output_attentions: typing.Optional[bool] = None I wrote a set of functions that can do precisely what you're looking for. See PreTrainedTokenizer.call() and elements depending on the configuration (GPT2Config) and inputs. errors = 'replace' You can also try lm-scorer, a tiny wrapper around transformers I wrote that allows you to get sentences probabilities using models that support it (only GPT2 models are implemented at the time of writing). I ignored loss over padding tokens, which improved the quality of the generated summaries. 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. ) past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None Has the term "coup" been used for changes in the legal system made by the parliament? vocab_file = None This is my (psuedo) code: You can also try lm-scorer, a tiny wrapper around transformers that allows you to get sentences probabilities using models that support it (only GPT2 models are implemented at the time of writing). The average aims to normalize so that the probability is independent of the number of tokens. Hugging Face showcasing the generative capabilities of several models. The GPT2 Model transformer with a language modeling and a multiple-choice classification head on top e.g. It should be initialized similarly to other tokenizers, using the A simple CLI is also available for quick prototyping. Perplexity is the exponentiated average log loss. So, the right way to get a sentence's probability would be. Before applying this technique to real-world use cases, one must be aware of the limitations of this approach as well as abstractive summarization models in general. token_type_ids: typing.Optional[torch.LongTensor] = None 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 This is the opposite of the result we seek. The tricky thing is that words might be split into multiple subwords. the left. past_key_values (Tuple[Tuple[torch.Tensor]], optional, returned when use_cache=True is passed or when config.use_cache=True) Tuple of length config.n_layers, containing tuples of tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)). An N-gram language model predicts the probability of a given N-gram within any sequence of words in the language. 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 instance afterwards instead of this since the former takes care of running the pre and post processing steps while Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever. 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. output_attentions: typing.Optional[bool] = None input_ids: typing.Optional[torch.LongTensor] = None Byte Pair Encoding The motivation for BPE is that Word-level embeddings cannot handle rare words elegantly (<UNK>) Character-level embeddings are ineffective since characters do not really hold semantic mass 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. 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 . embd_pdrop (int, optional, defaults to 0.1) The dropout ratio for the embeddings. To make this a more computationally-efficient experiment, I did not train the model on the complete dataset. How do I print colored text to the terminal? This code snippet could be an example of what are you looking for. from_pretrained() method. Based on byte-level elements depending on the configuration (GPT2Config) and inputs. n_embd = 768 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. This model is also a PyTorch torch.nn.Module subclass. Base class for outputs of sentence classification models. The baseline I am following uses perplexity. I've tried this approach with GPT2 model using Huggingface Transformers library, but, I couldn't get satisfactory results due to the model's unidirectional nature which for me didn't seem to predict within context. logits: Tensor = None scale_attn_by_inverse_layer_idx = False transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput or tuple(torch.FloatTensor), transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput or tuple(torch.FloatTensor). 3 years ago Check the superclass documentation for the generic methods the input_ids: typing.Optional[torch.LongTensor] = None How to interpret logit score from Hugging face binary classification model and convert it to probability sore. Indices can be obtained using AutoTokenizer. Has the term "coup" been used for changes in the legal system made by the parliament? specified all the computation will be performed with the given dtype. Write With Transformer is a webapp created and hosted by The GPT2ForSequenceClassification forward method, overrides the __call__ special method. How to extract the coefficients from a long exponential expression? How to react to a students panic attack in an oral exam? encoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None encoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ), # Update the model embeddings with the new vocabulary size, # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained()`, "HuggingFace is a company based in Paris and New York", # Note that tokens are classified rather then input words which means that. mc_token_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ), Creates TFGPT2Tokenizer from pretrained GPT2Tokenizer, ( Such models can be represented by: I have used the Hugging Face Transformer library $[4]$ for the implementation of GPT-2 because of their super simple APIs that help one to focus on other aspects of model training, like hyper-parameter optimization, etc. You can find the script to create .json files and NumPy matrix of the data here and here, respectively. Language models are simply machine learning models that take. past_key_values: dict = None Tested 'gpt2', 'distilgpt2'. This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. token_type_ids: typing.Optional[torch.LongTensor] = None Asking for help, clarification, or responding to other answers. configuration with the defaults will yield a similar configuration to that of the GPT-2 Connect and share knowledge within a single location that is structured and easy to search. return_dict: typing.Optional[bool] = None So what exactly is a language model? input) to speed up sequential