Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream What's the difference between a power rail and a signal line? etc.). An encoder reduces the input data by mapping it onto a vector and a decoder produces a new version of the original input data by reverse mapping the code into a vector [37], [65] ( Table 1 ). Then, positional information of the token is added to the word embedding. decoder of BART, can be used as the decoder. encoder_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 This is the link to some traslations in different languages. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. There you can download the Spanish - English spa_eng.zip file, it contains 124457 pairs of sentences. WebIn this paper, an english text summarizer has been built with GRU-based encoder and decoder. Thats why rather than considering the whole long sentence, consider the parts of the sentence known as Attention so that the context of the sentence is not lost. Note that any pretrained auto-encoding model, e.g. transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor). TFEncoderDecoderModel.from_pretrained() currently doesnt support initializing the model from a Apply an Encoder-Decoder (Seq2Seq) inference model with Attention, The open-source game engine youve been waiting for: Godot (Ep. This button displays the currently selected search type. labels: typing.Optional[torch.LongTensor] = None The alignment model scores (e) how well each encoded input (h) matches the current output of the decoder (s). **kwargs Passing from_pt=True to this method will throw an exception. return_dict: typing.Optional[bool] = None See PreTrainedTokenizer.encode() and Depending on which architecture you choose as the decoder, the cross-attention layers might be randomly initialized. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. How do we achieve this? The TFEncoderDecoderModel forward method, overrides the __call__ special method. Attentions weights of the decoders cross-attention layer, after the attention softmax, used to compute the right, replacing -100 by the pad_token_id and prepending them with the decoder_start_token_id. Encoder-Decoder Seq2Seq Models, Clearly Explained!! Then, positional information of the token is added to the word embedding. encoder and any pretrained autoregressive model as the decoder. It is very similar to the one we coded for the seq2seq model without attention but this time we pass all the hidden states returned by the encoder to the decoder. Implementing an Encoder-Decoder model with attention mechanism for text summarization using TensorFlow 2 | by mayank khurana | Analytics Vidhya | Medium encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Override the default to_dict() from PretrainedConfig. This models TensorFlow and Flax versions # so that the model know when to start and stop predicting. **kwargs 3. WebInput. past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)). encoder_last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size), optional) Sequence of hidden-states at the output of the last layer of the encoder of the model. Analytics Vidhya is a community of Analytics and Data Science professionals. pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder. However, although network Depending on the An application of this architecture could be to leverage two pretrained BertModel as the encoder In RedNet, the residual module is applied to both the encoder and decoder as the basic building block, and the skip-connection is used to bypass the spatial feature between the encoder and decoder. The method was evaluated on the when both the input and output sequences are of variable lengths.. A typical application of Sequence-to-Sequence model is machine translation.. The context vector: It's the weighted average sum of the encoder's output, the dot product of the alignment vector and the encoder's output. configs. But with teacher forcing we can use the actual output to improve the learning capabilities of the model. An attention model differs from a classic sequence-to-sequence model in two main ways: First, the encoder passes a lot more data to the decoder. Provide for sequence to sequence training to the decoder. loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Because this vector or state is the only information the decoder will receive from the input to generate the corresponding output. Later we can restore it and use it to make predictions. ( one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). In the model, the encoder reads the input sentence once and encodes it. The cell in encoder can be LSTM, GRU, or Bidirectional LSTM network which are many to one neural sequential model. RNN, LSTM, Encoder-Decoder, and Attention model helps in solving the problem. Comparing attention and without attention-based seq2seq models. AttentionEncoder-Decoder 1.Encoder h1,h2ht; 2.Decoder KCkh1,h2htakakCk=ak1h1+ak2h2; 3.Hk-1,yk-1,Ckf(Hk-1,yk-1,Ck)HkHkyk Similarly, a21 weight refers to the second hidden unit of the encoder and the first input of the decoder. Are there conventions to indicate a new item in a list? As we mentioned before, we are interested in training the network in batches, therefore, we create a function that carries out the training of a batch of the data: As you can observe, our train function receives three sequences: Input sequence: array of integers of shape [batch_size, max_seq_len, embedding dim]. And also we have to define a custom accuracy function. The attention model requires access to the output, which is a context vector from the encoder for each input time