6 | 9 | 9 | 8 | 31 | 9 | 9 | 8 | 0 | 1 | 0 | 0 |
464 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.MoEModelOutput | from dataclasses import dataclass
import torch
from .utils import ModelOutput
from typing import Optional
@dataclass
class MoEModelOutput(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_siz... | @dataclass
class MoEModelOutput(ModelOutput):
'''
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 29 | 5 | 5 | 5 | 4 | 19 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
465 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.MoEModelOutputWithPastAndCrossAttentions | import torch
from typing import Optional
from .cache_utils import Cache, EncoderDecoderCache
from .utils import ModelOutput
from dataclasses import dataclass
@dataclass
class MoEModelOutputWithPastAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to sp... | @dataclass
class MoEModelOutputWithPastAndCrossAttentions(ModelOutput):
'''
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding) as well as
Mixture of Expert's router hidden states terms, to train a MoE model.
Args:
last_hidden_state (`torch.Fl... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 8 | 7 | 7 | 6 | 35 | 7 | 7 | 6 | 0 | 1 | 0 | 0 |
466 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.MoeCausalLMOutputWithPast | from dataclasses import dataclass
from typing import Optional
import torch
from .cache_utils import Cache, EncoderDecoderCache
from .utils import ModelOutput
@dataclass
class MoeCausalLMOutputWithPast(ModelOutput):
"""
Base class for causal language model (or autoregressive) with mixture of experts outputs.
... | @dataclass
class MoeCausalLMOutputWithPast(ModelOutput):
'''
Base class for causal language model (or autoregressive) with mixture of experts outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token ... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 46 | 10 | 8 | 8 | 7 | 28 | 8 | 8 | 7 | 0 | 1 | 0 | 0 |
467 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.MoeModelOutputWithPast | from typing import Optional
from dataclasses import dataclass
from .cache_utils import Cache, EncoderDecoderCache
from .utils import ModelOutput
import torch
@dataclass
class MoeModelOutputWithPast(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
... | @dataclass
class MoeModelOutputWithPast(ModelOutput):
'''
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last ... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 4.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 39 | 6 | 6 | 6 | 5 | 27 | 6 | 6 | 5 | 0 | 1 | 0 | 0 |
468 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.MultipleChoiceModelOutput | from dataclasses import dataclass
import torch
from typing import Optional
from .utils import ModelOutput
@dataclass
class MultipleChoiceModelOutput(ModelOutput):
"""
Base class for outputs of multiple choice models.
Args:
loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `label... | @dataclass
class MultipleChoiceModelOutput(ModelOutput):
'''
Base class for outputs of multiple choice models.
Args:
loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size,... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 5 | 5 | 5 | 4 | 18 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
469 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.NextSentencePredictorOutput | from typing import Optional
from .utils import ModelOutput
from dataclasses import dataclass
import torch
@dataclass
class NextSentencePredictorOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
loss (`torch.FloatTensor` of shape `(1... | @dataclass
class NextSentencePredictorOutput(ModelOutput):
'''
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `next_sentence_label` is provided):
Next sequence prediction (cla... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 | 4 | 5 | 5 | 4 | 18 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
470 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.QuestionAnsweringModelOutput | from dataclasses import dataclass
from .utils import ModelOutput
from typing import Optional
import torch
@dataclass
class QuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of question answering models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when ... | @dataclass
class QuestionAnsweringModelOutput(ModelOutput):
'''
Base class for outputs of question answering models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start an... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.17 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 29 | 4 | 6 | 6 | 5 | 19 | 6 | 6 | 5 | 0 | 1 | 0 | 0 |
471 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.SemanticSegmenterOutput | from typing import Optional
import torch
from .utils import ModelOutput
from dataclasses import dataclass
@dataclass
class SemanticSegmenterOutput(ModelOutput):
"""
Base class for outputs of semantic segmentation models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `l... | @dataclass
class SemanticSegmenterOutput(ModelOutput):
'''
Base class for outputs of semantic segmentation models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 4.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 35 | 8 | 5 | 5 | 4 | 22 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
472 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.Seq2SeqLMOutput | from .utils import ModelOutput
from .cache_utils import Cache, EncoderDecoderCache
from typing import Optional
import torch
from dataclasses import dataclass
@dataclass
class Seq2SeqLMOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs.
