Types & classes — github.com/cedrickchee/transformers-llama

hub / github.com/cedrickchee/transformers-llama / types & classes

Types & classes9,398 in github.com/cedrickchee/transformers-llama

⨍ Functions 36,568◇ Types & classes 9,398↳ Endpoints 539

↓ 835 callersClassOptionalDependencyNotAvailable

Internally used error class for signalling an optional dependency was not found.

src/transformers/utils/import_utils.py:1135

↓ 479 callersClassANY

tests/pipelines/test_pipelines_common.py:70

↓ 276 callersClassConfigTester

tests/test_configuration_common.py:95

↓ 224 callersClassAddedToken

AddedToken represents a token to be added to a Tokenizer An AddedToken can have special options defining the way it should behave.

src/transformers/tokenization_utils_base.py:79

↓ 191 callersClass_LazyModule

Module class that surfaces all objects but only performs associated imports when the objects are requested.

src/transformers/utils/import_utils.py:1075

↓ 146 callersClassBaseModelOutput

Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`torch.FloatTensor` of shape `

src/transformers/modeling_outputs.py:24

↓ 98 callersClassHfArgumentParser

This subclass of `argparse.ArgumentParser` uses type hints on dataclasses to generate arguments. The class is designed to play well with the

src/transformers/hf_argparser.py:116

↓ 80 callersClassBatchEncoding

Holds the output of the [`~tokenization_utils_base.PreTrainedTokenizerBase.__call__`], [`~tokenization_utils_base.PreTrainedTokenizerBase.enc

src/transformers/tokenization_utils_base.py:163

↓ 78 callersClassBatchFeature

r""" Holds the output of the image processor specific `__call__` methods. This class is derived from a python dictionary and can be used as a

src/transformers/image_processing_utils.py:42

↓ 76 callersClassTFBaseModelOutput

Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`tf.Tensor` of shape `(batch_s

src/transformers/modeling_tf_outputs.py:24

↓ 70 callersClass_LazyAutoMapping

" A mapping config to object (model or tokenizer for instance) that will load keys and values when it is accessed. Args: - config_ma

src/transformers/models/auto/auto_factory.py:576

↓ 68 callersClassBaseModelOutputWithPastAndCrossAttentions

Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`

src/transformers/modeling_outputs.py:244

↓ 66 callersClassSequenceClassifierOutput

Base class for outputs of sentence classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when

src/transformers/modeling_outputs.py:896

↓ 60 callersClassTFSequenceClassifierOutput

Base class for outputs of sentence classification models. Args: loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned wh

src/transformers/modeling_tf_outputs.py:571

↓ 60 callersClassTrainer

Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. Args: model ([`PreTrained

src/transformers/trainer.py:221

↓ 49 callersClassTokenClassifierOutput

Base class for outputs of token classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `l

src/transformers/modeling_outputs.py:1016

↓ 46 callersClassBasicTokenizer

Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). Args: do_lower_case (`bool

src/transformers/models/bert/tokenization_bert.py:370

↓ 45 callersClassMaskedLMOutput

Base class for masked language models outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` i

src/transformers/modeling_outputs.py:702

↓ 45 callersClassQuestionAnsweringModelOutput

Base class for outputs of question answering models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `lab

src/transformers/modeling_outputs.py:1045

↓ 44 callersClassTFQuestionAnsweringModelOutput

Base class for outputs of question answering models. Args: loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `s

src/transformers/modeling_tf_outputs.py:811

↓ 44 callersClassTFTokenClassifierOutput

Base class for outputs of token classification models. Args: loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of

src/transformers/modeling_tf_outputs.py:782

↓ 42 callersClassTFMaskedLMOutput

Base class for masked language models outputs. Args: loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-mas

src/transformers/modeling_tf_outputs.py:453

↓ 41 callersClassTrainingArguments

TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop itself**. Using [`HfA

src/transformers/training_args.py:148

↓ 37 callersClassBaseModelOutputWithPooling

Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`torch.FloatTensor`

src/transformers/modeling_outputs.py:69

↓ 37 callersClassCaptureLogger

Context manager to capture `logging` streams Args: logger: 'logging` logger object Returns: The captured output is avai

