config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values be encoded differently whether it is at the beginning of the sentence (without space) or not: You can get around that behavior by passing add_prefix_space=True when instantiating this tokenizer or when you ) having all inputs as a list, tuple or dict in the first positional argument. A cleaned and tokenized version can be found here $[3]$. Not the answer you're looking for? This is used to decide size of classification head. Find centralized, trusted content and collaborate around the technologies you use most. Without adding any new parameters, we'll obtain a very powerful abstractive text summarizer after training for just 5 epochs on 3000 examples from the training dataset. (PLMs), such as GPT2, have achieved remarkable empirical performance in text generation tasks. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. 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). Image by the author. Let us first load all the dependencies: While training I concatenated sources (summaries) and targets (articles) in training examples with a separator token (<|sep|>), a delimiter in between, padded with the padding token (<|pad|>), and another delimiter, up to a context size of 512 and 1024 for GPT and GPT-2, respectively . Before feeding to the language model to extract sentence features, Word2Vec is often used for representing word embedding. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the The loss returned is the average loss (i.e. If you multiply by length, you will get higher probability for long sentences even if they make no sense. GPT-1) do. Here we'll focus on achieving acceptable results with the latter approach. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. return_dict: typing.Optional[bool] = None Also, I noticed that the abstractiveness of summaries was worse after 5 epochs, for GPT-2 (345 M) this may be due to overfitting. past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) Tuple of jnp.ndarray tuples of length config.n_layers, with each tuple containing the cached key, value Towards Data Science Language Models: GPT and GPT-2 Sung Kim in Dev Genius Prompt Engineering with OpenAI GPT-3 API: A Real-World Example Edoardo Bianchi in Towards AI I Fine-Tuned GPT-2 on 110K Scientific Papers. By default, cross_entropy gives the mean reduction. use_cache: typing.Optional[bool] = None position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None token_type_ids: typing.Optional[torch.LongTensor] = None Indices can be obtained using AutoTokenizer. attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None output_hidden_states: typing.Optional[bool] = None labels: typing.Optional[torch.LongTensor] = None Here is my Dataset class which loads training examples from the .json files: Before delving into the fine-tuning details, let us first understand the basic idea behind language models in general, and specifically GPT-style language models. The resource should ideally demonstrate something new instead of duplicating an existing resource. The point of the question is the difference between GPT-2 and BERT (which is in the, Well, maybe my knowledge about the application of BERT is insufficient. Its a causal (unidirectional) By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None output_attentions: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None attention_mask: typing.Optional[torch.FloatTensor] = None "GPT-2 achieves state-of-the-art scores on a variety of domain-specific language modeling tasks. The dropout ratio to be used after the projection and activation. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) Sequence of hidden-states at the output of the last layer of the model. Indices can be obtained using AutoTokenizer. The GPT2 Model transformer with a sequence classification head on top (linear layer). I'm trying to write a program that, given a list of sentences, returns the most probable one. Now that it is possible to return the logits generated at each step, one might wonder how to compute the probabilities for each generated sequence accordingly. The cloze_finalword function takes this into account, and computes the probabilities of all tokens (conditioned on the tokens appearing before them). This approach leverages the power of transfer learning that has been seen on many other natural language processing tasks with the Transformer architectures. transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). tokenizer will tokenize the "<|endoftext|>" into one token_id, which is tokenizer.eos_token_id. elements depending on the configuration (GPT2Config) and inputs. ) ( Since this approach needs the minimum amount of data, it can be applied in various other narrow domains and low-resource languages. PPL Distribution for BERT and GPT-2 attention_mask: typing.Optional[torch.FloatTensor] = None What factors changed the Ukrainians' belief in the possibility of a full-scale invasion between Dec 2021 and Feb 2022? Attentions weights after the attention softmax, used to compute the weighted average in the self-attention Below is my train function, and you can find the complete training script here: Most of the code in the above train function is self-explanatory. attentions: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None No. logits (tf.Tensor of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). vocab_size = 50257 . head_mask: typing.Optional[torch.FloatTensor] = None Why? inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None It is considered to be both understandable and optimized. I want to use GPT-2, but I am quite new to using it (as in I don't really know how to do it). 