transformer | Pytorch Implementation of Attention is All | Translation library
kandi X-RAY | transformer Summary
kandi X-RAY | transformer Summary
This repository includes pytorch implementations of "Attention is All You Need" (Vaswani et al., NIPS 2017) and "Weighted Transformer Network for Machine Translation" (Ahmed et al., arXiv 2017).
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Top functions reviewed by kandi - BETA
- Translate a batch
- Get the hypothesis for k k
- Compute the tensative hypothesis
- Advance the score
- Train the model
- Return a list of trainable parameters to trainable_model
- Project a probability matrix into a probability matrix
- Project the gradients of the model
- Forward computation
- Get the prior mask
- Compute the padding mask for a PAD
- Load test data
- Reads examples from src_path
- Convert index to text
- Return a list of trainable parameters that are used in trainable_model
- Builds a vocabulary
- Create a Transformer object
- Evaluate a model
- Read examples from src and tgt
- Load training data
- Returns the best score and index
transformer Key Features
transformer Examples and Code Snippets
Lines of Code : 106License : Non-SPDX (Apache License 2.0)
Copydef __init__(self,
input_shape,
dilation_rate,
padding,
build_op,
filter_shape=None,
spatial_dims=None,
data_format=None,
num_batc def _contrib_layers_l2_regularizer_transformer(
parent, node, full_name, name, logs):
"""Replace slim l2 regularizer with Keras one, with l=0.5*scale.
Also drops the scope argument.
"""
def _replace_scale_node(parent, old_value):
""" def __init__(self,
init_args,
init_func,
next_func,
finalize_func,
output_signature,
name=None):
"""Constructs a `_GeneratorDataset`.
Args:
init_ Community Discussions
Trending Discussions on transformer
QUESTION
I have updated node today and I'm getting this error:
ANSWER
Answered 2021-Oct-27 at 17:19Ran into the same issue with Node.js 17.0.0. To solve it, I downgraded to version 14.18.1, deleted node_modules and reinstalled.
QUESTION
I learned that you can redefine ContT from transformers such that the r type parameter is made implicit (and may be specified explicitly using TypeApplications), viz.:
ANSWER
Answered 2022-Feb-01 at 19:28Nobody uses this (invisible dependent quantification) for this purpose (where the dependency is not used) but it is the same as giving a Type -> .. parameter, implicitly.
QUESTION
I found that Reader is implemented based on ReaderT using Identity. Why don't make Reader first and then make ReaderT? Is there specific reason to implement that way?
ANSWER
Answered 2022-Jan-11 at 17:11They are the same data type to share as much code as possible between Reader and ReaderT. As it stands, only runReader, mapReader, and withReader have any special cases. And withReader doesn't have any unique code, it's just a type specialization, so only two functions actually do anything special for Reader as opposed to ReaderT.
You might look at the module exports and think that isn't buying much, but it actually is. There are a lot of instances defined for ReaderT that Reader automatically has as well, because it's the same type. So it's actually a fair bit less code to have only one underlying type for the two.
Given that, your question boils down to asking why Reader is implemented on top of ReaderT, and not the other way around. And for that, well, it's just the only way that works.
Let's try to go the other direction and see what goes wrong.
QUESTION
Given this type alias:
...ANSWER
Answered 2022-Jan-08 at 08:22Typescript types only exist at compile time. They do not exist in the compiled javascript. Thus you cannot populate an array (a runtime entity) with compile-time data (such as the RequestObject type alias), unless you do something complicated like the library you found.
- code something yourself that works like the library you found.
- find a different library that works with type aliases such as
RequestObject. - create an interface equivalent to your type alias and pass that to the library you found, e.g.:
QUESTION
After migrating from Remark to MDX, my builds on Netlify are failing.
I get this error when trying to build:
...ANSWER
Answered 2022-Jan-08 at 07:21The problem is that you have Node 17.2.0. locally but in Netlify's environment, you are running a lower version (by default it's not set as 17.2.0). So the local environment is OK, Netlify environment is KO because of this mismatch of Node versions.
When Netlify deploys your site it installs and builds again your site so you should ensure that both environments work under the same conditions. Otherwise, both node_modules will differ so your application will have different behavior or eventually won't even build because of dependency errors.
You can easily play with the Node version in multiple ways but I'd recommend using the .nvmrc file. Just run the following command in the root of your project:
QUESTION
Given an sklearn tranformer t, is there a way to determine whether t changes columns/column order of any given input dataset X, without applying it to the data?
For example with t = sklearn.preprocessing.StandardScaler there is a 1-to-1 mapping between the columns of X and t.transform(X), namely X[:, i] -> t.transform(X)[:, i], whereas this is obviously not the case for sklearn.decomposition.PCA.
A corollary of that would be: Can we know, how the columns of the input will change by applying t, e.g. which columns an already fitted sklearn.feature_selection.SelectKBest chooses.
I am not looking for solutions to specific transformers, but a solution applicable to all or at least a wide selection of transformers.
Feel free to implement your own Pipeline class or wrapper if necessary.
...ANSWER
Answered 2021-Nov-23 at 15:01I found a partial answer. Both StandardScaler and SelectKBest have .get_feature_names_out methods. I did not find the time to investigate further.
QUESTION
So I was trying to convert my data's timestamps from Unix timestamps to a more readable date format. I created a simple Java program to do so and write to a .csv file, and that went smoothly. I tried using it for my model by one-hot encoding it into numbers and then turning everything into normalized data. However, after my attempt to one-hot encode (which I am not sure if it even worked), my normalization process using make_column_transformer failed.
...ANSWER
Answered 2021-Dec-09 at 20:59using OneHotEncoder is not the way to go here, it's better to extract the features from the column time as separate features like year, month, day, hour, minutes etc... and give these columns as input to your model.
QUESTION
We can create a model from AutoModel(TFAutoModel) function:
...ANSWER
Answered 2021-Dec-05 at 09:07The difference between AutoModel and AutoModelForSequenceClassification model is that AutoModelForSequenceClassification has a classification head on top of the model outputs which can be easily trained with the base model
QUESTION
This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.
BackgroundI would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.
Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.
After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?
An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image
...ANSWER
Answered 2021-Nov-24 at 13:26What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true and y_pred.
QUESTION
Given a Zero-Shot Classification Task via Huggingface as follows:
...ANSWER
Answered 2021-Oct-22 at 21:51The ZeroShotClassificationPipeline is currently not supported by shap, but you can use a workaround. The workaround is required because:
- The shap Explainer forwards only one parameter to the model (a pipeline in this case), but the ZeroShotClassificationPipeline requires two parameters, namely text, and labels.
- The shap Explainer will access the config of your model and use its
label2idandid2labelproperties. They do not match the labels returned from the ZeroShotClassificationPipeline and will result in an error.
Below is a suggestion for one possible workaround. I recommend opening an issue at shap and requesting official support for huggingface's ZeroShotClassificationPipeline.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install transformer
You can use transformer like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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