blurr | integrates huggingface transformers with the world | Machine Learning library
kandi X-RAY | blurr Summary
kandi X-RAY | blurr Summary
A library that integrates huggingface transformers with version 2 of the fastai framework.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of blurr
blurr Key Features
blurr Examples and Code Snippets
Community Discussions
Trending Discussions on Machine Learning
QUESTION
I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.
I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.
I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.
...ANSWER
Answered 2022-Feb-17 at 10:47You should try to export the model using torch.onnx. The page gives you an example that you can start with.
An alternative is to use TorchScript, but that requires torch libraries.
Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html
ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)
A running exampleJust modifying a little your example to go over the errors I found
Notice that via tracing any if/elif/else, for, while will be unrolled
QUESTION
I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.
I'm using MNIST dataset.
...ANSWER
Answered 2022-Jan-14 at 23:47Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over Flux.params(model) is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.
Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:
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
I am trying to train a model using PyTorch. When beginning model training I get the following error message:
RuntimeError: CUDA out of memory. Tried to allocate 5.37 GiB (GPU 0; 7.79 GiB total capacity; 742.54 MiB already allocated; 5.13 GiB free; 792.00 MiB reserved in total by PyTorch)
I am wondering why this error is occurring. From the way I see it, I have 7.79 GiB total capacity. The numbers it is stating (742 MiB + 5.13 GiB + 792 MiB) do not add up to be greater than 7.79 GiB. When I check nvidia-smi I see these processes running
ANSWER
Answered 2021-Nov-23 at 06:13This is more of a comment, but worth pointing out.
The reason in general is indeed what talonmies commented, but you are summing up the numbers incorrectly. Let's see what happens when tensors are moved to GPU (I tried this on my PC with RTX2060 with 5.8G usable GPU memory in total):
Let's run the following python commands interactively:
QUESTION
I am a bit confusing with comparing best GridSearchCV model and baseline.
For example, we have classification problem.
As a baseline, we'll fit a model with default settings (let it be logistic regression):
ANSWER
Answered 2021-Nov-04 at 21:17No, they aren't comparable.
Your baseline model used X_train to fit the model. Then you're using the fitted model to score the X_train sample. This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen.
The grid searched model is at a disadvantage because:
- It's working with less data since you have split the
X_trainsample. - Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of
X_valper fold).
So your score for the grid search is going to be worse than your baseline.
Now you might ask, "so what's the point of best_model.best_score_? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context.
So how should one go about conducting a fair comparison?
- Split your training data for both models.
QUESTION
I am not able to access jupyter lab created on google cloud
I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.
I tried the diagnostic tool, which gave the following result:
but didn't fix it.
Thanks in advance.
...ANSWER
Answered 2021-Aug-20 at 14:00You should try this Google Notebook trouble shooting section about 524 errors : https://cloud.google.com/notebooks/docs/troubleshooting?hl=ja#opening_a_notebook_results_in_a_524_a_timeout_occurred_error
QUESTION
I am new to Machine Learning.
Having followed the steps in this simple Maching Learning using the Brain.js library, it beats my understanding why I keep getting the error message below:
I have double-checked my code multiple times. This is particularly frustrating as this is the very first exercise!
Kindly point out what I am missing here!
Find below my code:
...ANSWER
Answered 2021-Sep-29 at 22:47Turns out its just documented incorrectly.
In reality the export from brain.js is this:
QUESTION
IF we are not sure about the nature of categorical features like whether they are nominal or ordinal, which encoding should we use? Ordinal-Encoding or One-Hot-Encoding? Is there a clearly defined rule on this topic?
I see a lot of people using Ordinal-Encoding on Categorical Data that doesn't have a Direction. Suppose a frequency table:
...ANSWER
Answered 2021-Sep-04 at 06:43You're right. Just one thing to consider for choosing OrdinalEncoder or OneHotEncoder is that does the order of data matter?