Args:
loss (`torch.FloatTens... | @dataclass
class Seq2SeqLMOutput(ModelOutput):
'''
Base class for sequence-to-sequence language models outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
logits (`torch.FloatTensor` of shape `(batch_s... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.9 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 8 | 10 | 10 | 9 | 39 | 10 | 10 | 9 | 0 | 1 | 0 | 0 |
473 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.Seq2SeqMoEModelOutput | import torch
from typing import Optional
from .utils import ModelOutput
from dataclasses import dataclass
from .cache_utils import Cache, EncoderDecoderCache
@dataclass
class Seq2SeqMoEModelOutput(ModelOutput):
"""
Base class for model encoder's outputs that also contains : pre-computed hidden states that can ... | @dataclass
class Seq2SeqMoEModelOutput(ModelOutput):
'''
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 4.27 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 69 | 11 | 11 | 11 | 10 | 47 | 11 | 11 | 10 | 0 | 1 | 0 | 0 |
474 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.Seq2SeqMoEOutput | from typing import Optional
from .cache_utils import Cache, EncoderDecoderCache
import torch
from dataclasses import dataclass
from .utils import ModelOutput
@dataclass
class Seq2SeqMoEOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs.
Args:
loss (`torch.FloatTen... | @dataclass
class Seq2SeqMoEOutput(ModelOutput):
'''
Base class for sequence-to-sequence language models outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
logits (`torch.FloatTensor` of shape `(batch_... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 2.88 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 72 | 10 | 16 | 16 | 15 | 46 | 16 | 16 | 15 | 0 | 1 | 0 | 0 |
475 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.Seq2SeqModelOutput | from typing import Optional
from .cache_utils import Cache, EncoderDecoderCache
import torch
from .utils import ModelOutput
from dataclasses import dataclass
@dataclass
class Seq2SeqModelOutput(ModelOutput):
"""
Base class for model encoder's outputs that also contains : pre-computed hidden states that can spe... | @dataclass
class Seq2SeqModelOutput(ModelOutput):
'''
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Seq... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 4.44 | 1 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 58 | 9 | 9 | 9 | 8 | 40 | 9 | 9 | 8 | 0 | 1 | 0 | 0 |
476 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput | from .cache_utils import Cache, EncoderDecoderCache
from .utils import ModelOutput
import torch
from dataclasses import dataclass
from typing import Optional
@dataclass
class Seq2SeqQuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of sequence-to-sequence question answering models.
Arg... | @dataclass
class Seq2SeqQuestionAnsweringModelOutput(ModelOutput):
'''
Base class for outputs of sequence-to-sequence question answering models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cr... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.73 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 60 | 8 | 11 | 11 | 10 | 41 | 11 | 11 | 10 | 0 | 1 | 0 | 0 |
477 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput | from typing import Optional
from dataclasses import dataclass
from .cache_utils import Cache, EncoderDecoderCache
from .utils import ModelOutput
import torch
@dataclass
class Seq2SeqSequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sequence-to-sequence sentence classification models.
Ar... | @dataclass
class Seq2SeqSequenceClassifierOutput(ModelOutput):
'''
Base class for outputs of sequence-to-sequence sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided):
Classification (or regression if config.num_l... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.9 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 8 | 10 | 10 | 9 | 39 | 10 | 10 | 9 | 0 | 1 | 0 | 0 |
478 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.Seq2SeqSpectrogramOutput | import torch
from .cache_utils import Cache, EncoderDecoderCache
from typing import Optional
from .utils import ModelOutput
from dataclasses import dataclass
@dataclass
class Seq2SeqSpectrogramOutput(ModelOutput):
"""
Base class for sequence-to-sequence spectrogram outputs.
Args:
loss (`torch.Floa... | @dataclass
class Seq2SeqSpectrogramOutput(ModelOutput):
'''
Base class for sequence-to-sequence spectrogram outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Spectrogram generation loss.
