embeddings | Knowledge Base Embeddings for DBpedia | Graph Database library
kandi X-RAY | embeddings Summary
kandi X-RAY | embeddings Summary
Knowledge Graph Embeddings for DBpedia.
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Top functions reviewed by kandi - BETA
- Process the contents of the XML dump
- Load templates from a file
- Return True if there is no page in the namespace
- Reserve the given size
- Return a list of pages from a string
- Compute the scores for each test
- Returns the prediction for the given indices
- Given a set of test ids return a list of arguments
- Process jobs queue
- Extract magic words
- Compute the similarity of the entity
- Encoder function
- Performs a sharp switch
- Train the model
- Reduce the process of a process
- Generate embeddings
- Create mapping of resources and descriptions
- Generate a list of pages from a string
- Load templates from file
- Count the number of pronouns
- Normalize title
- This function is called when the function is called
- Callback function
- Creates a dict of anchor text
- Count the number of pronouns in a file
- Replace anchor text in a file
- Extract the magic words
- Replace anchor text in file
embeddings Key Features
embeddings Examples and Code Snippets
Lines of Code : 64
License : No License
Copyimport torch
from vit_pytorch.vit import ViT
v = ViT(
image_size = 256,
patch_size = 32,
num_classes = 1000,
dim = 1024,
depth = 6,
heads = 16,
mlp_dim = 2048,
dropout = 0.1,
emb_dropout = 0.1
)
# import Recorder def safe_embedding_lookup_sparse(embedding_weights,
sparse_ids,
sparse_weights=None,
combiner="mean",
default_id=None, def pad_sparse_embedding_lookup_indices(sparse_indices, padded_size):
"""Creates statically-sized Tensors containing indices and weights.
From third_party/cloud_tpu/models/movielens/tpu_embedding.py
Also computes sparse_indices.values % embed def visualize(self, visual_fld, num_visualize):
""" run "'tensorboard --logdir='visualization'" to see the embeddings """
# create the list of num_variable most common words to visualize
word2vec_utils.most_common_wor Community Discussions
Trending Discussions on embeddings
QUESTION
In a model with an embedding layer and SimpleRNN layer, I would like to compute the partial derivative dh_t/dh_0 for each step t.
The structure of my model, including imports and data preprocessing.
Toxic comment train data available: https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification/data?select=jigsaw-toxic-comment-train.csv
GloVe 6B 100d embeddings available: https://nlp.stanford.edu/projects/glove/
ANSWER
Answered 2022-Feb-18 at 14:02You could maybe try using tf.gradients. Also rather use tf.Variable for h0:
QUESTION
I have been doing some NLP categorisation tasks and noticed that my models train much faster if I use post-padding instead of pre-padding, and was wondering why that is the case.
I am using Google Colab to train these model with the GPU runtime. Here is my preprocessing code:
...ANSWER
Answered 2022-Mar-20 at 12:56This is related to the underlying LSTM implementation. There are in fact two: A "native Tensorflow" one and a highly optimized pure CUDA implementation which is MUCH faster. However, the latter can only be used under specific conditions (certain parameter settings etc.). You can find details in the docs. The main point here is:
Inputs, if use masking, are strictly right-padded.
This implies that the pre-padding version does not use the efficient implementation, which explains the much slower runtime. I don't think there is a reasonable workaround here except for sticking with post-padding.
Note that sometimes, Tensorflow actually outputs a warning message that it had to use the inefficient implementation. However, for me this has been inconsistent. Maybe keep your eyes out if any additional warning outputs are produced in the pre-padding case.
QUESTION
I am having trouble when switching a model from some local dummy data to using a TF dataset.
Sorry for the long model code, I have tried to shorten it as much as possible.
The following works fine:
...ANSWER
Answered 2022-Mar-10 at 08:57You will have to explicitly set the shapes of the tensors coming from tf.py_functions. Using None will allow variable input lengths. The Bert output dimension (384,) is, however, necessary:
QUESTION
I have created a class for word2vec vectorisation which is working fine. But when I create a model pickle file and use that pickle file in a Flask App, I am getting an error like:
AttributeError: module
'__main__'has no attribute 'GensimWord2VecVectorizer'
I am creating the model on Google Colab.
