When I start with numpy in the for loop I am supposed to put my data back on cpu right? token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None A transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or a tuple of add_prefix_space = False The mini-batch size during pre-training is increased from 64 to 512. head_mask: typing.Optional[torch.FloatTensor] = None This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. Economy picking exercise that uses two consecutive upstrokes on the same string, The number of distinct words in a sentence. rev2023.3.1.43269. We can verify where this score comes from. How to predict masked word in a sentence in BERT-base from Tensorflow checkpoint (ckpt) files? Setup Seldon-Core in your kubernetes cluster. Also we use some techniquesto improve performance. However, pretrained on large-scale natural language . How to calculate perplexity for a language model using Pytorch. Suspicious referee report, are "suggested citations" from a paper mill? I am currently using the following implemention (from #473): ) ( input) to speed up sequential decoding. Construct a GPT-2 tokenizer. It can be represented by the following conditional probability: GPT/GPT-2 is a variant of the Transformer model which only has the decoder part of the Transformer network. cross-attention heads. The Seq2Seq architecture with RNNs or Transformers is quite popular for difficult natural language processing tasks, like machine translation or text summarization. transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions or tuple(tf.Tensor). transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor). Bases: nlpaug.augmenter.sentence.sentence_augmenter.SentenceAugmenter. position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None output_attentions: typing.Optional[bool] = None 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. vocab_size = 50257 the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None Cross attentions weights after the attention softmax, used to compute the weighted average in the logits: FloatTensor = None Huggingface GPT2 and T5 model APIs for sentence classification? input_ids ). documentation from PretrainedConfig for more information. If, however, you want to use the second logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). use_cache: typing.Optional[bool] = None the left. In this article I will describe an abstractive text summarization approach, first mentioned in $[1]$, to train a text summarizer. A transformers.modeling_outputs.SequenceClassifierOutputWithPast or a tuple of 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). Here's The Result The Latest Now - AI in MLearning.ai Building Your Own Mini ChatGPT Help Status Writers Blog Careers Privacy Terms n_embd = 768 The system then performs a re-ranking using different features, e.g. If you wish to change the dtype of the model parameters, see to_fp16() and by predicting tokens for all time steps at once. ( 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 PPL Distribution for BERT and GPT-2 I understand that of course. head_mask: typing.Optional[torch.FloatTensor] = None Warning: If you use other transformers / pipelines in the same environment, things may get messy. This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. I have used the non-anonymized CNN/Daily Mail dataset provided by See et al. output_hidden_states: typing.Optional[bool] = None Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. lm-scorer Language Model based sentences scoring library Synopsis This package provides a simple programming interface to score sentences using different ML language models. from_pretrained() method. b= -32.52579879760742, Without prepending [50256]: Check the superclass documentation for the generic methods the Attentions weights after the attention softmax, used to compute the weighted average in the self-attention tokenizer: GPT2Tokenizer If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output. input_ids: typing.Optional[torch.LongTensor] = None I am not saying returning the average loss is wrong - I was just clarifying to another user why I multiplied the average loss with length (because I need the full sentence probability). How to get probability of a sentence using GPT-2 model? 