spectrogram (`torch.FloatTensor` of ... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.9 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 8 | 10 | 10 | 9 | 39 | 10 | 10 | 9 | 0 | 1 | 0 | 0 |
479 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.Seq2SeqTSModelOutput | from dataclasses import dataclass
import torch
from typing import Optional
from .cache_utils import Cache, EncoderDecoderCache
from .utils import ModelOutput
@dataclass
class Seq2SeqTSModelOutput(ModelOutput):
"""
Base class for time series model's encoder outputs that also contains pre-computed hidden states ... | @dataclass
class Seq2SeqTSModelOutput(ModelOutput):
'''
Base class for time series model's encoder outputs that also contains pre-computed hidden states that can speed up
sequential decoding.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 69 | 9 | 12 | 12 | 11 | 48 | 12 | 12 | 11 | 0 | 1 | 0 | 0 |
480 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.Seq2SeqTSPredictionOutput | from dataclasses import dataclass
import torch
from .utils import ModelOutput
from typing import Optional
from .cache_utils import Cache, EncoderDecoderCache
@dataclass
class Seq2SeqTSPredictionOutput(ModelOutput):
"""
Base class for time series model's decoder outputs that also contain the loss as well as the... | @dataclass
class Seq2SeqTSPredictionOutput(ModelOutput):
'''
Base class for time series model's decoder outputs that also contain the loss as well as the parameters of the
chosen distribution.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when a `future_values` is provide... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.69 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 69 | 8 | 13 | 13 | 12 | 48 | 13 | 13 | 12 | 0 | 1 | 0 | 0 |
481 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.SequenceClassifierOutput | from dataclasses import dataclass
import torch
from typing import Optional
from .utils import ModelOutput
@dataclass
class SequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when... | @dataclass
class SequenceClassifierOutput(ModelOutput):
'''
Base class for outputs of sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
log... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 | 4 | 5 | 5 | 4 | 17 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
482 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.SequenceClassifierOutputWithPast | from typing import Optional
import torch
from .cache_utils import Cache, EncoderDecoderCache
from dataclasses import dataclass
from .utils import ModelOutput
@dataclass
class SequenceClassifierOutputWithPast(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`t... | @dataclass
class SequenceClassifierOutputWithPast(ModelOutput):
'''
Base class for outputs of sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.67 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 33 | 5 | 6 | 6 | 5 | 22 | 6 | 6 | 5 | 0 | 1 | 0 | 0 |
483 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.TokenClassifierOutput | from typing import Optional
from .utils import ModelOutput
from dataclasses import dataclass
import torch
@dataclass
class TokenClassifierOutput(ModelOutput):
"""
Base class for outputs of token classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labe... | @dataclass
class TokenClassifierOutput(ModelOutput):
'''
Base class for outputs of token classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_siz... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 | 4 | 5 | 5 | 4 | 17 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
484 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.Wav2Vec2BaseModelOutput | from .utils import ModelOutput
from typing import Optional
import torch
from dataclasses import dataclass
@dataclass
class Wav2Vec2BaseModelOutput(ModelOutput):
"""
Base class for models that have been trained with the Wav2Vec2 loss objective.
Args:
last_hidden_state (`torch.FloatTensor` of shape ... | @dataclass
class Wav2Vec2BaseModelOutput(ModelOutput):
'''
Base class for models that have been trained with the Wav2Vec2 loss objective.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the la... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 | 4 | 5 | 5 | 4 | 17 | 5 | 5 | 4 | 0 | 1 | 0 | 0 |
485 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_outputs.py | transformers.modeling_outputs.XVectorOutput | from typing import Optional
import torch
from dataclasses import dataclass
from .utils import ModelOutput
@dataclass
class XVectorOutput(ModelOutput):
"""
Output type of [`Wav2Vec2ForXVector`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
... | @dataclass
class XVectorOutput(ModelOutput):
'''
Output type of [`Wav2Vec2ForXVector`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.17 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 29 | 4 | 6 | 6 | 5 | 19 | 6 | 6 | 5 | 0 | 1 | 0 | 0 |
486 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_utils.py | transformers.modeling_utils.ModuleUtilsMixin | from torch import Tensor, nn
from .utils import ADAPTER_SAFE_WEIGHTS_NAME, ADAPTER_WEIGHTS_NAME, CONFIG_NAME, DUMMY_INPUTS, SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME, ContextManagers, PushToHubMixin, cached_file, check_torch_load_is_safe, copy_func, download_url, extract_commit_hash, ... |
class ModuleUtilsMixin:
'''
A few utilities for `torch.nn.Modules`, to be used as a mixin.
'''
@staticmethod
def _hook_rss_memory_pre_forward(module, *args, **kwargs):
pass
@staticmethod
def _hook_rss_memory_post_forward(module, *args, **kwargs):
pass
def add_memory_hoo... | 20 | 12 | 20 | 2 | 11 | 7 | 3 | 0.65 | 0 | 11 | 0 | 2 | 11 | 2 | 14 | 14 | 309 | 44 | 163 | 52 | 134 | 106 | 117 | 39 | 99 | 9 | 0 | 4 | 44 |
487 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/modeling_utils.py | transformers.modeling_utils.PreTrainedModel | from .loss.loss_utils import LOSS_MAPPING
from torch.utils.checkpoint import checkpoint
import sys
from torch.distributions import constraints
import shutil
import re
from torch import Tensor, nn
import os
from .integrations.tensor_parallel import _get_parameter_tp_plan, distribute_model, initialize_tensor_parallelism,... |
class PreTrainedModel(nn.Module, EmbeddingAccessMixin, ModuleUtilsMixin, PushToHubMixin, PeftAdapterMixin):
'''
Base class for all models.