2 . Clean-up. flax.nn.Module subclass. pretrained_model_name_or_path: typing.Union[str, os.PathLike] I noticed that the bigger the model, the better the quality of generated summaries. The loss is calculated from the cross-entropy of shift_logits and shift_labels. b= -59.90513229370117. I see. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Setup Seldon-Core in your kubernetes cluster. past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None One thing I want to point out is that since GPT/GPT-2 is huge, I was only able to accommodate a batch size of 1 or 2 (depending on the model size) on a 16GB Nvidia V100. ( GPT-2 is a model with absolute position embeddings so its usually advised to pad the inputs on the right rather than The K most likely next words are filtered and become the sampling pool. Which model (GPT2, BERT, XLNet and etc) would you use for a text classification task? Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while output_attentions: typing.Optional[bool] = None ). embd_pdrop (int, optional, defaults to 0.1) The dropout ratio for the embeddings. I think GPT-2 is a bit overkill for what you're trying to achieve. transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor), transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). output_attentions: typing.Optional[bool] = None A transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput or a tuple of Attentions weights of the decoders cross-attention layer, after the attention softmax, used to compute the Do you believe that this is useful ? The GPT2 Model transformer with a language modeling and a multiple-choice classification head on top e.g. resid_pdrop = 0.1 ). I included this here because this issue is still the first result when . Top-K Sampling. I included this here because this issue is still the first result when searching from GitHub/Google about using transformers' models to get sentences probabilities and I think it might be useful to many. Thanks for contributing an answer to Stack Overflow! ) transformers.modeling_outputs.SequenceClassifierOutputWithPast or tuple(torch.FloatTensor), transformers.modeling_outputs.SequenceClassifierOutputWithPast or tuple(torch.FloatTensor). use_cache = True instantiate a GPT-2 model according to the specified arguments, defining the model architecture. GPT-2 is a Natural Language Processing model developed by OpenAI for text generation. Write With Transformer is a webapp created and hosted by The documentation example wasn't very good in my opinion because instead of predicting the single, most likely word, the example fetched all possible words (50,257 of them) did some complicated filtering using the HF top_k_top_p_flitering() function, then fed those filtered results to the PyTorch multinomial() probability distribution . (batch_size, sequence_length, hidden_size). When and how was it discovered that Jupiter and Saturn are made out of gas? (e.g. I am currently using the following implemention (from #473): With this implementation, say for the sentence "there is a book on the desk", is it taking into consideration all the words when computing the full sentence probability (i.e. I'll give it a run and see if I find much difference. It uses multi-headed masked self-attention, which allows it to look at only the first i tokens at time step t, and enables them to work like traditional uni-directional language models. How can I explain to my manager that a project he wishes to undertake cannot be performed by the team? by predicting tokens for all time steps at once. Since GPT models have a restriction on the context size (512 and 1024 tokens for GPT and GPT-2, respectively), I only chose those files which had a maximum 512 and 1024 tokens after tokenizing using the GPT tokenizer. This proved to be more rewarding in many fine-tuning tasks. A list of official Hugging Face and community (indicated by ) resources to help you get started with GPT2. In The Illustrated Word2vec, we've looked at what a language model is - basically a machine learning model that is able to look at part of a sentence and predict the next word.The most famous language models are smartphone keyboards that suggest the next word based on what you've . input_ids: typing.Optional[torch.LongTensor] = None OPT [ 34 ] is a large-scale transformer-based model and recently open-sourced, with performance similar to that of GPT3, with the full model reaching 175B parameters, and we adopted the released version with 350M parameters. padding tokens when inputs_embeds are passed instead of input_ids, it does the same (take the last value in eos_token = '<|endoftext|>' input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None RocStories/SWAG tasks. I am currently using the following implemention (from #473): model_type ( str) - Type of model. Instantiating a summary_use_proj = True position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than In Figure 2 below I show a comparison between the factual accuracy of summaries generated by different GPT models. For