from Transformers. 657 Examples 7 1234567891011121314next 3View Source File : language_model.py License : MIT License Project Creator : Aleph-Alpha def gptj_config(): should refer to the superclass for more information regarding methods. Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). This is useful if you want more control over how to convert input_ids indices into associated vectors MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios. Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of num_choices is the second dimension of the input tensors. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), refer to the TF 2.0 documentation for all matter related to general usage and behavior. of GLUE benchmark on the website. A tag already exists with the provided branch name. Our results are similar to the TensorFlow implementation results (actually slightly higher): To get these results we used a combination of: Here is the full list of hyper-parameters for this run: If you have a recent GPU (starting from NVIDIA Volta series), you should try 16-bit fine-tuning (FP16). This can be done for example by running the following command on each server (see the above mentioned blog post for more details): Where $THIS_MACHINE_INDEX is an sequential index assigned to each of your machine (0, 1, 2) and the machine with rank 0 has an IP address 192.168.1.1 and an open port 1234. Check out the from_pretrained() method to load the model weights. pre and post processing steps while the latter silently ignores them. PreTrainedModel also implements a few methods which are common among all the models to: The BertForPreTraining forward method, overrides the __call__() special method. Finally, embedding-as-service help you to encode any given text to fixed length vector from supported embeddings and models. usage and behavior. The inputs and output are identical to the TensorFlow model inputs and outputs. a next sentence prediction (classification) head. OpenAIGPTModel is the basic OpenAI GPT Transformer model with a layer of summed token and position embeddings followed by a series of 12 identical self-attention blocks. This tokenizer can be used for adaptive softmax and has utilities for counting tokens in a corpus to create a vocabulary ordered by toekn frequency (for adaptive softmax). Please refer to the doc strings and code in tokenization_transfo_xl.py for the details of these additional methods in TransfoXLTokenizer. deep, The BertForQuestionAnswering forward method, overrides the __call__() special method. (see input_ids above). Bert Model with two heads on top as done during the pre-training: , . The code has not been tested with half-precision training with apex on any GLUE task apart from MRPC, MNLI, CoLA, SST-2. The best would be to finetune the pooling representation for you task and use the pooler then. Mask values selected in [0, 1]: by concatenating and adding special tokens. heads. config=BertConfig.from_pretrained(TO_FINETUNE, num_labels=num_labels) tokenizer=BertTokenizer.from_pretrained(TO_FINETUNE) defconvert_examples_to_tf_dataset( examples: List[Tuple[str, int]], tokenizer, max_length=512, Loads data into a tf.data.Dataset for finetuning a given model. as a decoder, in which case a layer of cross-attention is added between of the semantic content of the input, youre often better with averaging or pooling Apr 25, 2019 is used in the cross-attention if the model is configured as a decoder. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. 9 comments lethienhoa commented on Jul 17, 2020 edited lethienhoa closed this as completed on Jul 17, 2020 mentioned this issue on Sep 25, 2022 This repository contains op-for-op PyTorch reimplementations, pre-trained models and fine-tuning examples for: These implementations have been tested on several datasets (see the examples) and should match the performances of the associated TensorFlow implementations (e.g. This is the token used when training this model with masked language The new_mems contain all the hidden states PLUS the output of the embeddings (new_mems[0]). architecture modifications. language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI Training with the previous hyper-parameters gave us the following results: The data for SWAG can be downloaded by cloning the following repository. Last layer hidden-state of the first token of the sequence (classification token) Word2Vecword2vecword2vec word2vec . You will find more information regarding the internals of apex and how to use apex in the doc and the associated repository. from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') Unlike the BERT Models, you don't have to download a different tokenizer for each different type of model. labels (torch.LongTensor of shape (batch_size, sequence_length), optional, defaults to None) Labels for computing the token classification loss. from_pretrained . This command runs in about 10 min on a single K-80 an gives an evaluation accuracy of about 87.7% (the authors report a median accuracy with the TensorFlow code of 85.8% and the OpenAI GPT paper reports a best single run accuracy of 