Most ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases e.g., for ordered categories such as:
quality = ["bad", "average", "good", "excellent"]orshirt_size = ["large", "medium", "small"]
but it is obviously not the case for the:
color = ["white","orange","black","green"]
column (except for the cases you need to consider a spectrum, say from white to black. Note that in this case, white category should be encoded as 0 and black should be encoded as the highest number in your categories), or if you have some cases for example, say, categories 0 and 4 may be more similar than categories 0 and 1. To fix this issue, a common solution is to create one binary attribute per category (One-Hot encoding)
QUESTION
I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language
by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.
code:
...ANSWER
Answered 2021-Aug-10 at 07:39Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.
Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.
If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.
QUESTION
I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.
Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?
Table example:
...ANSWER
Answered 2021-Aug-11 at 15:55You could build a model like this.
x = features you have. y = true_lable
from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install blurr
raising victor vargas : a review < br / > < br / > you know, raising victor vargas is like sticking your hands into a big, steaming bowl of oatmeal. it's warm and gooey, but you're not sure if it feels right. try as i might, no matter how warm and gooey raising victor vargas became i was always aware that something didn't quite feel right. victor vargas suffers from a certain overconfidence on the director's part. apparently, the director thought that the ethnic backdrop of a latino family on the lower east side, and an idyllic storyline would make the film critic proof. he was right, but it didn't fool me. raising victor vargas is the story about a seventeen - year old boy called, you guessed it, victor vargas ( victor rasuk ) who lives his teenage years chasing more skirt than the rolling stones could do in all the years they've toured. the movie starts off in ` ugly fat'donna's bedroom where victor is sure to seduce her, but a cry from outside disrupts his plans when his best - friend harold ( kevin rivera ) comes - a - looking for him. caught in the attempt by harold and his sister, victor vargas runs off for damage control. yet even with the embarrassing implication that he's been boffing the homeliest girl in the neighborhood, nothing dissuades young victor from going off on the hunt for more fresh meat. on a hot, new york city day they make way to the local public swimming pool where victor's eyes catch a glimpse of the lovely young nymph judy ( judy marte ), who's not just pretty, but a strong and independent too. the relationship that develops between victor and judy becomes the focus of the film. the story also focuses on victor's family that is comprised of his grandmother or abuelita ( altagracia guzman ), his brother nino ( also played by real life brother to victor, silvestre rasuk ) and his sister vicky ( krystal rodriguez ). the action follows victor between scenes with judy and scenes with his family. victor tries to cope with being an oversexed pimp - daddy, his feelings for judy and his grandmother's conservative catholic upbringing. < br / > < br / > the problems that arise from raising victor vargas are a few, but glaring errors. throughout the film you get to know certain characters like vicky, nino, grandma, judy and even. many neglect that this isn't just a classic due to the fact that it's the first 3d game, or even the first shoot -'em - up. it's also one of the first stealth games, one of the only ( and definitely the first ) truly claustrophobic games, and just a pretty well - rounded gaming experience in general. with graphics that are terribly dated today, the game thrusts you into the role of b. j. ( don't even * think * i'm going to attempt spelling his last name! ), an american p. o. w. caught in an underground bunker. you fight and search your way through tunnels in order to achieve different objectives for the six episodes ( but, let's face it, most of them are just an excuse to hand you a weapon, surround you with nazis and send you out to waste one of the nazi leaders ). the graphics are, as i mentioned before, quite dated and very simple. the least detailed of basically any 3d game released by a professional team of creators. if you can get over that, however ( and some would suggest that this simplicity only adds to the effect the game has on you ), then you've got one heck of a good shooter / sneaking game. the game play consists of searching for keys, health and ammo, blasting enemies ( aforementioned nazis, and a " boss enemy " per chapter ) of varying difficulty ( which, of course, grows as you move further in the game ), unlocking doors and looking for secret rooms. there is a bonus count after each level is beaten... it goes by how fast you were ( basically, if you beat the'par time ', which is the time it took a tester to go through the same level ; this can be quite fun to try and beat, and with how difficult the levels are to find your way in, they are even challenging after many play - throughs ), how much nazi gold ( treasure ) you collected and how many bad guys you killed. basically, if you got 100 % of any of aforementioned, you get a bonus, helping you reach the coveted high score placings. the game ( mostly, but not always ) allows for two contrastingly different methods of playing... stealthily or gunning down anything and everything you see. you can either run or walk, and amongst your weapons is also a knife... running is heard instantly the moment you enter the same room as the guard, as.
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