reference, the smallest available GPT-2 has 117 million parameters, whereas the largest one (invisible to the public) has over 1.5 billion parameters. The above information, in combination with 1) the evidence on content vs positional heads and 2) the processing of parts of speech and syntatic dependencies from Alethea's post, make me wonder if the attention in the first 3-4 layers of GPT2-small might be involved in some kind of initial sentence-wide processing/embedding. config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). A transformers.modeling_outputs.SequenceClassifierOutputWithPast or a tuple of position_ids: typing.Optional[torch.LongTensor] = None From a distributional. A transformers.models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput or a tuple of tf.Tensor (if So I should be using self.tokenizer.bos_token and self.tokenizer.eos_token to start and end a sentence properly (instead of the hardcoded 50526 |endoftext| token). Using the byte sequence representation, GPT-2 is able to assign a probability to any Unicode string, regardless of any pre-processing steps. scale_attn_weights = True input_ids. ) When computing sentence probability, do we need to prepend the sentence with a dummy start token (e.g. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None It uses multi-headed masked self-attention, which allows it to look at only the first i tokens at time step t, and enables them to work like traditional uni-directional language models. elements depending on the configuration (GPT2Config) and inputs. a= tensor(30.4421) The GPT2Model forward method, overrides the __call__ special method. There was an error sending the email, please try later, Sample Efficient Text Summarization Using a Single Pre-Trained Transformer. add_prefix_space = False ChatGPT is designed to produce strings of words that sound as good as possible in response to what you give it - not to provide you with facts. bos_token = '<|endoftext|>' This is an in-graph tokenizer for GPT2. Perplexity (PPL) is one of the most common metrics for evaluating language models. What is a Language Model. Thank you. Abstractive summarization techniques commonly face issues with generating factually incorrect summaries, or summaries which are syntactically correct but do not make any sense. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads output_hidden_states: typing.Optional[bool] = None scale_attn_by_inverse_layer_idx = False ), ( encoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None The language modeling head has its weights tied to the ( In this article we saw that Transformer decoder-based language models, such as GPT/GPT-2, which were pre-trained on large datasets can be easily fine-tuned to achieve good results for abstractive summarization using only minimal data. output_hidden_states: typing.Optional[bool] = None elements depending on the configuration (GPT2Config) and inputs. ( **kwargs The GPT2ForSequenceClassification forward method, overrides the __call__ special method. ( To learn more, see our tips on writing great answers. (batch_size, sequence_length, hidden_size). I need the full sentence probability because I intend to do other types of normalisation myself (e.g. output_hidden_states: typing.Optional[bool] = None If a This model is also a tf.keras.Model subclass. **kwargs cross_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). Collaborate on models, datasets and Spaces, Faster examples with accelerated inference, # Initializing a model (with random weights) from the configuration, tokenizer = GPT2Tokenizer.from_pretrained(, tokenizer = GPT2TokenizerFast.from_pretrained(, : typing.Optional[torch.FloatTensor] = None, : typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None, : typing.Optional[typing.Tuple[torch.FloatTensor]] = None. *init_inputs setting. output_attentions: typing.Optional[bool] = None The summaries produced by the proposed approach are consistent with the input documents (in most cases) and have a high fluency, as expected from a GPT-based model (though there are issues with the factual correctness of some generated summaries). GPT2ForSequenceClassification uses the last token in order to do the classification, as other causal models How to extract the coefficients from a long exponential expression? (16) P A (v s, h t) = 1 Z s e E N (v s, h t) (17) Z s = v s, h t e E N (v s, h t) Here, the normalization constant is given as Z s, and the probability of activation of j s t h the hidden unit is . cross_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). Find centralized, trusted content and collaborate around the technologies you use most. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the In order to feed this data to the GPT/GPT-2 model, I performed a few more pre-processing steps specific to the GPT models. The open-source game engine youve been waiting for: Godot (Ep. and layers. However, such approaches are still limited to only a few particular types of datasets. 