86.5%). Each derived config class implements model specific attributes. the hidden-states output) e.g. This model is a PyTorch torch.nn.Module sub-class. py2, Status: Training one epoch on this corpus takes about 1:20h on 4 x NVIDIA Tesla P100 with train_batch_size=200 and max_seq_length=128: Thank to the work of @Rocketknight1 and @tholor there are now several scripts that can be used to fine-tune BERT using the pretraining objective (combination of masked-language modeling and next sentence prediction loss). Position outside of the sequence are not taken into account for computing the loss. We detail them here. All _LRSchedule subclasses accept warmup and t_total arguments at construction. training (boolean, optional, defaults to False) Whether to activate dropout modules (if set to True) during training or to de-activate them Position outside of the sequence are not taken into account for computing the loss. Corpus (MRPC) corpus and runs in less than 10 minutes on a single K-80 and in 27 seconds (!) perform the optimization step on CPU to store Adam's averages in RAM. TPU are not supported by the current stable release of PyTorch (0.4.1). classmethod from_pretrained (pretrained_model_name_or_path, **kwargs) [source] The BertForSequenceClassification forward method, overrides the __call__() special method. First let's prepare a tokenized input with OpenAIGPTTokenizer, Let's see how to use OpenAIGPTModel to get hidden states. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general The BERT model was proposed in BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding intermediate_size (int, optional, defaults to 3072) Dimensionality of the intermediate (i.e., feed-forward) layer in the Transformer encoder. improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement). The rest of the repository only requires PyTorch. Bert Model with a language modeling head on top. A series of tests is included in the tests folder and can be run using pytest (install pytest if needed: pip install pytest). instead of this since the former takes care of running the Use it as a regular TF 2.0 Keras Model and Apr 25, 2019 Then run. The third NoteBook (Comparing-TF-and-PT-models-MLM-NSP.ipynb) compares the predictions computed by the TensorFlow and the PyTorch models for masked token language modeling using the pre-trained masked language modeling model. BertConfig output_hidden_state=True . bert_config = BertConfig.from_pretrained (MODEL_NAME) bert_config.output_hidden_states = True backbone = TFAutoModelForSequenceClassification.from_pretrained (MODEL_NAME,config=bert_config) input_ids = tf.keras.layers.Input (shape= (MAX_LENGTH,), name='input_ids', dtype='int32') features = backbone (input_ids) [1] [-1] pooling = This model is a tf.keras.Model sub-class. attention_probs_dropout_prob (float, optional, defaults to 0.1) The dropout ratio for the attention probabilities. textExtractor = BertModel. py3, Uploaded Indices should be in [0, 1]. This implementation does not add special tokens. BertForQuestionAnswering is a fine-tuning model that includes BertModel with a token-level classifiers on top of the full sequence of last hidden states. type_vocab_size (int, optional, defaults to 2) The vocabulary size of the token_type_ids passed into BertModel. It obtains new state-of-the-art results on eleven natural Configuration objects inherit from PretrainedConfig and can be used # Initializing a BERT bert-base-uncased style configuration, # Initializing a model from the bert-base-uncased style configuration, transformers.PreTrainedTokenizer.encode(), transformers.PreTrainedTokenizer.__call__(), # The last hidden-state is the first element of the output tuple, "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced. token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional, defaults to None) , Segment token indices to indicate first and second portions of the inputs. for Named-Entity-Recognition (NER) tasks. pre-trained using a combination of masked language modeling objective and next sentence prediction num_hidden_layers (int, optional, defaults to 12) Number of hidden layers in the Transformer encoder. ", # choice0 is correct (according to Wikipedia ;)), batch size 1, # the linear classifier still needs to be trained, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, https://github.com/huggingface/transformers/issues/328. Indices should be in [0, , num_choices] where num_choices is the size of the second dimension from transformers import BertForSequenceClassification, AdamW, BertConfig model = BertForSequenceClassification.from_pretrained( "bert-base-uncased", num_labels = 2, output_attentions = False, output_hidden_states = False, ) for GLUE tasks. usage and behavior. The TFBertForTokenClassification forward method, overrides the __call__() special method. PyTorch pretrained bert can be installed by pip as follows: If you want to reproduce the original tokenization process of the OpenAI GPT paper, you will need to install ftfy (limit to version 4.4.3 if you are using Python 2) and SpaCy : If you don't install ftfy and SpaCy, the OpenAI GPT tokenizer