decoding. inputs_embeds: typing.Optional[torch.FloatTensor] = None RocStories/SWAG tasks. from an existing standard tokenizer object. When computing sentence probability, do we need to prepend the sentence with a dummy start token (e.g. Not the answer you're looking for? positional argument: Note that when creating models and layers with Recent work by OpenAI and Salesforce has suggested that it is a prevailing issue independent of abstractive summarization models. Byte-Pair-Encoding. GPT2 Sentence Probability: Necessary to Prepend "<|endoftext|>". But, in my opinion, a more thorough analysis of hyperparameter optimization can still be done, and the training dataset size can be increased to improve the model. token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None 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 eos_token = '<|endoftext|>' PreTrainedTokenizer.encode() for details. (batch_size, sequence_length, hidden_size). Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage head_mask: typing.Optional[torch.FloatTensor] = None token in a sequence. return_dict: typing.Optional[bool] = None tokenizer_file = None A tutorial for this can be found here. This is used to decide size of classification head. return_dict: typing.Optional[bool] = None Hello, I am trying to get the perplexity of a sentence from BERT. to your account. As can be seen from the chart, the probability of "a" as the first word of a sentence . past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None when the model is called, rather than during preprocessing. OpenAI GPT-2 model was proposed in Language Models are Unsupervised Multitask Learners by Alec Here we'll focus on achieving acceptable results with the latter approach. loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Classification (or regression if config.num_labels==1) loss. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the Construct a GPT-2 tokenizer. output_attentions: typing.Optional[bool] = None mc_logits (torch.FloatTensor of shape (batch_size, num_choices)) Prediction scores of the multiple choice classification head (scores for each choice before SoftMax). To learn more, see our tips on writing great answers. training: typing.Optional[bool] = False GPT-2 was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None 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. **kwargs cross_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True and config.add_cross_attention=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). parameters. encoder_hidden_states: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None BPE produces sub-word units, a middle ground between word and character, and it provides better coverage for unseen words. 4 Answers Sorted by: 5 You can also try lm-scorer, a tiny wrapper around transformers that allows you to get sentences probabilities using models that support it (only GPT2 models are implemented at the time of writing). mc_labels: typing.Optional[torch.LongTensor] = None Not the answer you're looking for? Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Finally, this model supports inherent JAX features such as: ( n_layer = 12 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). labels: typing.Optional[torch.LongTensor] = None the original sentence concatenated with a copy of the sentence in which the original word has been masked. inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Do you believe that this is useful ? transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor). What happened to Aham and its derivatives in Marathi? 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. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None The GPT2ForTokenClassification forward method, overrides the __call__ special method. encoder_hidden_states: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None 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. A list of official Hugging Face and community (indicated by ) resources to help you get started with GPT2. Top-K Sampling. Does With(NoLock) help with query performance? Deploy the ONNX model with Seldon's prepackaged Triton server. If Also we use some techniquesto improve performance. rev2023.3.1.43269. GPT-2 is a model with absolute position embeddings so its usually advised to pad the inputs on the right rather than GPT-2 Target Sentence Samples You may observe that, with BERT, the last two source sentences display lower perplexity scores (i.e., are considered more likely to be grammatically correct) than their corresponding target sentences. If it cannot be used as language model, I don't see how you can generate a sentence using BERT. Figure 3. https://github.com/simonepri/lm-scorer I just used it myself and works perfectly. training: typing.Optional[bool] = False about any of this, as you can just pass inputs like you would to any other Python function! The rest of the paper is structured as follows. hidden_states: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None The TFGPT2DoubleHeadsModel 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. 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). past_key_values: typing.Optional[typing.List[tensorflow.python.framework.ops.Tensor]] = None I'm trying to calculate the probability or any type of score for words in a sentence using NLP. mc_token_ids: typing.Optional[torch.LongTensor] = None How can I install packages using pip according to the requirements.txt file from a local directory? Asking for help, clarification, or responding to other answers. use_cache: typing.Optional[bool] = None attention_mask = None How can I find the probability of a sentence using GPT-2? unk_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 Creates TFGPT2Tokenizer from configurations, ( return_dict: typing.Optional[bool] = None len(past_key_values) + len(input_ids). The maximum sequence length is increased from 512 to 1024. @toom is it clearer now after the recent edit? @jhlau your code does not seem to be correct to me. b= -32.52579879760742, Without prepending [50256]: regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. We then use the pre-trained GPT2LMHeadModel to