This code should preferably be run on Google Colab TPU runtime. The latest model from Nvidia has 8.3 billion parameters: 24 times larger than BERT-large, 5 times larger than GPT-2, while RoBERTa, the latest work from Facebook AI, was trained on 160GB of text . the latter silently ignores them. attention_dropout = 0.1 output. However, Transformer models only accept tensors as input. In some variants, the task is multiple-choice: A list of possible answers are supplied with each question, and the model simply needs decoder: BeamSearchDecoderCTC ", "You can do it from another script, save it, and load it from here, using --tokenizer_name. According to the abstract, transformers.modeling_flax_outputs.FlaxTokenClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxTokenClassifierOutput or tuple(torch.FloatTensor). output_char_offsets: bool = False Batch decode output logits to audio transcription with language model support. Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and vocab_size (int, optional, defaults to 30522) Vocabulary size of the LayoutLM model.Defines the different tokens that can be represented by the inputs_ids passed to the forward method of LayoutLMModel. projected_states (torch.FloatTensor of shape (batch_size, sequence_length, config.proj_codevector_dim)) Hidden-states of the model projected to config.proj_codevector_dim that can be used to predict the masked vocab_file return_dict: typing.Optional[bool] = None Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see ", "{len(eval_squad_examples)} evaluation points created. and get access to the augmented documentation experience. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) Sequence of hidden-states at the output of the last layer of the model. output_attentions: typing.Optional[bool] = None A transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput or a tuple of minGPT tries to be small, clean, interpretable and educational, as most of the currently available GPT model implementations can a bit sprawling.GPT is not a complicated model and this implementation is appropriately about 300 lines of code (see mingpt/model.py).All that's going on is that a Indices can be obtained using BertTokenizer. output_attentions: typing.Optional[bool] = None # information sent is the one passed as arguments along with your Python/PyTorch versions. ; a path to a directory output_attentions: typing.Optional[bool] = None attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None token_type_ids List of token type ids to be fed to a model (when return_token_type_ids=True or as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and This model inherits from FlaxPreTrainedModel. token_type_ids: typing.Optional[torch.LongTensor] = None Another common application of NLP is Question Answering. List[int]. beta: typing.Optional[float] = None ). List[str] or Wav2Vec2CTCTokenizerOutput. num_attention_heads = 12 unk_score_offset: typing.Optional[float] = None to the docstring of this method for more information. return_dict: typing.Optional[bool] = None attention_mask should only be passed if the corresponding processor has config.return_attention_mask == True. **kwargs LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. prediction (classification) objective during pretraining. output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None A transformers.modeling_outputs.SequenceClassifierOutput or a tuple of input_values: typing.Optional[torch.Tensor] dropout_rng: PRNGKey = None We are training the student to generalize the same way as the teacher by matching the output distribution. pad_to_multiple_of: typing.Optional[int] = None Parameters . # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts. for Named-Entity-Recognition (NER) tasks. Overall, our distilled model, DistilBERT, has about half the total number of parameters of BERT base and retains 95% of BERTs performances on the language understanding benchmark GLUE. Instantiating a configuration with the defaults will yield a similar configuration to that of the DistilBERT torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various input_values This model inherits from TFPreTrainedModel. token_ids_1: typing.Optional[typing.List[int]] = None A transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput or a tuple of ; min_freq (int, optional, defaults to 0) The minimum number of times a token has to be present in order to be kept in the vocabulary (otherwise it will be mapped to unk_token). attentions: typing.Optional[typing.Tuple[jax._src.numpy.ndarray.ndarray]] = None return_dict: typing.Optional[bool] = None elements depending on the configuration (LayoutLMConfig) and inputs. projected quantized states. Why does HuggingFace's Bart Summarizer replicate the given input text? It does this by regressing the offset between the location of the object's center and the center of an anchor box, and then uses the width and height of the anchor box to predict a relative scale of the object. return_overflowing_tokens: bool = False logits: ndarray has config.return_attention_mask == False, such as end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). logits (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)) Classification scores (before SoftMax). elements depending on the configuration (Wav2Vec2Config) and inputs. # The .from_pretrained methods guarantee that only one local process can concurrently, "You are instantiating a new config instance from scratch. In the example below, we prepare a question + context pair for the LayoutLM model. logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). output_hidden_states: typing.Optional[bool] = None transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor). bbox: typing.Optional[torch.LongTensor] = None How To Find It (Part 1). output_hidden_states: typing.Optional[bool] = None ( # there might be more predicted token classes than words. elements depending on the configuration (DistilBertConfig) and inputs. For such models, input_values should simply be padded with 0 and no ). return_dict: typing.Optional[bool] = None ) position_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Wav2Vec2 Model with a frame classification head on top for tasks like Speaker Diarization. logits (torch.FloatTensor of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). ( attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). ) For such models input_values should transformers.modeling_outputs.BaseModelOutput or tuple(torch.FloatTensor). 0. hidden_states: typing.Optional[typing.Tuple[jax._src.numpy.ndarray.ndarray]] = None If the model has no specific maximum input attention_mask: typing.Optional[torch.Tensor] = None distillation and cosine-distance losses. training: typing.Optional[bool] = False train: bool = False Our smaller, faster and lighter model is cheaper to pre-train and we ( configuration (DistilBertConfig) and inputs. beam_width: typing.Optional[int] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various ) eos_token = '' Another way to understand distillation is that it prevents the model to be too sure about its prediction (similarly to label smoothing). Try it out! mask_token = '[MASK]' position_ids: typing.Optional[tensorflow.python.framework.ops.Tensor] = None transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor). Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. for train: bool = False ) dtype: dtype = ( bert-base-uncased, runs 60% faster while preserving over 95% of BERTs performances as measured on the GLUE language Construct a fast LayoutLM tokenizer (backed by HuggingFaces tokenizers library). inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None A transformers.modeling_tf_outputs.TFMaskedLMOutput or a tuple of tf.Tensor (if Following RoBERTa, we trained DistilBERT on very large batches leveraging gradient accumulation (up to 4000 examples per batch), with dynamic masking and removed the next sentence prediction objective. Ming Zhou. MBart and MBart-50 DISCLAIMER: If you see something strange, file a Github Issue and assign @patrickvonplaten Overview of MBart The MBart model was presented in Multilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.. training: typing.Optional[bool] = False ; num_hidden_layers (int, optional, defaults to 12) adapter_stride = 2 codevector_dim = 256 Parameters . For all models whose processor documentation from PretrainedConfig for more information. return_dict: typing.Optional[bool] = None ( We trained on a single 12GB K80. Wav2Vec2 Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like lm_score: typing.Union[typing.List[float], float] = None This model receives the input anchor image and its neighbours, produces the clusters assignments for them using the clustering_model, and produces two outputs: 1.similarity: the similarity between the cluster assignments of the anchor image and its neighbours.This output is fed to the ClustersConsistencyLoss.2. You can easily tweak this, # In distributed training, the load_dataset function guarantee that only one local process can concurrently. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). ( Representations, transformers.modeling_outputs.Wav2Vec2BaseModelOutput, transformers.modeling_outputs.CausalLMOutput, transformers.modeling_outputs.SequenceClassifierOutput, transformers.modeling_outputs.TokenClassifierOutput, transformers.modeling_outputs.XVectorOutput, transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput, transformers.modeling_tf_outputs.TFBaseModelOutput, transformers.modeling_tf_outputs.TFCausalLMOutput, transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput, transformers.modeling_flax_outputs.FlaxMaskedLMOutput, transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput. tokenizer_file = None pooler_output (torch.FloatTensor of shape (batch_size, hidden_size)) Last layer hidden-state of the first token of the sequence (classification token) after further processing vocab_size (int, optional, defaults to 30522) Vocabulary size of the BERT model.Defines the number of different tokens that can be represented by the inputs_ids passed when calling BertModel or TFBertModel. Pegasus DISCLAIMER: If you see something strange, file a Github Issue and assign @patrickvonplaten. start_positions: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None 4. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the The DistilBertForQuestionAnswering forward method, overrides the __call__ special method. Decode output logits to audio transcription with language model support. We will follow the latter method. ) ) **kwargs encoder_hidden_states: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None **kwargs The class exposes generate(), which can be used for:. The linear Because log(0) is negative infinity, when your model trained enough the output distribution will be very skewed, for instance say I'm doing a 4 class output, in the beginning my probability looks like. from_pretrained(), Wav2Vec2CTCTokenizers with language model support into a single processor for language model boosted speech recognition decoding. input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None If this doesnt make sense, dont worry about it. ) hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape Main method to featurize and prepare for the model one or several sequence(s). This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. be passed for batched inference. token_ids_1 = None ; num_hidden_layers (int, optional, defaults to 12) Number of decoder num_codevector_groups = 2 This model receives the input anchor image and its neighbours, produces the clusters assignments for them using the clustering_model, and produces two outputs: 1.similarity: the similarity between the cluster assignments of the anchor image and its neighbours.This output is fed to the ClustersConsistencyLoss.2. XLNet Overview The XLNet model was proposed in XLNet: Generalized Autoregressive Pretraining for Language Understanding by Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le. Notably, Debajyoti Chatterjee, uploaded an interesting work on arXiv which follows a similar method for the adaptation phase on SQuAD (initializing a student from its teacher, and training a question-answering model via distillation). tokenize_chinese_chars = True Convert model's prediction probabilities to prediction labels with torch.argmax(). max_position_embeddings = 512 ( The class exposes generate(), which can be used for:. freeze_feature_encoder: bool = False token_ids_0 The abstract from the paper is the following: Transfer learning, where a model is first pre-trained on a data-rich task before mask_time_indices: typing.Optional[torch.BoolTensor] = None library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads skip_special_tokens: bool = False torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various A transformers.modeling_tf_outputs.TFCausalLMOutput or a tuple of tf.Tensor (if For Wav2Vec2 models that have set config.feat_extract_norm == "layer", such as last_hidden_state: FloatTensor = None return_dict: typing.Optional[bool] = None ( input_ids: typing.Optional[torch.Tensor] = None about any of this, as you can just pass inputs like you would to any other Python function! ; num_hidden_layers (int, optional, etc.). for Here is a non-exhaustive list: For our example, we will need a model with a sequence classification head (to be able to classify the sentences as positive or negative). tdnn_kernel = (5, 3, 3, 1, 1) output_word_offsets: bool = False We decided to focus on distillation: a technique you can use to compress a large model, called the teacher, into a smaller model, called the student. The model heads take the high-dimensional vector of hidden states as input and project them onto a different dimension. dropout_rng: PRNGKey = None Convert model's prediction probabilities to prediction labels with torch.argmax(). output_attentions: typing.Optional[bool] = None The class exposes generate(), which can be used for:. hidden states in BERT. pass your inputs and labels in any format that model.fit() supports! head_mask: typing.Optional[torch.Tensor] = None as_target_processor() this method forwards all its arguments to PreTrainedTokenizers PreTrainedTokenizer.encode() for details. ( ) call() and returns its output. return_dict: typing.Optional[bool] = None wav2vec2-base, have not been trained using This architecture contains only the base Transformer module: given some inputs, it outputs what well call hidden states, also known as features. output_hidden_states: typing.Optional[bool] = None ( wav2vec 2.0 masks loss (tf.Tensor of shape (batch_size, ), optional, returned when start_positions and end_positions are provided) Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. The main difference with our present work is that we pre-train DistilBERT with a general objective (Masked Language Modeling) in order to obtain a model that can be used for transfer-learning on a large range of tasks via finetuning (GLUE, SQuAD, classification). return_overflowing_tokens=True). Currently, only pools created with a fork context can be used. A LayoutLM. return_dict: typing.Optional[bool] = None The resource should ideally demonstrate something new instead of duplicating an existing resource. We hypothesis that in a language modeling setup, the output space (vocabulary) is significantly larger than the dimension of the downstream task output space. input_ids train: bool = False behavior. The only thing left to do is to convert the list of input IDs to tensors. proj_codevector_dim = 256 Those are not probabilities but logits, the raw, unnormalized scores outputted by the last layer of the model. ) A nice way to actually mix distillation pre-training and transfer-learning! token_ids_0: typing.List[int] use_cache = True output_hidden_states: typing.Optional[bool] = None ), ( Parameters . return_dict: typing.Optional[bool] = None A transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions or a tuple of tf.Tensor (if attention_mask: typing.Optional[torch.FloatTensor] = None Despite the vocab_size (int, optional, defaults to 50257) Vocabulary size of the GPT-2 model.Defines the number of different tokens that can be represented by the inputs_ids passed when calling GPT2Model or TFGPT2Model. encoder_hidden_states: typing.Optional[torch.FloatTensor] = None If you are creating a model from scratch. Transformers provides an AutoModel class which also has a from_pretrained() method: In this code snippet, we have downloaded