Code in Jupyter Notebook:
...ANSWER
Answered 2022-Feb-24 at 11:48Import GensimWord2VecVectorizer in your Flask Web app python file.
QUESTION
Currently i'm able to train a Semantic Role Labeling model using the config file below. This config file is based on the one provided by AllenNLP and works for the default bert-base-uncased model and also GroNLP/bert-base-dutch-cased.
ANSWER
Answered 2022-Feb-24 at 02:14The easiest way to resolve this is to patch SrlReader so that it uses PretrainedTransformerTokenizer (from AllenNLP) or AutoTokenizer (from Huggingface) instead of BertTokenizer. SrlReader is an old class, and was written against an old version of the Huggingface tokenizer API, so it's not so easy to upgrade.
If you want to submit a pull request in the AllenNLP project, I'd be happy to help you get it merged into AllenNLP!
QUESTION
I am trying to create an NLP neural-network using the following code:
imports:
...ANSWER
Answered 2022-Feb-13 at 11:58The TextVectorization layer is a preprocessing layer that needs to be instantiated before being called. Also as the docs explain:
The vocabulary for the layer must be either supplied on construction or learned via adapt().
Another important information can be found here:
Crucially, these layers are non-trainable. Their state is not set during training; it must be set before training, either by initializing them from a precomputed constant, or by "adapting" them on data
Furthermore, it is important to note, that the TextVectorization layer uses an underlying StringLookup layer that also needs to be initialized beforehand. Otherwise, you will get the FailedPreconditionError: Table not initialized as you posted.
QUESTION
I'm trying to use GridSearchCV to find the best hyperparameters for an LSTM model, including the best parameters for vocab size and the word embeddings dimension. First, I prepared my testing and training data.
ANSWER
Answered 2022-Feb-02 at 08:53I tried with scikeras but I got errors because it doesn't accept not-numerical inputs (in our case the input is in str format). So I came back to the standard keras wrapper.
The focal point here is that the model is not built correctly. The TextVectorization must be put inside the Sequential model like shown in the official documentation.
So the build_model function becomes:
QUESTION
I want to perfom similarity search using FAISS for 100k facial embeddings in C++.
For the distance calculator I would like to use cosine similarity. For this purpose, I choose faiss::IndexFlatIP .But according to the documentation we need to normalize the vector prior to adding it to the index. The documentation suggested the following code in python:
ANSWER
Answered 2022-Jan-31 at 11:15You can build and use the C++ interface of Faiss library (see this).
If you just want L2 normalization of a vector in C++:
QUESTION
I'm trying to implement a neural network to generate sentences (image captions), and I'm using Pytorch's LSTM (nn.LSTM) for that.
The input I want to feed in the training is from size batch_size * seq_size * embedding_size, such that seq_size is the maximal size of a sentence. For example - 64*30*512.
After the LSTM there is one FC layer (nn.Linear).
As far as I understand, this type of networks work with hidden state (h,c in this case), and predict the next word each time.
My question is- in the training - do we have to manually feed the sentence word by word to the LSTM in the forward function, or the LSTM knows how to do it itself?
My forward function looks like this:
...ANSWER
Answered 2022-Jan-02 at 19:24The answer is, LSTM knows how to do it on its own. You do not have to manually feed each word one by one.
An intuitive way to understand is that the shape of the batch that you send, contains seq_length (batch.shape[1]), using which it decides the number of words in the sentence. The words are passed through LSTM Cell generating the hidden states and C.
QUESTION
I'm following step by step the Vespa tutorials: https://docs.vespa.ai/en/tutorials/news-5-recommendation.html
ANSWER
Answered 2021-Dec-14 at 10:36The Vespa index has no user documents here, so most likely the user and news embeddings have not been fed to the system. After they are calculated in the previous step (https://docs.vespa.ai/en/tutorials/news-4-embeddings.html), be sure to feed them to Vespa:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install embeddings
You can use embeddings 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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