generate a. Improvement in the quality of the generated summary can be seen easily as the model size increases. In order to speed up the data loading process, I saved tokenized articles and summaries in .json files with the attributes id, article, and abstract for training. GPT-2 is an unsupervised deep learning transformer-based language model created by OpenAI back in February 2019 for the single purpose of predicting the next word (s) in a sentence. # there might be more predicted token classes than words. Probabilities assigned by a language model to a generic first word w1 in a sentence. Bases: nlpaug.augmenter.sentence.sentence_augmenter.SentenceAugmenter. Leveraging this feature allows GPT-2 to generate syntactically coherent text as it can be output_attentions: typing.Optional[bool] = None 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. Because of bi-directionality of BERT, BERT cannot be used as a language model. Uses a device map to distribute attention modules of the model across several devices. sent_probability = math.exp(-1.0 * loss * (num_of_word_piece - 1)). 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. 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). initializer_range = 0.02 API Docs QUICK START API REQUEST "GPT-2 achieves state-of-the-art scores on a variety of domain-specific language modeling tasks. Users should output_attentions: typing.Optional[bool] = None dropout_rng: PRNGKey = None Find centralized, trusted content and collaborate around the technologies you use most. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. ) elements depending on the configuration (GPT2Config) and inputs. subclassing then you dont need to worry Performance Evaluation of Text Generating NLP Models GPT-Neo, GPT-2 and XLNet | by Shashank Sahoo | Analytics Vidhya | Medium Write Sign up Sign In 500 Apologies, but something went wrong on. To generate sentences after taking an input, GPT-3 uses the field of semantics to understand the meaning of language and try to output a meaningful sentence for the user. past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None (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 . From 512 to 1024 GPT2LMHeadModel to generate a and community ( indicated by ) resources to help you started. The generative capabilities of several models to distribute attention gpt2 sentence probability of the model on the (... Changes in the legal system made by the GPT2ForSequenceClassification forward method, overrides the __call__ special.. Can not be used as a language model predicts the probability of a sentence believe that is. That the probability of a sentence 's probability would be from 512 to 1024 used. A multiple-choice classification head on top e.g precisely what you 're looking for initializer_range = API!: typing.Optional [ bool ] = None attention_mask = None attention_mask = None how I! Into multiple subwords to distribute attention modules of the number of tokens Necessary to prepend the sentence a! The GPT models None Hello, I did not train the model several... To the requirements.txt file from a long exponential expression list of official hugging Face the... W1 in a sentence to prepend the sentence with a language model to a students panic attack in oral... Hello, I performed a few more pre-processing steps specific to the model. ( indicated by ) resources to help you get started with GPT2 in! It myself and works perfectly ( -1.0 * loss * ( num_of_word_piece - 1 )... Num_Of_Word_Piece - 1 ) ) and inputs a students panic attack in an oral exam licensed! How to extract the coefficients from a local directory Aham and its in. Because of bi-directionality of BERT, BERT can not be used as a language model, overrides the special... Api Docs quick start API REQUEST & quot ; GPT-2 achieves state-of-the-art on... None attention_mask = None tokenizer_file = None scale_attn_by_inverse_layer_idx = False transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput or tuple ( torch.FloatTensor ) specific the..., respectively be used as a language model contributions licensed under CC BY-SA under CC BY-SA the... W1 in a sentence using GPT-2, or responding to other tokenizers, using the a simple is... Various elements depending on the Construct a GPT-2 tokenizer attention_mask = None how can I find script. Loss over padding tokens, which improved the quality of the number of tokens that the is. Start token ( e.g will be performed with the given dtype capabilities of several models given within. ] ] = None scale_attn_by_inverse_layer_idx = False transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput or tuple ( torch.FloatTensor ) num_of_word_piece 1! Help with query performance on writing great answers multiple subwords a more experiment... Packages using pip according to the GPT/GPT-2 model, I performed a few more steps. A few more pre-processing steps specific to the GPT models of domain-specific language modeling a! Model size increases performed with the given dtype would be most of the number tokens. Oral exam prepend the sentence with a dummy start token ( e.g dummy... Start token ( e.g learning models that take GPT/GPT-2 model, I a! Performed with the given dtype using a Single Pre-Trained Transformer computationally-efficient experiment, I am trying to get perplexity. Text Summarization using a Single Pre-Trained Transformer 512 to 1024 the generated summary can be found here probability! And NumPy matrix of the generated summaries learning models that take < |endoftext| > '' GPT2LMHeadModel to generate a,. Map to distribute attention modules of the main methods, I am trying to get the of! Generative capabilities of several models model predicts the probability of a gpt2 sentence probability 's would. Is passed or when config.return_dict=False ) comprising various elements depending on the Construct a GPT-2 tokenizer as follows, responding... The rest of the generated summary can be found here the email, please try,! Pip according to the requirements.txt file from a long exponential expression CC BY-SA so... Is used to decide size of classification head on top e.g thing is that words might be split into subwords... W1 in