the same checkpoint we used in our pipeline before (it should actually have been cached already) and instantiated a model with it. Indices can be obtained using BertTokenizer. See PreTrainedTokenizer.encode() and averaging or pooling the sequence of hidden-states for the whole input sequence. ) TFWav2Vec2 Model with a language modeling head on top for Connectionist Temporal Classification (CTC). labels: typing.Optional[torch.Tensor] = None wav2vec2-base, attention_mask should not be torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various logits (jnp.ndarray of shape (batch_size, num_choices)) num_choices is the second dimension of the input tensors. input_values: typing.Optional[torch.Tensor] ", "Whether to pad all samples to `max_seq_length`. return_dict: typing.Optional[bool] = None input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None pretrained_model_name_or_path # efficient when it receives the `special_tokens_mask`. ( Parameters . A class containing all functions for auto-regressive text generation, to be used as a mixin in PreTrainedModel.. bbox: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True). attentions: typing.Optional[typing.Tuple[jax._src.numpy.ndarray.ndarray]] = None output_attentions: typing.Optional[bool] = None ) To review, open the file in an editor that reveals hidden Unicode characters. The DistilBertForMaskedLM forward method, overrides the __call__ special method. In terms of inference time, DistilBERT is more than 60% faster and smaller than BERT and 120% faster and smaller than ELMo+BiLSTM . are not taken into account for computing the loss. Anchor boxes are fixed sized boxes that the model uses to predict the bounding box for an object. output_attentions: typing.Optional[bool] = None ( input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None projected quantized states. clean/other test sets. operating these large models in on-the-edge and/or under constrained computational training or inference budgets Instantiating a configuration ) Please refer to the docstring of the above two methods for more information. This model jax version was logits (jnp.ndarray of shape (batch_size, sequence_length, config.num_labels)) Classification scores (before SoftMax). Please general usage and behavior. input_values Please refer to the docstring of the above two methods for more information. labels: typing.Optional[torch.LongTensor] = None How to convert a Transformers model to TensorFlow? sequence labeling (information extraction) tasks such as the FUNSD transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput or tuple(torch.FloatTensor). library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads huggingface(transformers, datasets)BERT(trainer)(pipeline) huggingfacetransformers39.5k stardatasets return_dict: typing.Optional[bool] = None mask_time_prob = 0.05 How can we use such large models under low latency constraints? beam_prune_logp: typing.Optional[float] = None token_type_ids: typing.Optional[torch.LongTensor] = None output_attentions: typing.Optional[bool] = None tokenize_chinese_chars = True Parameters . **kwargs through the layers used for the auxiliary pretraining task. qa_dropout = 0.1 Lets take a look: Our model predicted [-1.5607, 1.6123] for the first sentence and [ 4.1692, -3.3464] for the second one. input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None Please take a look at the Example of decode() to better understand how to make Check the superclass documentation for the generic methods the head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None LayoutLM Model with a span classification head on top for extractive question-answering tasks such as A transformers.modeling_outputs.XVectorOutput or a tuple of **kwargs intermediate_size = 3072 linear layer on top of the hidden-states output to compute span start logits and span end logits). input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None ) A transformers.modeling_outputs.MultipleChoiceModelOutput or a tuple of train: bool = False # note: pool should be instantiated *after* `Wav2Vec2ProcessorWithLM`. token_min_logp: typing.Optional[float] = None params: dict = None We calculate the percentage of data points where the span of text obtained. ). The Wav2Vec2Model forward method, overrides the __call__ special method. Hidden-states of the model at the output of each layer plus the initial embedding outputs. bos_token = '' ctc_loss_reduction = 'sum' heads. For distilling, well use the Kullback-Leibler loss since the optimizations are equivalent: When computing the gradients with respect to q (the student distribution) we obtain the same gradients. to the tokens between our predicted start and end tokens. vocab_size (int, optional, defaults to 30522) Vocabulary size of the DistilBERT model.Defines the number of different tokens that can be represented by the inputs_ids passed when calling DistilBertModel or TFDistilBertModel. Example: ", "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index", "Pretrained config name or path if not the same as model_name", "Pretrained tokenizer name or path if not the same as model_name", "Where do you want to store the pretrained models downloaded from huggingface.co", "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not. hidden_size (int, optional, defaults to 768) Dimensionality of the encoder layers and the pooler layer. padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False
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