[`PreTrainedModel`] takes care of storing the configuration of the models and handles methods for loading,
downloading and saving models as well as a few methods co... | 125 | 54 | 56 | 6 | 37 | 12 | 8 | 0.35 | 5 | 30 | 6 | 31 | 51 | 13 | 67 | 129 | 3,998 | 507 | 2,590 | 572 | 2,354 | 913 | 1,476 | 423 | 1,384 | 146 | 1 | 7 | 574 |
488 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/albert/configuration_albert.py | transformers.models.albert.configuration_albert.AlbertConfig | from ...configuration_utils import PretrainedConfig
class AlbertConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`AlbertModel`] or a [`TFAlbertModel`]. It is used
to instantiate an ALBERT model according to the specified arguments, defining the model architectu... |
class AlbertConfig(PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`AlbertModel`] or a [`TFAlbertModel`]. It is used
to instantiate an ALBERT model according to the specified arguments, defining the model architecture. Instantiating
a configuration with the defau... | 2 | 1 | 43 | 1 | 42 | 0 | 1 | 1.61 | 1 | 1 | 0 | 0 | 1 | 17 | 1 | 1 | 126 | 11 | 44 | 43 | 19 | 71 | 21 | 20 | 19 | 1 | 1 | 0 | 1 |
489 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/albert/configuration_albert.py | transformers.models.albert.configuration_albert.AlbertOnnxConfig | from collections import OrderedDict
from collections.abc import Mapping
from ...onnx import OnnxConfig
class AlbertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == 'multiple-choice':
dynamic_axis = {0: 'batch', 1: 'choice', 2: 'sequence... |
class AlbertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
pass | 3 | 0 | 12 | 0 | 12 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 1 | 0 | 1 | 1 | 14 | 0 | 14 | 4 | 11 | 0 | 6 | 3 | 4 | 2 | 1 | 1 | 2 |
490 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/albert/modeling_albert.py | transformers.models.albert.modeling_albert.AlbertAttention | from ...processing_utils import Unpack
from .configuration_albert import AlbertConfig
from torch import nn
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
import torch
from typing import Callable, Optional, Union
from ...modeling_utils import ALL_ATTENTION_FU... |
class AlbertAttention(nn.Module):
def __init__(self, config: AlbertConfig):
pass
def prune_heads(self, heads: list[int]) -> None:
pass
def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor]=None, head_mask: Optional[torch.FloatTensor]=None, **kwargs: U... | 4 | 0 | 26 | 4 | 20 | 2 | 3 | 0.12 | 1 | 7 | 1 | 1 | 4 | 15 | 4 | 14 | 110 | 19 | 82 | 48 | 71 | 10 | 70 | 42 | 65 | 7 | 1 | 2 | 13 |
491 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/albert/modeling_albert.py | transformers.models.albert.modeling_albert.AlbertEmbeddings | import torch
from typing import Callable, Optional, Union
from torch import nn
from .configuration_albert import AlbertConfig
class AlbertEmbeddings(nn.Module):
"""
Construct the embeddings from word, position and token_type embeddings.
"""
def __init__(self, config: AlbertConfig):
super().__i... |
class AlbertEmbeddings(nn.Module):
'''
Construct the embeddings from word, position and token_type embeddings.