src/transformers/testing_utils.py:940

↓ 36 callersClassCausalLMOutputWithCrossAttentions

Base class for causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, retur

src/transformers/modeling_outputs.py:622

↓ 35 callersClassMultipleChoiceModelOutput

Base class for outputs of multiple choice models. Args: loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels

src/transformers/modeling_outputs.py:985

↓ 34 callersClassTFMultipleChoiceModelOutput

Base class for outputs of multiple choice models. Args: loss (`tf.Tensor` of shape *(batch_size, )*, *optional*, returned when `labe

src/transformers/modeling_tf_outputs.py:751

↓ 33 callersClassFlaxBaseModelOutput

Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`jnp.ndarray` of shape `(batch

src/transformers/modeling_flax_outputs.py:23

↓ 31 callersClassDataCollatorForLanguageModeling

Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they are not all of the same leng

src/transformers/data/data_collator.py:607

↓ 30 callersClassFlaxBaseModelOutputWithPastAndCrossAttentions

Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`

src/transformers/modeling_flax_outputs.py:159

↓ 29 callersClassConversation

Utility class containing a conversation and its history. This class is meant to be used as an input to the [`ConversationalPipeline`]. The co

src/transformers/pipelines/conversational.py:18

↓ 29 callersClassDataCollatorWithPadding

Data collator that will dynamically pad the inputs received. Args: tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]

src/transformers/data/data_collator.py:215

↓ 29 callersClassTFBaseModelOutputWithPastAndCrossAttentions

Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`

src/transformers/modeling_tf_outputs.py:242

↓ 28 callersClassBaseModelOutputWithPoolingAndCrossAttentions

Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`torch.FloatTensor`

src/transformers/modeling_outputs.py:195

↓ 28 callersClassBertTokenizer

r""" Construct a BERT tokenizer. Based on WordPiece. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main met

src/transformers/models/bert/tokenization_bert.py:137

↓ 28 callersClassEsmFoldLinear

A Linear layer with built-in nonstandard initializations. Called just like torch.nn.Linear. Implements the initializers in 1.11.4, plus some

src/transformers/models/esm/modeling_esmfold.py:267

↓ 28 callersClassRotation

A 3D rotation. Depending on how the object is initialized, the rotation is represented by either a rotation matrix or a quaternion, though bo

src/transformers/models/esm/openfold_utils/rigid_utils.py:252

↓ 26 callersClassAlmostAccuracy

tests/trainer/test_trainer.py:166

↓ 26 callersClassTFBaseModelOutputWithPooling

Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`tf.Tensor` of shape

src/transformers/modeling_tf_outputs.py:69

↓ 25 callersClassAdamW

Implements Adam algorithm with weight decay fix as introduced in [Decoupled Weight Decay Regularization](https://arxiv.org/abs/1711.05101).

src/transformers/optimization.py:358

↓ 25 callersClassTFSeq2SeqLMOutput

Base class for sequence-to-sequence language models outputs. Args: loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the num

src/transformers/modeling_tf_outputs.py:482

↓ 23 callersClassContextManagers

Wrapper for `contextlib.ExitStack` which enters a collection of context managers. Adaptation of `ContextManagers` in the `fastcore` library.

src/transformers/utils/generic.py:344

↓ 23 callersClassSeq2SeqLMOutput

Base class for sequence-to-sequence language models outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned w

src/transformers/modeling_outputs.py:731

↓ 22 callersClassConv1D

src/transformers/utils/dummy_pt_objects.py:7370

↓ 21 callersClassInputExample

A single training/test example for simple sequence classification. Args: guid: Unique id for the example. text_a: string. Th

src/transformers/data/processors/utils.py:30

↓ 21 callersClassRagRetriever

Retriever used to get documents from vector queries. It retrieves the documents embeddings as well as the documents contents, and it formats

src/transformers/models/rag/retrieval_rag.py:323

↓ 20 callersClassBartConfig

r""" This is the configuration class to store the configuration of a [`BartModel`]. It is used to instantiate a BART model according to the sp