will default to tokenize using BERT's BasicTokenizer followed by Byte-Pair Encoding (which should be fine for most usage, don't worry). from_pretrained ('bert-base-uncased') self. RocStories dataset and unpack it to some directory $ROC_STORIES_DIR. labels (torch.LongTensor of shape (batch_size, sequence_length), optional, defaults to None) Labels for computing the masked language modeling loss. Three notebooks that were used to check that the TensorFlow and PyTorch models behave identically (in the notebooks folder): These notebooks are detailed in the Notebooks section of this readme. Now, let's import the available pretrained model from the IndoNLU project that is hosted in the Hugging-Face platform. If you're not sure which to choose, learn more about installing packages. 1 indicates sequence B is a random sequence. This is the token which the model will try to predict. You can use the same tokenizer for all of the various BERT models that hugging face provides. Then, a tokenizer that we will use later in our script to transform our text input into BERT tokens and then pad and truncate them to our max length. PyTorch PyTorch out4 NumPy GPU CPU the input of the softmax when we have a language modeling head on top). This example code fine-tunes BERT on the Microsoft Research Paraphrase Some features may not work without JavaScript. A tag already exists with the provided branch name. The BertForTokenClassification forward method, overrides the __call__() special method. This should likely be deactivated for Japanese: ", "The sky is blue due to the shorter wavelength of blue light. encoder_hidden_states is expected as an input to the forward pass. Use it as a regular TF 2.0 Keras Model and The data for SQuAD can be downloaded with the following links and should be saved in a $SQUAD_DIR directory. To help with fine-tuning these models, we have included several techniques that you can activate in the fine-tuning scripts run_classifier.py and run_squad.py: gradient-accumulation, multi-gpu training, distributed training and 16-bits training . Indices can be obtained using transformers.BertTokenizer. Here is a quick-start example using TransfoXLTokenizer, TransfoXLModel and TransfoXLModelLMHeadModel class with the Transformer-XL model pre-trained on WikiText-103. The abstract from the paper is the following: We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations labels (torch.LongTensor of shape (batch_size,), optional, defaults to None) Labels for computing the sequence classification/regression loss. This should improve model performance, if the language style is different from the original BERT training corpus (Wiki + BookCorpus). is_decoder argument of the configuration set to True; an Here is how to extract the full list of hidden states from the model output: TransfoXLLMHeadModel includes the TransfoXLModel Transformer followed by an (adaptive) softmax head with weights tied to the input embeddings. BertBERTBERTBERT()2021BertBert . to control the model outputs. Position outside of the sequence are not taken into account for computing the loss. Rouge encoded_layers: controled by the value of the output_encoded_layers argument: pooled_output: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a classifier pretrained on top of the hidden state associated to the first character of the input (CLF) to train on the Next-Sentence task (see BERT's paper). Developed and maintained by the Python community, for the Python community. OpenAI GPT-2 was released together with the paper Language Models are Unsupervised Multitask Learners by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**. tuple of tf.Tensor (one for each layer) of shape GPT2Tokenizer perform byte-level Byte-Pair-Encoding (BPE) tokenization. model. # OPTIONAL: if you want to have more information on what's happening, activate the logger as follows, # Load pre-trained model tokenizer (vocabulary), "[CLS] Who was Jim Henson ? a language modeling head with weights tied to the input embeddings (no additional parameters) and: a multiple choice classifier (linear layer that take as input a hidden state in a sequence to compute a score, see details in paper). Args: examples: List of tuples representing the examples to be fed This model is a PyTorch torch.nn.Module sub-class. OpenAI GPT was released together with the paper Improving Language Understanding by Generative Pre-Training by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. this function, one should call the Module instance afterwards The Linear Instantiating a configuration with the defaults will yield a similar configuration to that of the BERT bert-base-uncased architecture. This second option is useful when using tf.keras.Model.fit() method which currently requires having config = BertConfig. Bert Model with a multiple choice classification head on top (a linear layer on top of model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids]), a dictionary with one or several input Tensors associated to the input names given in the docstring: Positions are clamped to the length of the sequence (sequence_length). Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss. TF 2.0 models accepts two formats as inputs: having all inputs as keyword arguments (like PyTorch models), or. First install apex as indicated here. There are three types of files you need to save to be able to reload a fine-tuned model: Here is the recommended way of saving the model, configuration and vocabulary to an output_dir directory and reloading the model and tokenizer afterwards: Here is another way you can save and reload the model if you want to use specific paths for each type of files: Models (BERT, GPT, GPT-2 and Transformer-XL) are defined and build from configuration classes which containes the parameters of the models (number of layers, dimensionalities) and a few utilities to read and write from JSON configuration files. all systems operational. . two sequences num_choices is the size of the second dimension of the input tensors. Here is an example of hyper-parameters for a FP16 run we tried: The results were similar to the above FP32 results (actually slightly higher): We include three Jupyter Notebooks that can be used to check that the predictions of the PyTorch model are identical to the predictions of the original TensorFlow model. BERT Bidirectional Encoder Representations from Transformers Google Transformer Encoder BERTlanguage ModelLM . Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax). Again module does not support Python 2! Bert Model with a token classification head on top (a linear layer on top of Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. You only need to run this conversion script once to get a PyTorch model. the hidden-states output to compute span start logits and span end logits). A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. BertConfig.from_pretrainedBertModel.from_pretrainedBERTBertConfig.from_pretrainedBertModel.from_pretrained This package comprises the following classes that can be imported in Python and are detailed in the Doc section of this readme: Eight Bert PyTorch models (torch.nn.Module) with pre-trained weights (in the modeling.py file): Three OpenAI GPT PyTorch models (torch.nn.Module) with pre-trained weights (in the modeling_openai.py file): Two Transformer-XL PyTorch models (torch.nn.Module) with pre-trained weights (in the modeling_transfo_xl.py file): Three OpenAI GPT-2 PyTorch models (torch.nn.Module) with pre-trained weights (in the modeling_gpt2.py file): Tokenizers for BERT (using word-piece) (in the tokenization.py file): Tokenizer for OpenAI GPT (using Byte-Pair-Encoding) (in the tokenization_openai.py file): Tokenizer for Transformer-XL (word tokens ordered by frequency for adaptive softmax) (in the tokenization_transfo_xl.py file): Tokenizer for OpenAI GPT-2 (using byte-level Byte-Pair-Encoding) (in the tokenization_gpt2.py file): Optimizer for BERT (in the optimization.py file): Optimizer for OpenAI GPT (in the optimization_openai.py file): Configuration classes for BERT, OpenAI GPT and Transformer-XL (in the respective modeling.py, modeling_openai.py, modeling_transfo_xl.py files): Five examples on how to use BERT (in the examples folder): One example on how to use OpenAI GPT (in the examples folder): One example on how to use Transformer-XL (in the examples folder): One example on how to use OpenAI GPT-2 in the unconditional and interactive mode (in the examples folder): These examples are detailed in the Examples section of this readme. cls_token (string, optional, defaults to [CLS]) The classifier token which is used when doing sequence classification (classification of the whole If config.num_labels > 1 a classification loss is computed (Cross-Entropy). can be represented by the inputs_ids passed to the forward method of BertModel. pip install pytorch-pretrained-bert encoder_hidden_states (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional, defaults to None) Sequence of hidden-states at the output of the last layer of the encoder. in the first positional argument : a single Tensor with input_ids only and nothing else: model(inputs_ids), a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: The first NoteBook (Comparing-TF-and-PT-models.ipynb) extracts the hidden states of a full sequence on each layers of the TensorFlow and the PyTorch models and computes the standard deviation between them. bertpoolingQA. Text preprocessing is the end-to-end transformation of raw text into a model's integer inputs. Mask values selected in [0, 1]: This output is usually not a good summary Instantiating a configuration with the defaults will yield a similar configuration to that of the BERT bert-base-uncased architecture. This command will download a pre-processed version of the WikiText 103 dataset in which the vocabulary has been computed. PyTorch Pretrained BERT: The Big & Extending Repository of pretrained Transformers This repository contains op-for-op PyTorch reimplementations, pre-trained models and fine-tuning examples for: Google's BERT model, OpenAI's GPT model, Google/CMU's Transformer-XL model, and OpenAI's GPT-2 model. SCIBERT follows the same architecture as BERT but is instead pretrained on scientific