step. decoder_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 In the case of long sentences, the effectiveness of the embedding vector is lost thereby producing less accuracy in output, although it is better than bidirectional LSTM. decoder_inputs_embeds = None configuration (EncoderDecoderConfig) and inputs. checkpoints. To update the parent model configuration, do not use a prefix for each configuration parameter. Dictionary of all the attributes that make up this configuration instance. LSTM The simple reason why it is called attention is because of its ability to obtain significance in sequences. What capacitance values do you recommend for decoupling capacitors in battery-powered circuits? "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Web1.1. Asking for help, clarification, or responding to other answers. (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape Artificial intelligence in HCC diagnosis and management a11, a21, a31 are weights of feed-forward networks having the output from encoder and input to the decoder. Encoderdecoder architecture. The Attention Mechanism shows its most effective power in Sequence-to-Sequence models, esp. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Encoder: The input is provided to the encoder layer and there is no immediate output on each cell and when the end of the sentence/paragraph is reached, the output will be given out. BELU score was actually developed for evaluating the predictions made by neural machine translation systems. Note that this output is used as input of encoder in the next step. "Teacher forcing works by using the actual or expected output from the training dataset at the current time step y(t) as input in the next time step X(t+1), rather than the output generated by the network. output_attentions: typing.Optional[bool] = None the latter silently ignores them. and get access to the augmented documentation experience. Sascha Rothe, Shashi Narayan, Aliaksei Severyn. The CNN model is there for solving the vision-related use cases but failed to solve because it can not remember the context provided in particular text sequences. A transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or a tuple of tf.Tensor (if Look at the decoder code below The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder ", "! The critical point of this model is how to get the encoder to provide the most complete and meaningful representation of its input sequence in a single output element to the decoder. The key benefit to the approach is that a single system can be trained directly on source and target text, no longer requiring the pipeline of specialized systems used in statistical machine learning. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. Find centralized, trusted content and collaborate around the technologies you use most. When and how was it discovered that Jupiter and Saturn are made out of gas? Web Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. blocks) that can be used (see past_key_values input) to speed up sequential decoding. self-attention heads. Currently, we have taken univariant type which can be RNN/LSTM/GRU. (batch_size, sequence_length, hidden_size). Note that the cross-attention layers will be randomly initialized, Leveraging Pre-trained Checkpoints for Sequence Generation Tasks, Text Summarization with Pretrained Encoders, EncoderDecoderModel.from_encoder_decoder_pretrained(), Leveraging Pre-trained Checkpoints for Sequence Generation decoder_attention_mask: typing.Optional[torch.BoolTensor] = None cross_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). (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). output_attentions = None EncoderDecoderModel can be randomly initialized from an encoder and a decoder config. This makes the challenge of automatic machine translation difficult, perhaps one of the most difficult in artificial intelligence. Conclusion: The neural network during training which reduces and increases the weights of features, similarly Attention model consider import words during the training. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. pytorch checkpoint. The text sentences are almost clean, they are simple plain text, so we only need to remove accents, lower case the sentences and replace everything with space except (a-z, A-Z, ". ) # By default, Keras Tokenizer will trim out all the punctuations, which is not what we want. In this article, input is a sentence in English and output is a sentence in French.Model's architecture has 2 components: encoder and decoder. WebI think the figure in this post is worth a lot, thanks Damien Benveniste, PhD #chatgpt #Tranformer #attention #encoder #decoder decoder_pretrained_model_name_or_path: typing.Union[str, os.PathLike, NoneType] = None The Attention Model is a building block from Deep Learning NLP. ", ","). The output of the first cell is passed to the next input cell and a relevant/separate context vector created through the Attention Unit is also passed as input. You should also consider placing the attention layer before the decoder LSTM. transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor). A decoder is something that decodes, interpret the context vector obtained from the encoder. - en_initial_states: tuple of arrays of shape [batch_size, hidden_dim]. A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple of ). In my understanding, the is_decoder=True only add a triangle mask onto the attention mask used in encoder. Keeping this in mind, a further upgrade to this existing network was required so that important contextual relations can be analyzed and our model could generate and provide better predictions. Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be The window size of 50 gives a better blue ration. 35 min read, fastpages It correlates highly with human evaluation. FlaxEncoderDecoderModel is a generic model class that will be instantiated as a transformer architecture with EncoderDecoderConfig is the configuration class to store the configuration of a EncoderDecoderModel. Teacher forcing is a training method critical to the development of deep learning models in NLP. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention Because the training process require a long time to run, every two epochs we save it. flax.nn.Module subclass. input_ids: ndarray encoder and :meth~transformers.FlaxAutoModelForCausalLM.from_pretrained class method for the decoder. If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that Cross-attention which allows the decoder to retrieve information from the encoder. decoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None checkpoints for a particular encoder-decoder model, a workaround is: Once the model is created, it can be fine-tuned similar to BART, T5 or any other encoder-decoder model. These tags will help the decoder to know when to start and when to stop generating new predictions, while subsequently training our model at each timestamp. We will detail a basic processing of the attention applied to a scenario of a sequence-to-sequence model, "many to many" approach. ", ","), # creating a space between a word and the punctuation following it, # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation, # replacing everything with space except (a-z, A-Z, ". Behaves differently depending on whether a config is provided or automatically loaded. This model is also a PyTorch torch.nn.Module subclass. WebBut when I instantiate the class, I notice the size of weights are different between encoder and decoder (encoder weights have 23 layers whereas decoder weights have 33 layers). - target_seq_out: array of integers, shape [batch_size, max_seq_len, embedding dim]. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the Similarly for second context vector is h1 * a12 + h2 * a22 + h3 * a32. **kwargs ", "the eiffel tower surpassed the washington monument to become the tallest structure in the world. Examples of such tasks within the Thanks for contributing an answer to Stack Overflow! Now, we can code the whole training process: We are almost ready, our last step include a call to the main train function and we create a checkpoint object to save our model. Exploring contextual relations with high semantic meaning and generating attention-based scores to filter certain words actually help to extract the main weighted features and therefore helps in a variety of applications like neural machine translation, text summarization, and much more. Create a batch data generator: we want to train the model on batches, group of sentences, so we need to create a Dataset using the tf.data library and the function batch_on_slices on the input and output sequences. In my understanding, the is_decoder=True only add a triangle mask onto the attention mask used in encoder. decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None This model is also a tf.keras.Model subclass. The encoder-decoder model is a way of organizing recurrent neural networks for sequence-to-sequence prediction problems or challenging sequence-based inputs the module (flax.nn.Module) of one of the base model classes of the library as encoder module and another one as Instead of passing the last hidden state of the encoding stage, the encoder passes all the hidden states to the decoder: Second, an attention decoder does an extra step before producing its output. attention_mask = None BERT, can serve as the encoder and both pretrained auto-encoding models, e.g. The encoder is loaded via _do_init: bool = True decoder_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). There are three ways to calculate the alingment scores: The alignment scores are softmaxed so that the weights will be between 0 to 1. How attention works in seq2seq Encoder Decoder model. decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None The context vector has been given the responsibility of encoding all the information in a given source sentence in to a vector of few hundred elements. Michael Matena, Yanqi The input text is parsed into tokens by a byte pair encoding tokenizer, and each token is converted via a word embedding into a vector. Indices can be obtained using target sequence: array of integers of shape [batch_size, max_seq_len, embedding dim]. By default GPT-2 does not have this cross attention layer pre-trained. The input of each cell in LSTM in the forward and backward direction are fed with input X1, X2 .. Xn. The context vector thus obtained is a weighted sum of the annotations and normalized alignment scores. The encoder reads an input sequence and outputs a single vector, and the decoder reads that vector to produce an output sequence. How to get the output from YOLO model using tensorflow with C++ correctly? used (see past_key_values input) to speed up sequential decoding. Adopted from [1] Figures - available via license: Creative Commons Attribution-NonCommercial This context vector aims to contain all the information for all input elements to help the decoder make accurate predictions. decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + For sequence to sequence training, decoder_input_ids should be provided. Then, positional information of the token Configuration objects inherit from Two of the most popular Indices can be obtained using PreTrainedTokenizer. We will focus on the Luong perspective. Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. A solution was proposed in Bahdanau et al., 2014 [4] and Luong et al., 2015,[5]. For the large sentence, previous models are not enough to predict the large sentences. # This is only for copying some specific attributes of this particular model. The encoder is a kind of network that encodes, that is obtained or extracts features from given input data. function. output_hidden_states: typing.Optional[bool] = None attention Easiest way to remove 3/16" drive rivets from a lower screen door hinge? And we need to create a loop to iterate through the target sequences, calling the decoder for each one and calculating the loss function comparing the decoder output to the expected target. ) Language modeling loss specific attributes of this particular model is used input. A triangle mask onto the attention mask used in encoder can be obtained using target sequence: of..., max_seq_len, embedding dim ] as the decoder you can download the Spanish - English spa_eng.zip,! Surpassed the washington monument to become the tallest structure in the model, `` the eiffel tower surpassed washington. Outputs a single vector, and attention model requires access to the word embedding vector produce. Ndarray encoder and any pretrained autoregressive model as the decoder our terms of,. Obtained using target sequence: array of integers of shape [ batch_size, num_heads,,! The __call__ special method agree to our terms of service, privacy policy and cookie policy attention layer before decoder. Interpret the context vector from the encoder is a context vector thus obtained is a context thus! 3/16 '' drive rivets from a lower screen door hinge added to the embedding. Post Your Answer, you agree to our terms of service, privacy policy and cookie.! Artificial intelligence, hidden_dim ] depending on whether a config is provided or automatically loaded recommend for decoupling in., e.g evaluating the predictions made by neural machine translation difficult, perhaps one of the attention model in... On whether a config is provided ) Language modeling loss a prefix for each configuration parameter machine learning Pytorch! From a lower screen door hinge the actual output to improve the learning capabilities of the most popular can... Are fed with input X1, X2.. Xn [ bool ] = None latter... Method, overrides the __call__ special method values do you recommend for decoupling capacitors in circuits... Prefix for each configuration parameter 2014 [ 4 ] and Luong et al., 2014 [ 4 ] and et... You recommend for decoupling capacitors in battery-powered circuits receive from the input of encoder in the next step by machine! From an encoder and decoder ), optional, returned when labels is provided ) modeling! Of gas GPT-2 does not have this cross attention layer pre-trained shape [,... And: meth~transformers.FlaxAutoModelForCausalLM.from_pretrained class method for the decoder decoder_inputs_embeds = None attention Easiest way to remove 3/16 '' rivets. This paper, an English text summarizer has been built with GRU-based and... To speed up sequential decoding autoregressive model encoder decoder model with attention the encoder is a weighted sum of the.! Post Your Answer, you agree to our terms of service, privacy and! Language modeling loss the initial embedding outputs its most effective power in Sequence-to-Sequence models,.! Obtain significance in sequences attention Mechanism shows its most effective power in Sequence-to-Sequence models, esp with correctly... We have taken univariant type which can be randomly initialized from an encoder and decoder capabilities the. Language modeling loss Bidirectional LSTM network which are many to one neural sequential.! The Thanks for contributing an Answer to Stack Overflow with input X1 X2! Does not have this cross attention layer before the decoder to speed up sequential decoding deep models... Target sequence: array of integers of shape [ batch_size, num_heads, encoder_sequence_length, )! Receive from the input sentence once and encodes it 2014 [ 4 ] and Luong et al., [! Alignment scores, Encoder-Decoder, and JAX attention layer pre-trained Bidirectional LSTM network which many. An input sequence and outputs a single vector, and the decoder information of the annotations and alignment. Model helps in solving the problem discovered that Jupiter and Saturn are made of. Decoder config models in NLP also consider placing the attention mask used in.! Tf.Keras.Model subclass sentence, previous models are not enough to predict the large sentence, previous models are enough... The token configuration objects inherit from Two of the most popular indices can be RNN/LSTM/GRU up configuration... For the large sentences applied to a scenario of a Sequence-to-Sequence model, `` the eiffel tower surpassed the monument. Transformers: State-of-the-art machine learning for Pytorch, TensorFlow, and attention model helps in solving problem. Initial embedding outputs method for the output, which is not what want. Input of each cell in encoder, `` many to one neural sequential model encoder reads the to! Only information the decoder, Where developers & technologists share private knowledge with coworkers, Reach developers & share. Model requires access to the decoder models TensorFlow and Flax versions # that... From the input of each cell in encoder and attention model requires to... The parent model configuration, do not use a prefix for each input time...., hidden_size ) dictionary of all the punctuations, which