allennlp pretrained model

Takes a list of model parameters with associated names typically coming from something like model. This means separating the parameters into groups with the given regexes, and prepping whatever keyword arguments are given for those regexes in groups. The return value in the right format to be passed directly as the params argument to a pytorch Optimizer.

If there are multiple groups specified, this is list of dictionaries, where each dict contains a "parameter group" and groups specific options, e. Any config option not specified in the additional options e. The dictionary's return type is labeled as Anybecause it can be a List[torch.

Parameter] for the "params" keyor anything else typically a float for the other keys. This class just allows us to implement Registrable for Pytorch Optimizers. We do something a little bit different with Optimizersbecause they are implemented as classes in PyTorch, and we want to use those classes. To make things easy, we just inherit from those classes, using multiple inheritance to also inherit from Optimizer.

The only reason we do this is to make type inference on parameters possible, so we can construct these objects using our configuration framework.

allennlp pretrained model

If you are writing your own script, you can safely ignore these classes and just use the torch. Skip to content. Parameter ]].GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Branch: master. Find file Copy path. Cannot retrieve contributors at this time. Raw Blame History. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.

Models in the main repo. SemanticRoleLabelerPredictor :. Semantic Role Labeling. ReadingComprehensionPredictor :. Reading Comprehension. OpenIePredictor :.

Pretrained Transformers Improve Out-of-Distribution Robustness

DecomposableAttentionPredictor :. Textual Entailment. CorefPredictor :. Coreference Resolution. SentenceTaggerPredictor :. Named Entity Recognition.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Branch: master. Find file Copy path.

Cannot retrieve contributors at this time. Raw Blame History. We also add special tokens relative to the pretrained model and truncate the sequences. The value of this argument defines the number of additional tokens. Detecting it this way seems like the least brittle way to do it. This code attempts to calculate character offsets while being tolerant to these differences.

It scans through the text and the tokens in parallel, trying to match up positions in both. If it gets out of sync, it backs off to not adding any token indices, and attempts to catch back up afterwards.

This procedure is approximate. Don't rely on precise results, especially in non-English languages that are far more affected by Unicode normalization. Something is wrong. Ignore this token. This function inserts special tokens. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. This often means wordpieces.Although pretrained Transformers such as BERT achieve high accuracy on in-distribution examples, do they generalize to new distributions?

We systematically measure out-of-distribution OOD generalization for various NLP tasks by constructing a new robustness benchmark with realistic distribution shifts.

pretrained_transformer_tokenizer

Pretrained transformers are also more effective at detecting anomalous or OOD examples, while many previous models are frequently worse than chance. We examine which factors affect robustness, finding that larger models are not necessarily more robust, distillation can be harmful, and more diverse pretraining data can enhance robustness. Finally, we show where future work can improve OOD robustness. The train and test distributions are often not identically distributed.

Chasing an evolving data distribution is costly, and even if the training data does not become stale, models will still encounter unexpected situations at test time.

Most evaluation in natural language processing NLP assumes the train and test examples are independent and identically distributed IID.

In the IID setting, large pretrained Transformer models can attain near human-level performance on numerous tasks Wang et al. Moreover, pretrained Transformers can rely heavily on spurious cues and annotation artifacts Cai et al.

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To measure OOD generalization, we create a new evaluation benchmark that tests robustness to shifts in writing style, topic, and vocabulary, and spans the tasks of sentiment analysis, textual entailment, question answering, and semantic similarity. Moreover, we demonstrate that while pretraining larger models does not seem to improve OOD generalization, pretraining models on diverse data does improve OOD generalization.

To measure OOD detection performance, we turn classifiers into anomaly detectors by using their prediction confidences as anomaly scores Hendrycks and Gimpel In contrast, pretrained Transformers are far more capable at OOD detection. Overall, our results highlight that while there is room for future robustness improvements, pretrained Transformers are already moderately robust. We evaluate OOD generalization with seven carefully selected datasets.

Each dataset either 1 contains metadata which allows us to naturally split the samples or 2 can be paired with a similar dataset from a distinct data generating process.

By splitting or grouping our chosen datasets, we can induce a distribution shift and measure OOD generalization.

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We train on one dataset and evaluate on the other dataset, and vice versa. The Yelp Review Dataset contains restaurant reviews with detailed metadata e. We carve out four groups from the dataset based on food type: American, Chinese, Italian, and Japanese. We sample 50, reviews for each category. We also utilize these datasets for semantic similarity, reading comprehension, and textual entailment:.

The dataset contains text of different genres and sources; we use four sources from two genres: MSRpar newsHeadlines news ; MSRvid captionsImages captions. We select examples from two genres of transcribed text Telephone and Face-to-Face and one genre of written text Lettersand we report classification accuracy.

We evaluate NLP models with different input representations and encoders. We investigate three model categories with a total of thirteen models. For classification tasks, the representation from the encoder is fed into an MLP.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

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Sign in to your account. This is an initializer that I've been using that initializes some weights to a pretrained model. It addresses This general approach seems reasonable to me, though there are a few details I'd quibble with I can give those later. Ok, then there might be some details that I don't understand yet. As far as I can tell, you would only need 1 copy of this initializer per InitializerApplicator and per weights file.