'''
def __init__(self, config: AlbertConfig):
pass
def forward(self, input_ids: Optional[torch.LongTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Op... | 3 | 1 | 30 | 4 | 23 | 3 | 4 | 0.21 | 1 | 4 | 1 | 0 | 2 | 6 | 2 | 12 | 66 | 9 | 47 | 23 | 37 | 10 | 34 | 16 | 31 | 7 | 1 | 2 | 8 |
492 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/albert/modeling_albert.py | transformers.models.albert.modeling_albert.AlbertForMaskedLM | from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
import torch
from ...utils.generic ... | @auto_docstring
class AlbertForMaskedLM(AlbertPreTrainedModel):
def __init__(self, config):
pass
def get_output_embeddings(self) -> nn.Linear:
pass
def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
pass
def get_input_embeddings(self) -> nn.Embedding:
... | 9 | 1 | 19 | 3 | 10 | 6 | 2 | 0.55 | 2 | 5 | 3 | 0 | 5 | 2 | 5 | 6 | 106 | 19 | 56 | 28 | 36 | 31 | 27 | 15 | 21 | 5 | 2 | 1 | 9 |
493 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/albert/modeling_albert.py | transformers.models.albert.modeling_albert.AlbertForMultipleChoice | from torch import nn
import torch
from typing import Callable, Optional, Union
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
from ...processing_utils import Unpack
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, M... | @auto_docstring
class AlbertForMultipleChoice(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
pass
@can_return_tuple
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.L... | 6 | 1 | 37 | 4 | 29 | 4 | 6 | 0.11 | 1 | 7 | 4 | 0 | 2 | 3 | 2 | 3 | 81 | 9 | 65 | 27 | 44 | 7 | 28 | 14 | 25 | 11 | 2 | 1 | 12 |
494 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/albert/modeling_albert.py | transformers.models.albert.modeling_albert.AlbertForPreTraining | from typing import Callable, Optional, Union
from ...utils.generic import can_return_tuple, check_model_inputs
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...processing_utils import Unpack
from torch import nn
import torch
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, ... | @auto_docstring(custom_intro='\n Albert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a\n `sentence order prediction (classification)` head.\n ')
class AlbertForPreTraining(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
pass
... | 9 | 1 | 19 | 3 | 11 | 5 | 2 | 0.38 | 1 | 7 | 5 | 0 | 5 | 3 | 5 | 6 | 103 | 19 | 61 | 33 | 40 | 23 | 30 | 19 | 24 | 5 | 2 | 1 | 9 |
495 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/albert/modeling_albert.py | transformers.models.albert.modeling_albert.AlbertForPreTrainingOutput | from ...utils import ModelOutput, TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
import torch
from typing import Callable, Optional, Union
from dataclasses import dataclass
@dataclass
@auto_docstring(custom_intro='\n Output type of [`AlbertForPreTraining`].\n ')
class AlbertForPreTrain... | @dataclass
@auto_docstring(custom_intro='\n Output type of [`AlbertForPreTraining`].\n ')
class AlbertForPreTrainingOutput(ModelOutput):
'''
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and th... | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 3.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 4 | 6 | 6 | 5 | 21 | 6 | 6 | 5 | 0 | 1 | 0 | 0 |
496 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/albert/modeling_albert.py | transformers.models.albert.modeling_albert.AlbertForQuestionAnswering | from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput
from .configuration_albert import AlbertConfig
from torch import nn
from ...utils import ModelOutput, TransformersKwargs, ... | @auto_docstring
class AlbertForQuestionAnswering(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
pass
@can_return_tuple
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torc... | 6 | 0 | 41 | 5 | 30 | 7 | 4 | 0.18 | 1 | 7 | 4 | 0 | 2 | 3 | 2 | 3 | 94 | 10 | 71 | 30 | 45 | 13 | 32 | 16 | 29 | 7 | 2 | 2 | 8 |
497 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/albert/modeling_albert.py | transformers.models.albert.modeling_albert.AlbertForSequenceClassification | from .configuration_albert import AlbertConfig
from ...utils.generic import can_return_tuple, check_model_inputs
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput
from torc... | @auto_docstring(custom_intro='\n Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled\n output) e.g. for GLUE tasks.\n ')
class AlbertForSequenceClassification(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
pass
... | 6 | 1 | 41 | 5 | 33 | 4 | 7 | 0.09 | 1 | 6 | 3 | 0 | 2 | 5 | 2 | 3 | 92 | 10 | 75 | 27 | 52 | 7 | 35 | 14 | 32 | 12 | 2 | 3 | 13 |
498 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/albert/modeling_albert.py | transformers.models.albert.modeling_albert.AlbertForTokenClassification | from .configuration_albert import AlbertConfig
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
from ...processing_utils import Unpack
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...utils.generic import can_return_tuple, check_model_i... | @auto_docstring
class AlbertForTokenClassification(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
pass
@can_return_tuple
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[to... | 6 | 1 | 33 | 4 | 27 | 3 | 4 | 0.08 | 1 | 5 | 3 | 0 | 2 | 4 | 2 | 3 | 74 | 9 | 60 | 27 | 39 | 5 | 23 | 14 | 20 | 5 | 2 | 1 | 7 |
499 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/albert/modeling_albert.py | transformers.models.albert.modeling_albert.AlbertLayer | from ...activations import ACT2FN
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
import torch
from torch import nn
from ...processing_utils import Unp... |
class AlbertLayer(nn.Module):
def __init__(self, config: AlbertConfig):
pass
def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor]=None, head_mask: Optional[torch.FloatTensor]=None, **kwargs: Unpack[TransformersKwargs]) -> tuple[torch.Tensor, torch.Tensor]:
... | 4 | 0 | 12 | 1 | 11 | 0 | 1 | 0.03 | 1 | 4 | 1 | 0 | 3 | 9 | 3 | 13 | 39 | 5 | 34 | 23 | 23 | 1 | 22 | 16 | 18 | 1 | 1 | 0 | 3 |
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