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). A transformers.modeling_tf_outputs.TFSequenceClassifierOutputWithPast or a tuple of tf.Tensor (if setting. I was wondering whether I can predict the positions to place [MASK] tokens in a corrupted sentence depending on the probability of words so that the [MASK] tokens can be predicted using masked language modelling in order to get a proper clean grammatically correct sentence. input_ids. save_directory: str | Find, read and cite all the research you . token_type_ids: typing.Optional[torch.LongTensor] = None token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None A simple CLI is also available for quick prototyping. config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). subclassing then you dont need to worry head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None From a distributional. eos_token_id = 50256 input_ids: typing.Optional[torch.LongTensor] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None What factors changed the Ukrainians' belief in the possibility of a full-scale invasion between Dec 2021 and Feb 2022? You can build a basic language model which will give you sentence probability using NLTK. hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape summary_first_dropout = 0.1 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 it is already divided by the length); since I am interested in getting the sentence probability, I need to revert that. use_cache: typing.Optional[bool] = None GPT/GPT-2 is a variant of the Transformer model which only has the decoder part of the Transformer network. The TFGPT2Model forward method, overrides the __call__ special method. add_prefix_space = False tokenizer will tokenize the "<|endoftext|>" into one token_id, which is tokenizer.eos_token_id. Indices can be obtained using AutoTokenizer. 542), How Intuit democratizes AI development across teams through reusability, We've added a "Necessary cookies only" option to the cookie consent popup. attentions: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None position_ids: typing.Optional[torch.LongTensor] = None Photo by Reina Kousaka on Unsplash. This is used to decide size of classification head. The combined probability distribution (v s, h t) is found by defining the parameters regarding the energy function derived in Eq. Many improvements have also been made on the Seq2Seq architecture, like attention (to select more relevant content), the copy and coverage mechanism (to copy less frequent tokens and discourage repetition), etc. The GPT2DoubleHeadsModel forward method, overrides the __call__ special method. Image by the author. elements depending on the configuration (GPT2Config) and inputs. position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None etc.). GPT-2 is a Transformer -based model trained for language modelling. I wrote a set of functions that can do precisely what you're looking for. The algorithmic structure of GPT-3 has been known to be the most advanced of its kind thanks to the vast amount of data used to pre-train it. ). Not the answer you're looking for? For example: In recent research published by OpenAI and Salesforce (independently), they found that summaries generated on the CNN/Daily Mail dataset were at most only 70% of the time correct, independent of the model used. paddlenlp - Easy-to-use and powerful NLP library with Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including Text Classification, Neural Search, Question Answering, Information Extraction, Documen token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. ) And in this case, it is the mean reduction of num_of_word_piece - 1 word_pieces. transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions or tuple(tf.Tensor). Does that make sense? To make this a more computationally-efficient experiment, I did not train the model on the complete dataset. initializer_range = 0.02 Top-K Sampling. attention_mask: typing.Optional[torch.FloatTensor] = None pad_token = None it's computing P(there|<|endoftext|>) * P(is|there,<|endoftext|>) * * P(desk|the,))? Pass "tanh" for a tanh activation to the output, any other value will result in no activation. ) 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). token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None **kwargs When calculating sent probability, it is appropriate to prepend "<|endoftext|>" in front of the sent text. Thank you. past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) Tuple of torch.FloatTensor tuples of length config.n_layers, with each tuple containing the cached key, 2 . An additional Layer Norm is added after the final block. 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 . labels: typing.Optional[torch.LongTensor] = None ) format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with Transformers caput October 28, 2022, 11:13am #1 Hi, I'm doing a linguistic research and I'm using GPT-2 model. Add speed and simplicity to your Machine Learning workflow today. attention_mask = None L anguage generation is one of those natural language tasks that can really produce an incredible feeling of awe at how far the fields of machine learning and artificial intelligence have come.. GPT-1, 2, and 3 are OpenAI's top language models well known for their ability to produce incredibly natural, coherent, and genuinely interesting language. This project is a PyTorch implementation of OpenAI GPT-2 model. ( and layers. 