d-bert | Distilling BERT using natural language generation | Natural Language Processing library
kandi X-RAY | d-bert Summary
kandi X-RAY | d-bert Summary
Distilling BERT using natural language generation.
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Top functions reviewed by kandi - BETA
- Convert the given examples into a list of features
- Truncate a sequence pair
- Evaluate the model
- Ingest the model
- Perform batch prediction
- Predict given texts
- Augment texts using BERT encoding
- Compute the BERT query
- Run a single step
- Generate a sequence of tokens
- Compute the embedding
- Fetch embedding
- Sample from the corpus
- Loads checkpoint
- Balance multiple buffers
- Creates a function that returns a function that increments checkpoint loss
- Extract tensors from text
- Ingest the matrix
- Forward embedding
- Bert - encoder
- Convert a sequence of tokens into tokens
- Evaluate a given model
- Generate prompt
- Forward the embedding
- Sample a query
- Encodes the given list of queries
- Generate a sentence
d-bert Key Features
d-bert Examples and Code Snippets
Community Discussions
Trending Discussions on d-bert
QUESTION
I am trying to run a Custom Training Job in Google Cloud Platform's Vertex AI Training service.
The job is based on a tutorial from Google that fine-tunes a pre-trained BERT model (from HuggingFace).
When I use the gcloud CLI tool to auto-package my training code into a Docker image and deploy it to the Vertex AI Training service like so:
ANSWER
Answered 2022-Mar-01 at 08:34The image size shown in the UI is the virtual size of the image. It is the compressed total image size that will be downloaded over the network. Once the image is pulled, it will be extracted and the resulting size will be bigger. In this case, the PyTorch image's virtual size is 6.8 GB while the actual size is 17.9 GB.
Also, when a docker push command is executed, the progress bars show the uncompressed size. The actual amount of data that’s pushed will be compressed before sending, so the uploaded size will not be reflected by the progress bar.
To cut down the size of the docker image, custom containers can be used. Here, only the necessary components can be configured which would result in a smaller docker image. More information on custom containers here.
QUESTION
I'm new to BERT QA model & was trying to follow the example found in this article. The problem is when I run the code attached to the example it produces a Type error as follows TypeError: argmax(): argument 'input' (position 1) must be Tensor, not str.
Here is the code that I've tried running :
...ANSWER
Answered 2021-Aug-21 at 07:45So after referring to the BERT Documentation we identified that the model output object contains multiple properties not only start & end scores. Thus, we applied the following changes to the code.
QUESTION
My question here is no how to add new tokens, or how to train using a domain-specific corpus, I'm already doing that.
The thing is, am I supposed to add the domain-specific tokens before the MLM training, or I just let Bert figure out the context? If I choose to not include the tokens, am I going to get a poor task-specific model like NER?
To give you more background of my situation, I'm training a Bert model on medical text using Portuguese language, so, deceased names, drug names, and other stuff are present in my corpus, but I'm not sure I have to add those tokens before the training.
I saw this one: Using Pretrained BERT model to add additional words that are not recognized by the model
But the doubts remain, as other sources say otherwise.
Thanks in advance.
...ANSWER
Answered 2021-Apr-17 at 14:01Yes, you have to add them to the models vocabulary.
QUESTION
I fine-tuned the BERT base model on my own dataset following the script here:
https://github.com/cedrickchee/pytorch-pretrained-BERT/tree/master/examples/lm_finetuning
I saved the model as a .pt file and I want to use it now for a sentence similarity task. Unfortunately, it is not clear to me, how to load the fine-tuned model. I tried the following:
ANSWER
Answered 2021-Mar-19 at 12:53To load a model with BertModel.from_pretrained() you need to have saved it using save_pretrained() (link).
Any other storage method would require the corresponding load. I am not familiar with S3, but I assume you can use get_object (link) to retrieve the model, and then save it using the huggingface api. From then on, you should be able to use from_pretrained() normally.
QUESTION
I am building a Docker container based on python:3.7-slim-stretch (same problem also happens on python:3.7-slim-stretch), and it is getting Killed on
ANSWER
Answered 2021-Feb-22 at 06:09I experience something similar on Windows when my docker containers run out of memory in WSL. I think the settings are different for Mac, but it looks like there is info here on setting the VM RAM/disk size/swap file settings for Docker for Desktop on Mac:
QUESTION
This might be a very strange question for most of you, but in this case I am grateful for an easy explanation. What confuses me is the following. Let's say I have a git repository such as the following:
https://github.com/cedrickchee/pytorch-pretrained-BERT
In the README they say, I can either install the repository with pip or from source. Within the repo, there are certain .py scripts that I want to use. What creates confusion to me is: How can I access those scripts when installing the repository with pip? I am talking about scripts like these:
ANSWER
Answered 2020-Nov-26 at 13:19Those scripts are examples. They are not installed even if you install from the git repository. To access them you need to clone the repository and copy the scripts out of examples directory.
QUESTION
I am doing the following operation,
...ANSWER
Answered 2020-Aug-27 at 06:07I took a look at your code (which by the way, didnt run with seq_len = 10) and the problem is that you hard coded the batch_size to be equal 1 (line 143) in your code.
It looks like the example you are trying to run the model on has batch_size = 2.
Just uncomment the previous line where you wrote batch_size = query.shape[0] and everything runs fine.
QUESTION
On my MacBook (version 10.14.6) I am succesfully running a Django application including TensorFlow and tf-sentencepiece (in particular to use the universal sentence encoder model). When I perform a pipenv lock -r > requirements.txt I get the following required packages:
ANSWER
Answered 2020-Jan-09 at 09:54I have no skills in Django, but it seems that tensorflow is trying to find a package (with a strange name) and failing.
I'd first suggest to try and fix your docker container setup, and check that pipenv lock -r yield the same result inside and outside your container.
1) as you said in the commentaries, on the host pc
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You can use d-bert 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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