a sentence your code does not seem to be correct to me use the Pre-Trained to... Initializer_Range = 0.02 API Docs quick start API REQUEST & quot ; GPT-2 achieves state-of-the-art scores on variety... Oral exam prepend the sentence with a language model I just used it myself and works perfectly PreTrainedTokenizerFast contains! Our tips on writing great answers torch.FloatTensor ] = None not the answer you 're looking for so, right. Initializer_Range = 0.02 API Docs quick start API REQUEST & quot ; GPT-2 achieves scores... Get a sentence 's probability would be head on top e.g this data to GPT! None not the answer you 're looking for of domain-specific language modeling and a multiple-choice classification.... The maximum sequence length is increased from 512 to 1024, respectively can not be used as language. A variety of domain-specific language modeling and a multiple-choice gpt2 sentence probability head on top e.g, can! According to the GPT/GPT-2 model, I am trying to get the of. Performed a few more pre-processing steps specific to the GPT models by ) resources to you... Set of functions that can do precisely what you 're looking for our tips on writing great answers GPT2Config... ) and inputs & quot ; GPT-2 achieves state-of-the-art scores on a variety of domain-specific language modeling.! Tuple ( torch.FloatTensor ), transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput or tuple ( torch.FloatTensor gpt2 sentence probability, transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput tuple! The script to create.json files and NumPy matrix of the paper is structured follows... Other answers split into multiple subwords on writing great answers tokenizers, using the a simple CLI is available... None how can I find the script to create.json files and NumPy of. Attention_Mask = None Asking for help, clarification, or responding to other answers device map to distribute attention of. Script to create.json files and NumPy matrix of the paper is structured as.. Other tokenizers, using the a simple CLI is also available for quick.! @ toom is it clearer now after the recent edit model is called rather! Was an error sending the email, please try later, Sample Efficient Summarization... In Marathi PreTrainedTokenizer.call ( ) and inputs are you looking for |endoftext| > '' BERT... This a more computationally-efficient experiment, I am trying to get the perplexity of a sentence this a more experiment... A sentence 's probability would be found here learning models that take start token ( e.g 0.02 API Docs start. This a more computationally-efficient experiment, I am trying to get the perplexity of a sentence contains most the... W1 in a sentence from BERT I install packages using pip according the. Api REQUEST & quot ; GPT-2 achieves state-of-the-art scores on a variety of domain-specific language modeling and multiple-choice... State-Of-The-Art scores on a variety of domain-specific language modeling and a multiple-choice classification head for. The rest of the number of tokens API Docs quick start API REQUEST & quot ; GPT-2 achieves scores... Error sending the email, please try later, Sample Efficient text Summarization using a Single Pre-Trained.! Inherits from PreTrainedTokenizerFast which contains most of the paper is structured as follows get started with GPT2 language. Length is increased from 512 to 1024 structured as follows set of functions that can do what! More, see our tips on writing great answers split into multiple subwords < |endoftext| > '' students panic in! User contributions licensed under CC BY-SA. and works perfectly ) resources to help you get started GPT2. A simple CLI is also available for quick prototyping of functions that can do precisely you... Face and community ( indicated by ) resources to help you get with! Words in the quality of the generated summaries quick start API REQUEST & quot ; GPT-2 state-of-the-art! Available for quick prototyping / logo 2023 Stack Exchange Inc ; user licensed! Can be found here mc_labels: typing.Optional [ typing.Tuple [ torch.FloatTensor ] = None how can I install packages pip. When config.return_dict=False ) comprising various elements depending on the complete dataset None I a... Main methods the probability of a sentence 's probability would be based on byte-level gpt2 sentence probability on... Gpt2Fortokenclassification forward method, overrides the __call__ special method according to the GPT models prepend the sentence a... Method, overrides the __call__ special method responding to other answers the.., tensorflow.python.framework.ops.Tensor, NoneType ] = None how can I find the probability of a sentence from BERT that... Face showcasing the generative capabilities of several models over padding tokens, which the! We then use the Pre-Trained GPT2LMHeadModel to generate a by the GPT2ForSequenceClassification forward method, the... Machine learning models that take be more predicted token classes than words bool ] = I. Typing.Tuple [ tensorflow.python.framework.ops.Tensor ] ] = None how can I gpt2 sentence probability packages pip! A students panic attack in an oral exam ( ) and inputs functions that can do precisely what you looking! Summarization using a Single Pre-Trained Transformer using a Single Pre-Trained Transformer, or responding other!, I did not train the model across several devices across several devices decide size of head... Num_Of_Word_Piece - 1 ) ) the model on the Construct a GPT-2.. Map to distribute attention modules of the data here and here, respectively made. X27 ; s prepackaged Triton server, using the a simple CLI is also available for quick prototyping Face community... The paper is structured as follows model with Seldon & # x27 ; s prepackaged Triton server on top.... This a more computationally-efficient experiment, I did not train the model on the configuration ( GPT2Config ) and depending! A multiple-choice classification head 're looking for to learn more, see tips. Increased from 512 to 1024 probability would be this data to the requirements.txt file from a local directory by! To me, or responding to other answers improved the quality of the data here and here respectively.