text." I'm trying to understand how to train the model on two tasks as above. Transformer XL use a relative positioning with sinusiodal patterns and adaptive softmax inputs which means that: This model takes as inputs: The same option as in the original scripts are provided, please refere to the code of the example and the original repository of OpenAI. than the models internal embedding lookup matrix. Inputs are the same as the inputs of the TransfoXLModel class plus optional labels: Outputs a tuple of (last_hidden_state, new_mems). max_position_embeddings (int, optional, defaults to 512) The maximum sequence length that this model might ever be used with. value (nn.Module) A module mapping vocabulary to hidden states. the hidden-states output) e.g. A BERT sequence pair mask has the following format: if token_ids_1 is None, only returns the first portion of the mask (0s). GLUE data by running Here is a quick-start example using OpenAIGPTTokenizer, OpenAIGPTModel and OpenAIGPTLMHeadModel class with OpenAI's pre-trained model. refer to the TF 2.0 documentation for all matter related to general usage and behavior. The TFBertForSequenceClassification forward method, overrides the __call__() special method. layer weights are trained from the next sentence prediction (classification) Use it as a regular TF 2.0 Keras Model and if target is None: log probabilities of tokens, shape [batch_size, sequence_length, n_tokens], else: Negative log likelihood of target tokens with shape [batch_size, sequence_length]. never_split (Iterable, optional, defaults to None) Collection of tokens which will never be split during tokenization. gradient_checkpointing (bool, optional, defaults to False) If True, use gradient checkpointing to save memory at the expense of slower backward pass. It is used to instantiate an BERT model according to the specified arguments, defining the model the tokens in the vocabulary have to be sorted to decreasing frequency. This tokenizer inherits from PreTrainedTokenizer which contains most of the methods. BertAdam is a torch.optimizer adapted to be closer to the optimizer used in the TensorFlow implementation of Bert. all the tensors in the first argument of the model call function: model(inputs). Users The differences with BertAdam is that OpenAIAdam compensate for bias as in the regular Adam optimizer. TFBertForQuestionAnswering.from_pretrained()BERT . from_pretrained ("bert-base-cased", num_labels = 3) model = BertForSequenceClassification. input_ids (Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length)) , attention_mask (Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length), optional, defaults to None) , token_type_ids (Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length), optional, defaults to None) , position_ids (Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length), optional, defaults to None) , labels (tf.Tensor of shape (batch_size,), optional, defaults to None) Labels for computing the multiple choice classification loss. # Step 1: Save a model, configuration and vocabulary that you have fine-tuned, # If we have a distributed model, save only the encapsulated model, # (it was wrapped in PyTorch DistributedDataParallel or DataParallel), # If we save using the predefined names, we can load using `from_pretrained`, # Step 2: Re-load the saved model and vocabulary. This example code evaluate the pre-trained Transformer-XL on the WikiText 103 dataset. modeling.py. list of input IDs with the appropriate special tokens. The token-level classifier is a linear layer that takes as input the last hidden state of the sequence. All experiments were run on a P100 GPU with a batch size of 32. Chapter 2. google. This could be the symptom of proxies parameter not being passed through the request package commands. refer to the TF 2.0 documentation for all matter related to general usage and behavior. Positions are clamped to the length of the sequence (sequence_length). pad_token (string, optional, defaults to [PAD]) The token used for padding, for example when batching sequences of different lengths. (if set to False) for evaluation. You can convert any TensorFlow checkpoint for BERT (in particular the pre-trained models released by Google) in a PyTorch save file by using the convert_tf_checkpoint_to_pytorch.py script. The results of the tests performed on pytorch-BERT by the NVIDIA team (and my trials at reproducing them) can be consulted in the relevant PR of the present repository. For our sentiment analysis task, we will perform fine-tuning using the BertForSequenceClassification model class from HuggingFace transformers package. usage and behavior. It becomes increasingly difficult to ensure . This model is a PyTorch torch.nn.Module sub-class. Apr 25, 2019 pretrained_model_config 1 . OpenAIGPTLMHeadModel includes the OpenAIGPTModel Transformer followed by a language modeling head with weights tied to the input embeddings (no additional parameters). The model can behave as an encoder (with only self-attention) as well the BERT bert-base-uncased architecture.

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