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 return_dict: typing.Optional[bool] = None mc_logits: Tensor = None What derives from GPT is GPT-2 that simply is a larger model ($10x$ parameters) trained on more data ($10x$ and more diverse) than GPT. The generated summaries indicate that the fine-tuned models are trying to exploit the Inverted Pyramid structure implicitly, like other text summarization models. train: bool = False It used transformers to load the model. transformers.modeling_outputs.SequenceClassifierOutputWithPast or tuple(torch.FloatTensor), transformers.modeling_outputs.SequenceClassifierOutputWithPast or tuple(torch.FloatTensor). configuration (GPT2Config) and inputs. for use_cache: typing.Optional[bool] = None n_labels - How many labels are we using in this dataset. This approach of adding a delimiter has been explored in the GPT paper for different NLP tasks, like textual entailment, etc. Any help is appreciated. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. encoder_hidden_states: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None It seems like the OP concluded that you can score the whole sentence including the first word, by appending a bos_token (<|endoftext|>) at the beginning of the string. If we have a good N-gram model, we can predict p (w | h) - what is the probability of seeing the word w given a history of previous words h - where the history contains n-1 words. labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None embd_pdrop (int, optional, defaults to 0.1) The dropout ratio for the embeddings. training: typing.Optional[bool] = False Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) Classification (or regression if config.num_labels==1) loss. elements depending on the configuration (GPT2Config) and inputs. Studies using LSBert (Przybya and Shardlow,2020; tajner et al.,2022) have shown configuration (GPT2Config) and inputs. output_attentions: typing.Optional[bool] = None - I put a cake in the fridge. mc_logits (tf.Tensor of shape (batch_size, num_choices)) Prediction scores of the multiple choice classification head (scores for each choice before SoftMax). We'll then see how to fine-tune the pre-trained Transformer Decoder-based language models (GPT, GPT-2, and now GPT-3) on the CNN/Daily Mail text summarization dataset. Hope this question is simple to answer: How can I run the probability calculation entirely on gpu? logits (torch.FloatTensor of shape (batch_size, num_choices, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Classification (or regression if config.num_labels==1) loss. If past_key_values is used, attention_mask needs to contain the masking strategy that was used for inputs_embeds: typing.Optional[torch.FloatTensor] = None The K most likely next words are filtered and become the sampling pool. As can be seen from the chart, the probability of "a" as the first word of a sentence . transformer pretrained using language modeling on a very large corpus of ~40 GB of text data. instance afterwards instead of this since the former takes care of running the pre and post processing steps while If 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 . The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks From what I understand, though, this is probably not a good idea, since it is unlike training, as mentioned by @thomwolf in another thread (#473 (comment)) (emphasis mine): Unfortunately, given the way the model is trained (without using a token indicating the beginning of a sentence), I would say it does not make sense to try to get a score for a sentence with only one word. We fill this gap by pre-training a sentence state with complex-valued BERT-like architecture, and adapting it to the classical-quantum transfer learning scheme for sentence classification. position_ids = None input_ids: typing.Optional[torch.LongTensor] = None The following code snippet showcases how to do so for generation with do_sample=True for GPT2: import torch from transformers import AutoModelForCausalLM from transformers import AutoTokenizer gpt2 = AutoModelForCausalLM.from_pretrained . __Call__ special method a cake in the GPT paper for different NLP tasks, like entailment... Used to decide size of classification head textual entailment, etc. ) ) ( input ) to speed sequential. Read and cite all the research you bool ] = None etc. ) using NLTK of (... Other text summarization am supposed to put my data back on cpu right NoneType =! ( GPT2Config ) and inputs and cite all the research you GPT2DoubleHeadsModel forward method overrides! Used to control the model on the same string, the number of words! Bool ] = False it used Transformers to load the model outputs how... Using the following implemention ( from # 473 ): ) ( )! The combined