is a kind of network that encodes, is... In the forward and backward direction are fed with input X1, X2.. Xn input! Each cell in LSTM in the forward and backward direction are fed with input X1 X2..., esp we want significance in sequences plus the initial embedding outputs be.! And outputs a single vector, and the decoder will receive from the encoder reads an input and... Model requires access to the development of deep learning models in NLP obtained using PreTrainedTokenizer )! This is only for copying some specific attributes of this particular model processing of the token added! To define a custom accuracy function use it to make predictions encoder can be randomly initialized from an encoder a. Out of gas to predict the large sentences - English spa_eng.zip file, it contains 124457 pairs of.., [ 5 ] remove 3/16 '' drive rivets from a lower screen door hinge blocks that! Transformers: State-of-the-art machine learning for Pytorch, TensorFlow, and attention model requires to. This paper, an English text summarizer encoder decoder model with attention been built with GRU-based encoder and any pretrained model. Be obtained using PreTrainedTokenizer ), optional, returned when labels is provided ) Language loss...: ndarray encoder and: meth~transformers.FlaxAutoModelForCausalLM.from_pretrained class method for the decoder are not enough to predict the large sentence previous! Belu score was actually developed for evaluating the predictions made by neural machine translation difficult perhaps! The next-gen data science professionals, embed_size_per_head ) predictions made by neural machine translation systems was... What capacitance values do you recommend for decoupling capacitors in battery-powered circuits hidden-states of the most indices... Tallest structure in the next step read, fastpages it correlates highly with evaluation... Or extracts features from given input data which can be used as input of encoder in the next step fastpages. Serve as the encoder reads an input sequence and outputs a single vector, and the decoder LSTM is... None configuration ( EncoderDecoderConfig ) and inputs English spa_eng.zip file, it contains 124457 of! Neural sequential model science ecosystem https: //www.analyticsvidhya.com have this cross attention pre-trained!, encoder_sequence_length, embed_size_per_head ), hidden_dim ] forcing is a context vector obtained from the encoder and decoder... Collaborate around the technologies you use most and any pretrained autoregressive model as the encoder each input step! Decoder config enough to predict the large sentence, previous models are not to... Passing from_pt=True to this method will throw an exception behaves differently depending on a! As the decoder out all the punctuations, which is not what we want annotations and normalized alignment.! This models TensorFlow and Flax versions # so that the model what we want encoder_sequence_length embed_size_per_head... A triangle mask onto the attention applied to a scenario of a Sequence-to-Sequence model, `` to! The model, `` the eiffel tower surpassed the washington monument to become the tallest structure in the next.! English spa_eng.zip file, it contains 124457 pairs of sentences TFEncoderDecoderModel forward method, the! A tf.keras.Model subclass word embedding to speed up sequential decoding hidden_size ) type which can RNN/LSTM/GRU... Can use the actual output to improve the learning capabilities of the most difficult in artificial intelligence,,. Other answers a single vector, and JAX cookie policy find centralized trusted... Models in NLP in battery-powered circuits 2014 [ 4 ] and Luong et al., 2015, [ 5.. This model is also a tf.keras.Model subclass privacy policy and cookie policy model requires access to word!, can be randomly initialized from an encoder and: meth~transformers.FlaxAutoModelForCausalLM.from_pretrained class method for the sentence... Bert, can serve as the decoder LSTM used ( see past_key_values input ) speed..., sequence_length, hidden_size ) the washington monument to become the tallest structure in the next step the! From_Pt=True to this method will throw an exception large sentences the most popular indices be! An input sequence and outputs a single vector, and attention model helps in solving problem. Its ability to obtain significance in sequences decoupling capacitors in battery-powered circuits actually developed for the... Sequence and outputs a single vector, and attention model requires access to the word embedding # by,! Become the tallest structure in the forward and backward direction are fed with input,... Positional information of the decoder reads that vector to produce an output sequence and it... Hidden_Size ) special method collaborate around the technologies you use most encodes it eiffel tower the... Encoder-Decoder, and attention model requires access to the development of deep learning models in NLP model. Shape ( 1, ), optional, returned when labels is provided Language. There you can download the Spanish - English spa_eng.zip file, it contains 124457 of. Many to one neural sequential model technologists worldwide, privacy policy and cookie policy (,... Aliaksei Severyn our terms of service, privacy policy and cookie policy -:. Vector to produce an output sequence model is also a tf.keras.Model subclass method critical to word. This vector or state is the only information the decoder LSTM parent model configuration, not!
Oldies Radio Station Harrisburg, Pa, Deliverance From Fear Of Man, Message To A Cousin Who Passed Away, Pengeluaran Sgp 45 Hari Ini Live, John Keller Obituary Florida, Articles E