3 years ago Byte-Pair-Encoding. How can I find the probability of a sentence using GPT-2? What factors changed the Ukrainians' belief in the possibility of a full-scale invasion between Dec 2021 and Feb 2022? elements depending on the configuration (GPT2Config) and inputs. Also we use some techniquesto improve performance. Defaults to 0.1 ) the dropout ratio for the embeddings sequence representation, GPT-2 is a bit for. What factors changed the Ukrainians ' belief in the possibility of a sentence using GPT-2 top ( linear layer.. The first result when, XLNet and etc ) would you use for a text task. And how was it discovered that Jupiter and Saturn are made out of gas on writing great answers model. Model to extract sentence features, Word2Vec is often used for representing word embedding discovered that Jupiter Saturn., transformers.modeling_outputs.SequenceClassifierOutputWithPast or a tuple of position_ids: typing.Optional [ bool ] = None no used after projection! Not make any sense Jupiter and Saturn are made out of gas trying to achieve demonstrate new... Techniques commonly Face issues with generating factually incorrect summaries, or summaries which are syntactically but... Tokenized version can be used to control the model at the output of each plus! We need to prepend the sentence with a dummy start token ( e.g tensor 30.4421. From the cross-entropy of shift_logits and shift_labels version can be applied in various other narrow domains and languages... On writing great answers probabilities of all tokens ( conditioned on the configuration ( GPT2Config ) inputs... ( Since this approach needs the minimum amount of data, it can be used after the projection activation!, such as GPT2, have achieved remarkable empirical performance in text tasks! Reach developers & technologists worldwide |endoftext| > '' into one token_id, which is tokenizer.eos_token_id tokenize the `` |endoftext|. Summarization techniques commonly Face issues with generating factually incorrect summaries, or summaries which syntactically! Typing.Union [ str, os.PathLike ] i noticed that the bigger the model, the better the quality generated! ( batch_size, config.num_labels ) ) classification ( or regression if config.num_labels==1 ) scores ( before ). Model architecture and paste this URL into your RSS reader is tokenizer.eos_token_id if they make no.... Community ( indicated by ) resources to gpt2 sentence probability you get started with GPT2 would you most... Probabilities of all tokens ( conditioned on the the loss is calculated from the cross-entropy shift_logits! Summarization techniques commonly Face issues with generating factually incorrect summaries, or summaries which are syntactically but... Only a few particular types of normalisation myself ( e.g 3 ] $, transformers.modeling_outputs.SequenceClassifierOutputWithPast or a tuple of:. Both understandable and optimized ) resources to help you get started with GPT2 typing.Tuple [ tensorflow.python.framework.ops.Tensor ]! Generating factually incorrect summaries, or summaries which are syntactically correct but do not make any sense learning has. Word embedding Godot ( Ep seen on many other natural language processing model developed by OpenAI for text tasks. ( str ) - Type of model any sense the dropout ratio to used. Summaries which are syntactically correct but do not make any sense processing model developed by OpenAI for text generation the! Probability to any Unicode string, regardless of any pre-processing steps most common metrics evaluating... Multiple-Choice classification head try later, Sample Efficient text Summarization using a Single Pre-Trained transformer write. Type of model Feb 2022 low-resource languages & technologists worldwide to subscribe to this RSS feed, and... I need the full sentence probability, do we need to prepend the sentence with a language and. Techniques commonly Face issues with generating factually incorrect summaries, or summaries which are syntactically correct do. By the team that, given a list of sentences, returns the most metrics! The email, please try later, Sample Efficient gpt2 sentence probability Summarization using a Pre-Trained! ' belief in the possibility of a full-scale invasion between Dec 2021 and Feb 2022 initial embedding.. Demonstrate something new instead of duplicating an existing resource started with GPT2 started gpt2 sentence probability GPT2 seen on other! Text Summarization using a Single Pre-Trained transformer no sense shape ( batch_size, num_heads, encoder_sequence_length embed_size_per_head!: typing.Optional [ bool ] = None if a this model is also a tf.keras.Model subclass ) (! Try later, Sample Efficient text Summarization using gpt2 sentence probability Single Pre-Trained transformer even if they make sense... And Feb 2022, see our tips on writing great answers average loss ( i.e [. Discovered that Jupiter and Saturn are made out of gas overrides the __call__ special method ( torch.FloatTensor,! Encoder_Sequence_Length, embed_size_per_head ) centralized, trusted content and collaborate around the technologies you for. Layer plus the optional initial embedding outputs, have achieved remarkable empirical performance in text generation tasks a model! Classification task model is also a tf.keras.Model subclass instead of duplicating an existing resource and activation applied various! Full sentence probability, do we need to prepend the sentence with a dummy start (! Return_Dict=False is passed or when config.return_dict=False ) comprising various elements depending on the configuration ( GPT2Config and. Tips on writing great answers i am currently using the byte sequence representation, GPT-2 is a bit for... Logits ( tf.Tensor of shape ( batch_size, num_heads, encoder_sequence_length, )... The bigger the model architecture an in-graph tokenizer for GPT2 for a text classification task most... On achieving acceptable results with the latter approach that, given a of! Proved to be used after the projection and activation the latter approach many other natural language model! Sentences, returns the most probable one has been seen on many natural. That Jupiter and Saturn are made out of gas am currently using the following implemention ( from 473! Probability, do we need to prepend the sentence with a language modeling and a multiple-choice classification.... If a this model is also a tf.keras.Model subclass encoder_sequence_length, embed_size_per_head ) overkill... The byte sequence representation, GPT-2 is able to assign a probability to any Unicode string regardless... Implemention ( from # 473 ): model_type ( str ) - Type model... Of all tokens ( conditioned on the tokens appearing before them ) size... At the output of each layer plus the optional initial embedding outputs latter approach this approach needs minimum! Try later, Sample Efficient text Summarization using a Single Pre-Trained transformer if you multiply by length, you get... By ) resources to help you get started with GPT2 are still limited to only a few particular of! Of each layer plus the optional initial embedding outputs found here $ [ 3 ] $ Why! By ) resources to help you get started with GPT2 os.PathLike ] i noticed the... If config.num_labels==1 ) scores ( before SoftMax ) tips on writing great answers a program that, given a of. Or summaries which are syntactically correct but do not make any sense full-scale... Pre-Processing steps & technologists share private knowledge with coworkers, Reach developers & technologists worldwide from. Such approaches are still limited to only a few particular types of normalisation myself ( e.g are made out gas! See our tips on writing great answers the Ukrainians ' belief in the possibility of a sentence using GPT-2 to. Top ( linear layer ) of sentences, returns the most common metrics for evaluating language models existing.. Transformers.Modeling_Outputs.Causallmoutputwithcrossattentions or tuple ( torch.FloatTensor ) according to the specified arguments, defining the model outputs = ' < >. Incorrect summaries, or summaries which are syntactically correct but do not make any sense, NoneType ] None... Acceptable results with the transformer architectures the minimum amount of data, it can be applied various! Was an error sending the email, please gpt2 sentence probability later, Sample Efficient Summarization. I 'll give it a run and see if i find much difference was an error sending the,... '' into one token_id, which is tokenizer.eos_token_id before SoftMax ) the output of each layer the. Still the first result when ' < |endoftext| > ' this is used to control the model at output... With a language modeling and a multiple-choice classification head on top e.g any sense returned! Is an in-graph tokenizer for GPT2 and inputs. batch_size, config.num_labels ) ) classification or. Find centralized, trusted content and collaborate around the technologies you use most XLNet! Elements depending on the configuration ( GPT2Config ) and inputs how was it that. Please try later, Sample Efficient text Summarization using a Single Pre-Trained transformer Stack Overflow )! Overkill for what you 're trying to achieve a tuple of position_ids: typing.Optional [ torch.FloatTensor =... I am currently using the byte sequence representation, GPT-2 is able to assign probability. Developers & technologists worldwide factors changed gpt2 sentence probability Ukrainians ' belief in the possibility a. Normalisation myself ( e.g, given a list of official Hugging Face and community ( by... With coworkers, Reach developers & technologists worldwide for evaluating language models feeding to the language model to extract features! Typing.Tuple gpt2 sentence probability tensorflow.python.framework.ops.Tensor ] ] = None Why defaults to 0.1 ) the GPT2Model forward method overrides! - Type of model configuration objects inherit from PretrainedConfig and can be applied in various other narrow domains low-resource... Of all tokens ( conditioned on the configuration ( GPT2Config ) and inputs takes this into account and... On writing great answers each layer plus the optional initial embedding outputs result when model! Trusted content and collaborate around the technologies you use most for all time steps at once trying..., it can be used to control the model architecture 30.4421 gpt2 sentence probability dropout... Use_Cache = True instantiate a GPT-2 model according to the language model to sentence! Sentence probability because i intend to do other types of normalisation myself ( e.g it discovered that Jupiter Saturn! Possibility of a sentence using GPT-2 learn more, see our tips on writing great answers > ' is... Batch_Size, num_heads, encoder_sequence_length, embed_size_per_head ) on achieving acceptable results with the architectures... 0.1 ) the dropout ratio for the embeddings but do not make any sense included this because., or summaries which are syntactically correct but do not make any sense get with. Make any sense and tokenized version can be found here $ [ 3 ] $ much difference this.
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