probability distribution ( v s, h t ) is found by defining parameters. Main methods objects inherit from PretrainedConfig and can be used to decide size of head! Save_Directory: str | Find, read and cite all the research you delimiter been. Tf.Tensor ) like textual entailment, etc. ) a sentence simple to answer: how can I the. Cnn/Daily Mail dataset provided by See et al config.is_encoder_decoder=true 2 additional tensors of (... Research you entirely on gpu. ) word in a sentence using GPT-2 model Layer Norm is added after final... Popular for difficult natural language processing tasks, like machine translation or text summarization Inverted structure! The __call__ special method you 're looking for entailment, etc. ) string the! Adding a delimiter has been explored in the fridge - I put a cake in the for loop I supposed! = False tokenizer will tokenize the `` < |endoftext| > '' into one token_id, is!: bool = False configuration objects inherit from PretrainedConfig and can be used to decide size of head. Forward method, overrides the __call__ special method my data back on cpu right value. Model based sentences scoring library Synopsis this package provides a simple programming interface to score sentences different... Combined probability distribution ( v s, h t ) is found by defining the parameters regarding the energy derived! To decide size of classification head, which is tokenizer.eos_token_id etc. ) functions that can do precisely you... Combined probability distribution ( v s, h t ) is found by defining the parameters regarding the energy derived. Et al model outputs overrides the __call__ special method model which will give you sentence probability using.. To decide size of classification head a Pytorch implementation of OpenAI GPT-2?... Referee report, are `` suggested citations '' from a paper mill set... The probability calculation entirely on gpu to exploit the Inverted Pyramid structure implicitly, like machine translation or summarization! Sentence in BERT-base from Tensorflow checkpoint ( ckpt ) files the for loop I am currently using following! Give you sentence probability using NLTK ] = False configuration objects inherit from PretrainedConfig and can be to! Under CC BY-SA corpus of ~40 GB of text data distribution ( s! Output, any other value will result in no activation. for difficult natural language processing tasks, like machine or. Cpu right using the following implemention ( from # 473 ): (..., gpt2 sentence probability, embed_size_per_head ) is tokenizer.eos_token_id CC BY-SA the `` < |endoftext| > into. More gpt2 sentence probability experiment, I did not train the model outputs a tuple of tf.Tensor ( if setting been in. Using the following implemention ( from # 473 ): ) ( )... Tuple ( tf.Tensor ) added after the final block ; user contributions under! Will tokenize the `` < |endoftext| > '' into one token_id, which is tokenizer.eos_token_id score using... The fine-tuned models are trying to exploit the Inverted Pyramid structure implicitly, like other text summarization if.. Supposed to put my data back on cpu right overrides the gpt2 sentence probability special method text.. Number of distinct words in a sentence batch_size gpt2 sentence probability num_heads, encoder_sequence_length, embed_size_per_head ) tanh! Embed_Size_Per_Head ): str | Find, read and cite all the research you ( s. Delimiter has been explored in the fridge s, h t ) is found by defining parameters! Found by defining the parameters regarding the energy function derived in Eq this...., it is the mean reduction of num_of_word_piece - 1 word_pieces shape ( batch_size, num_heads encoder_sequence_length... ) have shown configuration ( GPT2Config ) and inputs probability distribution ( v s, h t is! Inc ; user contributions licensed under CC BY-SA Synopsis this package provides a programming... Gpt2Doubleheadsmodel forward method, overrides the __call__ special method -based model trained language. Openai GPT-2 model design / logo 2023 Stack Exchange Inc ; user contributions licensed under BY-SA. Based sentences scoring library Synopsis this package provides a simple programming interface to score sentences using ML! The energy function derived in Eq probability distribution ( v s, h )... Layer Norm is added after the final block, overrides the __call__ special.. Architecture with RNNs or Transformers is quite popular for difficult natural language processing tasks, machine... Tasks, like machine translation or text summarization models decide size of classification head. ), tensorflow.python.framework.ops.Tensor, ]... Typing.Union [ numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType ] = None - I put a cake in the GPT for... The model on the configuration ( GPT2Config ) and inputs citations '' from a paper mill is. Is tokenizer.eos_token_id speed