src/transformers/models/bart/configuration_bart.py:35

↓ 20 callersClassFlaxCausalLMOutputWithCrossAttentions

Base class for causal language model (or autoregressive) outputs. Args: logits (`jnp.ndarray` of shape `(batch_size, sequence_length

src/transformers/modeling_flax_outputs.py:266

↓ 20 callersClassTFSeq2SeqModelOutput

Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential decoding. Args:

src/transformers/modeling_tf_outputs.py:285

↓ 19 callersClassDataCollatorForTokenClassification

Data collator that will dynamically pad the inputs received, as well as the labels. Args: tokenizer ([`PreTrainedTokenizer`] or [`Pr

src/transformers/data/data_collator.py:266

↓ 19 callersClassRegressionDataset

tests/trainer/test_trainer.py:109

↓ 19 callersClassSeq2SeqModelOutput

Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential decoding. Args:

src/transformers/modeling_outputs.py:424

↓ 18 callersClassEncoderDecoderModel

src/transformers/utils/dummy_pt_objects.py:2452

↓ 18 callersClassLogitsProcessorList

This class can be used to create a list of [`LogitsProcessor`] or [`LogitsWarper`] to subsequently process a `scores` input tensor. This clas

src/transformers/generation/logits_process.py:73

↓ 18 callersClassModelPatterns

Holds the basic information about a new model for the add-new-model-like command. Args: model_name (`str`): The model name.

src/transformers/commands/add_new_model_like.py:41

↓ 18 callersClassQuantAct

Quantizes the given activation. Args: activation_bit (`int`): Bitwidth for the quantized activation. act_range_m

src/transformers/models/ibert/quant_modules.py:114

↓ 18 callersClassRigid

A class representing a rigid transformation. Little more than a wrapper around two objects: a Rotation object and a [*, 3] translation Design

src/transformers/models/esm/openfold_utils/rigid_utils.py:729

↓ 18 callersClassStoppingCriteriaList

src/transformers/generation/stopping_criteria.py:110

↓ 18 callersClassTFCausalLMOutputWithCrossAttentions

Base class for causal language model (or autoregressive) outputs. Args: loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is th

src/transformers/modeling_tf_outputs.py:410

↓ 16 callersClassBertConfig

r""" This is the configuration class to store the configuration of a [`BertModel`] or a [`TFBertModel`]. It is used to instantiate a BERT mode

src/transformers/models/bert/configuration_bert.py:72

↓ 16 callersClassBertModel

src/transformers/utils/dummy_pt_objects.py:911

↓ 16 callersClassTFBaseModelOutputWithPoolingAndCrossAttentions

Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`tf.Tensor` of shape

src/transformers/modeling_tf_outputs.py:125

↓ 16 callersClassWav2Vec2Processor

r""" Constructs a Wav2Vec2 processor which wraps a Wav2Vec2 feature extractor and a Wav2Vec2 CTC tokenizer into a single processor. [`Wav

src/transformers/models/wav2vec2/processing_wav2vec2.py:26

↓ 15 callersClassAttention

Multi-headed attention from 'Attention Is All You Need' paper

src/transformers/models/fsmt/modeling_fsmt.py:856

↓ 15 callersClassImageFeatureExtractionMixin

Mixin that contain utilities for preparing image features.

src/transformers/image_utils.py:287

↓ 14 callersClassEvalPrediction

Evaluation output (always contains labels), to be used to compute metrics. Parameters: predictions (`np.ndarray`): Predictions of th

src/transformers/trainer_utils.py:100

↓ 14 callersClassIndexMap

Index grouping entries within a tensor.

src/transformers/models/tapas/modeling_tf_tapas.py:1715

↓ 14 callersClassIndexMap

Index grouping entries within a tensor.

