glove | Compute Glove vectors using a co-occurence matrix | Machine Learning library
kandi X-RAY | glove Summary
kandi X-RAY | glove Summary
Cython general implementation of the Glove multi-threaded training. GloVe is an unsupervised learning algorithm for generating vector representations for words. Training is done using a co-occcurence matrix from a corpus. The resulting representations contain structure useful for many other tasks.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Train the model .
- Initialize latent variables .
- read a file
glove Key Features
glove Examples and Code Snippets
Lines of Code : 22
License : No License
Copyimport glove
cooccur = {
0: {
0: 1.0,
2: 3.5
},
1: {
2: 0.5
},
2: {
0: 3.5,
1: 0.5,
2: 1.2
}
}
model = glove.Glove(cooccur, d=50, alpha=0.75, x_max=100.0)
for epoch in range(25):
err = model.train(batch_size=200, workers=9, b Community Discussions
Trending Discussions on glove
QUESTION
I'm designing the mechanics behind a RPG. There are classes for Item, Player, NPC, etc. The Player class has attributes inventory and equipment. Equipment is a list of dictionaries, such as:
...ANSWER
Answered 2022-Mar-31 at 04:17Is it safe, efficient, and reliable to pass an entire object as a value?
Yes! Everything in Python is an object.
If I'm correct, this is the print function returning an address in memory denoting the object. Does this represent any issues? ... is the print function returning an address in memory denoting the object. Does this represent any issues?
No issues here. It depends entirely on the __repr__ overrides of the class. If it doesn't have one, then the default implementation is to print out the id() of the object and its class type. E.g.
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 am facing the following attribute error when loading glove model:
Code used to load model:
...ANSWER
Answered 2022-Mar-17 at 14:08spacy version: 3.1.4 does not have the feature from_glove.
I was able to use nlp.vocab.vectors.from_glove() in spacy version: 2.2.4.
If you want, you can change your spacy version by using:
!pip install spacy==2.2.4 on your Jupyter cell.
QUESTION
I have a pose estimation model pretrained on a dataset in which hands are in its nartural color. I want to finetune that model on the dataset of hands of surgeons doing surgeries. Those hands are in surgical gloves so the image of the hands are a bit different than normal hands.
Does this difference in hand colors affect the model performance? If I can make images of those surgical hands more like normal hands, will I get better performance?
...ANSWER
Answered 2022-Jan-22 at 07:37Well, it depends on what your pre-trained model has learned to capture from the pre-training (initial) dataset. Suppose your model had many feature maps and not enough skin color variation in your pre-training dataset (leads to overfitting issues). In that case, your model has likely "taken the path of least resistance" and exploited that to learn feature maps that rely on the color space as means of feature extraction (which might not generalize well due to color differences).
The more your pre-training dataset match/overlap with your target dataset, the better the effects of transfer learning will be. So yes, there is a very high chance that making your target dataset (surgical hands) look more similar to your pre-training dataset will positively impact your model's performance. Moreover, I would conjecture that introducing some color variation (e.g., Color Jitter augmentation) in your pre-training dataset could also help your model generalize to your target dataset.
QUESTION
In the deprecated encoding method with tfds.deprecated.text.TokenTextEncoder We first create a vocab set of token
...ANSWER
Answered 2021-Dec-22 at 09:53Maybe try something like this:
QUESTION
Here is a dummy DataFrame of my data, I have categorical rows (represented by the existence of NaN value of 'Price') and data rows (represented by a non-NaN
value of 'Price').
ANSWER
Answered 2021-Oct-13 at 18:17Try mask the notna, then ffill to get the correct Sport:
QUESTION
I have a code like this
...ANSWER
Answered 2021-Sep-03 at 19:41I ran the code, but as I don't have the necessary tokenizer packages installed, I couldnt get that to run. Instead, I ran a simpler function below:
QUESTION
i'm trying to manipulate text from all spans under "critical-product-marquee-container" div using python, selenium and xpath selector.
...ANSWER
Answered 2021-Aug-23 at 09:15basically that's marquee in HTML5, so you have to explicitly wait for each elements.
Code :
QUESTION
I am using MOSI dataset for the multimodal sentiment analysis, where for now I am training the model for text dataset only. For text, I am using glove embeddings of 300 dimensions for processing text. My total vocab size is 2173 and my padded sequence length is 30. My target array is [0,0,0,0,0,0,1] where left most is highly -ve and right most highly +ve.
I am splitting the dataset like this
X_train, X_test, y_train, y_test = train_test_split(WDatasetX, y7, test_size=0.20, random_state=42)
My tokenization process is
...ANSWER
Answered 2021-Aug-11 at 19:22A large difference between Train and Validation stats typically indicates overfitting of models to the Train data.
To minimize this I do a few things
- reduce the size of the model.
- Add a few dropout or similar layers in the model. I have had good success with using these layers:
layers.LeakyReLU(alpha=0.8),
See guidance here: https://www.tensorflow.org/tutorials/keras/overfit_and_underfit#strategies_to_prevent_overfitting
QUESTION
I am using Word2Vec and using a wiki trained model that gives out the most similar words. I ran this before and it worked but now it gives me this error even after rerunning the whole program. I tried to take off return_path=True but im still getting the same error
ANSWER
Answered 2021-Aug-06 at 18:44You are probably looking for .wv.most_similar, so please try:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install glove
You can use glove 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.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