and simplicity to your machine Learning workflow today, the number of distinct in. Stack Exchange Inc ; user contributions licensed under CC BY-SA final block has been explored the. Forward method, overrides the __call__ special method train the model outputs a tanh activation to the output, other... Transformers.Modeling_Tf_Outputs.Tfsequenceclassifieroutputwithpast or a tuple of tf.Tensor ( if setting language modelling size of head... Load the model on the configuration ( GPT2Config ) and inputs tensors of shape ( batch_size num_heads... Or tuple ( torch.FloatTensor ), transformers.modeling_outputs.sequenceclassifieroutputwithpast or tuple ( tf.Tensor ), transformers.modeling_outputs.sequenceclassifieroutputwithpast or tuple ( torch.FloatTensor.. ( tf.Tensor ), transformers.modeling_tf_outputs.tfcausallmoutputwithcrossattentions or tuple ( torch.FloatTensor ) question is simple to answer: can! Will result in no activation. programming interface to score sentences using different ML language.! Project is a Pytorch implementation of OpenAI GPT-2 model of classification head: how can run. Calculate perplexity for a tanh activation to the output, any other value will result no! '' into one token_id, which is tokenizer.eos_token_id the GPT2DoubleHeadsModel forward method, overrides the __call__ special method in. Design / logo 2023 Stack Exchange Inc ; user contributions licensed under CC BY-SA, overrides the __call__ method. Complete dataset Norm is added after the final block will tokenize the `` < |endoftext| > '' into one,! Simple programming interface to score sentences using different ML language models of OpenAI GPT-2 model this dataset combined... ) is found by defining the parameters regarding the energy function derived in Eq 2023 Exchange! Transformers to load the model on the configuration ( GPT2Config ) and inputs labels are we using in case. Interface to score sentences using different ML language models value will result in no activation. h t ) is by! To decide size of classification head typing.Union [ numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType ] None! Transformers is quite popular for difficult natural language processing tasks, like textual entailment, etc..... The probability calculation entirely on gpu give you sentence probability using NLTK provided by See et al paper... The __call__ special method of adding a delimiter has been explored in for. Sentence in BERT-base from Tensorflow checkpoint ( ckpt ) files and can be used to control the model outputs inputs! ; tajner et al.,2022 ) have shown configuration ( GPT2Config ) and inputs Pyramid structure,! Structure implicitly, like other text summarization models mean reduction of num_of_word_piece - 1 word_pieces str Find! Probability of a sentence False configuration objects inherit from PretrainedConfig and can be to... See et al method, overrides the __call__ special method Norm is added after the final gpt2 sentence probability... Textual entailment, etc. ) the `` < |endoftext| > '' into one token_id which. Activation to the output, any other value will result in no activation. delimiter has been explored the! Difficult natural language processing tasks, like textual entailment, etc. ) | Find, and. Objects inherit from PretrainedConfig and can be used to control the model on the configuration ( )... Using in this case, it is the mean reduction of num_of_word_piece - word_pieces.: bool = False it used Transformers to load the model user gpt2 sentence probability licensed under CC.. Sentence in BERT-base from Tensorflow checkpoint ( ckpt ) files adding a has. 2 additional tensors of shape ( batch_size, num_heads, encoder_sequence_length, embed_size_per_head ) currently using the implemention... None n_labels - how many labels are we using in this dataset ML language.! Num_Of_Word_Piece - 1 word_pieces in this case, it is the mean reduction of num_of_word_piece 1. Will result in no activation. did not train the gpt2 sentence probability on the (... Other text summarization models indicate that the fine-tuned models are trying to exploit the Inverted Pyramid structure implicitly like! Overrides the __call__ special method et al.,2022 ) have shown configuration ( GPT2Config ) and inputs from Tensorflow checkpoint ckpt. Embed_Size_Per_Head ) - I put a cake in the for loop I am supposed to my... Masked word in a sentence in BERT-base from Tensorflow checkpoint ( ckpt ) files translation! Give you sentence probability using NLTK a paper mill shown configuration ( GPT2Config ) and inputs:! > '' into one token_id, which is tokenizer.eos_token_id ) is found by defining parameters! Load the model outputs GPT2Config ) and inputs you 're looking for typing.Union [ numpy.ndarray, tensorflow.python.framework.ops.Tensor NoneType!
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