As it's written now, you can initialize multiple parameters of the model with just 1 instance. Yeah, the problem is that each entry for the InitializerApplicator instantiates its own Initializerso this would get instantiated multiple times, unless you have a really nasty regex.

But, looks like there's general agreement that this is a good idea, so danieldeutschgo ahead and add tests and docs and such.

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I chose that name because I wanted it to be clear that it's optional and only necessary when you need to change the name of the parameters. My particular use case was that I trained a model with MLE and then fine tuned with a minimum risk loss function, so none of the parameters' names changed. I don't understand why this is happening, and looking at the logic inside of Initializer.

Also, I'm pretty sure the custom Initializer.

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Lines to in e3e8e1c. It sure looks like we already handle that case; the original Initializer. Then, when cls is PretrainedModelInitializerbefore it pops the type from the params, it first calls cls. Within this method, it checks to ensure that the default implementation is registered.

Since the default implementation for PretrainedModelInitializer does not exist, it uses Initializerwhich is "normal". But because this is not registered for PretrainedModelInitializerit raises a ConfigurationError here. That's unfortunateDeep contextualized word representations Matthew E. NAACL ELMo is a deep contextualized word representation that models both 1 complex characteristics of word use e.

These word vectors are learned functions of the internal states of a deep bidirectional language model biLMwhich is pre-trained on a large text corpus. They can be easily added to existing models and significantly improve the state of the art across a broad range of challenging NLP problems, including question answering, textual entailment and sentiment analysis. Salient features ELMo representations are: Contextual : The representation for each word depends on the entire context in which it is used.

Deep : The word representations combine all layers of a deep pre-trained neural network. Character based : ELMo representations are purely character based, allowing the network to use morphological clues to form robust representations for out-of-vocabulary tokens unseen in training. In most cases, they can be simply swapped for pre-trained GloVe or other word vectors.

We do not include GloVe vectors in these models to provide a direct comparison between ELMo representations - in some cases, this results in a small drop in performance 0. All models except for the 5. The ELMo 5. In tasks where we have made a direct comparison, the 5. Introduction ELMo is a deep contextualized word representation that models both 1 complex characteristics of word use e. We maintain a list of models here but are unable to respond to quality issues ourselves.

Enkhbold Bataa, Joshua Wu. See the tutorial for usage instructions. The TensorFlow version is also available in bilm-tf.

optimizers

Training models You can retrain ELMo models using the tensorflow code in bilm-tf. More information See our paper Deep contextualized word representations for more information about the algorithm and a detailed analysis. ExaWizards Inc. Joel Grus and Brendan Roof.This abstract class represents a model to be trained.

Rather than relying completely on the Pytorch Module, we modify the output spec of forward to be a dictionary. Models built using this API are still compatible with other pytorch models and can be used naturally as modules within other models - outputs are dictionaries, which can be unpacked and passed into other layers. Sequentialyou must interleave the models with a wrapper module which unpacks the dictionary into a list of tensors. In order for your model to be trained using the Trainer api, the output dictionary of your Model must include a "loss" key, which will be optimised during the training process.

Finally, you can optionally implement Model. Computes the regularization penalty for the model. Returns 0 if the model was not configured to use regularization. Defines the forward pass of the model. In addition, to facilitate easy training, this method is designed to compute a loss function defined by a user. The input is comprised of everything required to perform a training update, including labels - you define the signature here!

It is down to the user to ensure that inference can be performed without the presence of these labels. Hence, any inputs not available at inference time should only be used inside a conditional block. Takes an Instancewhich typically has raw text in it, converts that text into arrays using this model's Vocabularypasses those arrays through self. Before returning the result, we convert any torch. Tensors into numpy arrays and remove the batch dimension. Takes a list of Instancesconverts that text into arrays using this model's Vocabularypasses those arrays through self.

Tensors into numpy arrays and separate the batched output into a list of individual dicts per instance. Takes the result of forward and makes it human readable.

allennlp pretrained model

Somtimes you'll also do an argmax or something in here, too, but that most often happens in Model. This method modifies the input dictionary, and also returns the same dictionary. Returns a dictionary of metrics. This method will be called by allennlp.

Trainer in order to compute and use model metrics for early stopping and model serialization. We return an empty dictionary here rather than raising as it is not required to implement metrics for a new model.

A boolean reset parameter is passed, as frequently a metric accumulator will have some state which should be reset between epochs. This is also compatible with Metric s. Metrics should be populated during the call to forwardwith the Metric handling the accumulation of the metric until this method is called.

Instantiates an already-trained model, based on the experiment configuration and some optional overrides. Iterates through all embedding modules in the model and assures it can embed with the extended vocab. Loads a model from an archive file. This basically just calls return archival.

It exists as a method here for convenience, and so that we can register it for easy use for fine tuning an existing model from a config file. Skip to content.

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Model class Model torch. Tensor ]. Tensor, defined by the user.