src/transformers/models/tapas/modeling_tapas.py:1584

↓ 14 callersClassMemory

`Memory` NamedTuple have a single field `bytes` and you can get a human readable str of the number of mega bytes by calling `__repr__`

src/transformers/benchmark/benchmark_utils.py:151

↓ 14 callersClassMobileViTConvLayer

src/transformers/models/mobilevit/modeling_mobilevit.py:88

↓ 14 callersClassModelOutputTest

tests/utils/test_model_output.py:24

↓ 14 callersClassPyTorchBenchmark

src/transformers/benchmark/benchmark.py:50

↓ 14 callersClassRegressionModelConfig

tests/trainer/test_trainer.py:176

↓ 14 callersClassTFMobileViTConvLayer

src/transformers/models/mobilevit/modeling_tf_mobilevit.py:82

↓ 13 callersClassBertModelTester

tests/models/bert/test_modeling_bert.py:47

↓ 13 callersClassDPRConfig

r""" [`DPRConfig`] is the configuration class to store the configuration of a *DPRModel*. This is the configuration class to store the config

src/transformers/models/dpr/configuration_dpr.py:45

↓ 13 callersClassFlaxSeq2SeqLMOutput

Base class for sequence-to-sequence language models outputs. Args: logits (`jnp.ndarray` of shape `(batch_size, sequence_length, con

src/transformers/modeling_flax_outputs.py:336

↓ 13 callersClassPretrainedConfig

r""" Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as methods for loading/do

src/transformers/configuration_utils.py:48

↓ 13 callersClassPyTorchBenchmarkArguments

src/transformers/utils/dummy_pt_objects.py:12

↓ 13 callersClassVisionEncoderDecoderModel

src/transformers/utils/dummy_pt_objects.py:6479

↓ 12 callersClassBaseModelOutputWithNoAttention

Base class for model's outputs, with potential hidden states. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, nu

src/transformers/modeling_outputs.py:50

↓ 12 callersClassBertModel

The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added betwee

src/transformers/models/bert/modeling_bert.py:871

↓ 12 callersClassFlaxSequenceClassifierOutput

Base class for outputs of sentence classification models. Args: logits (`jnp.ndarray` of shape `(batch_size, config.num_labels)`):

src/transformers/modeling_flax_outputs.py:420

↓ 12 callersClassImageClassifierOutputWithNoAttention

Base class for outputs of image classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `l

src/transformers/modeling_outputs.py:1206

↓ 12 callersClassMobileNetV2ConvLayer

src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py:288

↓ 12 callersClassSequenceSummary

r""" Compute a single vector summary of a sequence hidden states. Args: config ([`PretrainedConfig`]): The config used by

src/transformers/modeling_utils.py:3447

↓ 12 callersClassTFEncoderDecoderModel

src/transformers/utils/dummy_tf_objects.py:1151

↓ 12 callersClassTensorFlowBenchmark

src/transformers/utils/dummy_tf_objects.py:12

↓ 12 callersClassWav2Vec2FeatureExtractor

src/transformers/models/wav2vec2/modeling_wav2vec2.py:459

↓ 11 callersClassBaseModelOutputWithPast

Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`

src/transformers/modeling_outputs.py:123

↓ 11 callersClassBaseModelOutputWithPoolingAndNoAttention

Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`torch.FloatTensor`

src/transformers/modeling_outputs.py:101

↓ 11 callersClassBeamSearchScorer

r""" [`BeamScorer`] implementing standard beam search decoding. Adapted in part from [Facebook's XLM beam search code](https://github.com

src/transformers/generation/beam_search.py:119

↓ 11 callersClassCausalLMOutput

Base class for causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, retur

src/transformers/modeling_outputs.py:557

↓ 11 callersClassCompose

examples/pytorch/semantic-segmentation/run_semantic_segmentation.py:68

↓ 11 callersClassFlaxMaskedLMOutput

Base class for masked language models outputs. Args: logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size

src/transformers/modeling_flax_outputs.py:307

↓ 11 callersClassNormalize

examples/pytorch/semantic-segmentation/run_semantic_segmentation.py:150

↓ 11 callersClassPushToHubCallback

Callback that will save and push the model to the Hub regularly. By default, it pushes once per epoch, but this can be changed with the `save

src/transformers/keras_callbacks.py:268

↓ 11 callersClassReformerConfig

r""" This is the configuration class to store the configuration of a [`ReformerModel`]. It is used to instantiate a Reformer model according t

src/transformers/models/reformer/configuration_reformer.py:32

↓ 11 callersClassRegressionModel

tests/trainer/test_trainer.py:226

next →1–100 of 9,398, ranked by callers