mlm | web application framework with expressive , elegant syntax

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mlm is a HTML library. mlm has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

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Community Discussions

Trending Discussions on mlm

How to train BERT from scratch on a new domain for both MLM and NSP?

Python regex: Extract volume (mL) from strings

PipelineException: No mask_token ([MASK]) found on the input

RuntimeError: Input, output and indices must be on the current device. (fill_mask("Random text .")

Is there any TF implementation of the Original BERT other than Google and HuggingFace?

BERT - Is that needed to add new tokens to be trained in a domain specific environment?

Recursive optim() function in R causes errors

Huggingface error: AttributeError: 'ByteLevelBPETokenizer' object has no attribute 'pad_token_id'

Looping through columns to analyse different dependent variable

Using functions and lapply to make properly labelled histograms

QUESTION

How to train BERT from scratch on a new domain for both MLM and NSP?

Asked 2021-Jun-01 at 14:42

I’m trying to train BERT model from scratch using my own dataset using HuggingFace library. I would like to train the model in a way that it has the exact architecture of the original BERT model.

In the original paper, it stated that: “BERT is trained on two tasks: predicting randomly masked tokens (MLM) and predicting whether two sentences follow each other (NSP). SCIBERT follows the same architecture as BERT but is instead pretrained on scientific text.”

I’m trying to understand how to train the model on two tasks as above. At the moment, I initialised the model as below:

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ANSWER

Answered 2021-Feb-10 at 14:04

I would suggest doing the following:

First pre-train BERT on the MLM objective. HuggingFace provides a script especially for training BERT on the MLM objective on your own data. You can find it here. As you can see in the run_mlm.py script, they use AutoModelForMaskedLM, and you can specify any architecture you want.
Second, if want to train on the next sentence prediction task, you can define a BertForPretraining model (which has both the MLM and NSP heads on top), then load in the weights from the model you trained in step 1, and then further pre-train it on a next sentence prediction task.

UPDATE: apparently the next sentence prediction task did help improve performance of BERT on some GLUE tasks. See this talk by the author of BERT.

Source https://stackoverflow.com/questions/65646925

QUESTION

Python regex: Extract volume (mL) from strings

Asked 2021-May-17 at 14:10

I have the following string to extract volume (match only ml, not mg/ml)

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ANSWER

Answered 2021-May-17 at 13:10

You can use

Source https://stackoverflow.com/questions/67570152

QUESTION

PipelineException: No mask_token ([MASK]) found on the input

Asked 2021-May-12 at 22:45

I am getting this error "PipelineException: No mask_token ([MASK]) found on the input" when I run this line. fill_mask("Auto Car .")

I am running it on Colab. My Code:

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ANSWER

Answered 2021-May-12 at 22:45

Even if you have already found the error, a recommendation to avoid it in the future. Instead of calling

Source https://stackoverflow.com/questions/67511800

QUESTION

RuntimeError: Input, output and indices must be on the current device. (fill_mask("Random text .")

Asked 2021-May-12 at 19:58

I am getting "RuntimeError: Input, output and indices must be on the current device." when I run this line. fill_mask("Auto Car .")

I am running it on Colab. My Code:

...

ANSWER

Answered 2021-May-12 at 19:58

The trainer trains your model automatically at GPU (default value no_cuda=False). You can verify this by running:

Source https://stackoverflow.com/questions/67496616

QUESTION

Is there any TF implementation of the Original BERT other than Google and HuggingFace?

Asked 2021-May-08 at 07:52

Trying to find any Tensorflow/Keras implementation of the original BERT model trained using MLM/NSP. The official google and HuggingFace implementations are very complex and has so much of added functionalities. But I want to learn and implement BERT for just learning its working.

Any leads will be helpful?

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ANSWER

Answered 2021-May-08 at 07:52

As mentioned in the comment, you can try the following implementation of MLP-BERT TensorFlow. It's a simplified version and easy to follow comparatively.

Source https://stackoverflow.com/questions/67429425

QUESTION

BERT - Is that needed to add new tokens to be trained in a domain specific environment?

Asked 2021-Apr-17 at 14:01

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.

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ANSWER

Answered 2021-Apr-17 at 14:01

Yes, you have to add them to the models vocabulary.

Source https://stackoverflow.com/questions/67058709

QUESTION

Recursive optim() function in R causes errors

Asked 2021-Apr-05 at 20:17

I am trying to use the optim() function in R to minimize a value with matrix operations. In this case, I am trying to minimize the volatility of a group of stocks whose individual returns covary with each other. The objective function being minimized is calculate_portfolio_variance.

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ANSWER

Answered 2021-Apr-05 at 20:17

No error occurs if the first argument is renamed par and you switch the order in which you apply t() to the parameter vectors used in that flanking matrix-multiply operation:

Source https://stackoverflow.com/questions/66937457

QUESTION

Huggingface error: AttributeError: 'ByteLevelBPETokenizer' object has no attribute 'pad_token_id'

Asked 2021-Mar-27 at 16:25

I am trying to tokenize some numerical strings using a WordLevel/BPE tokenizer, create a data collator and eventually use it in a PyTorch DataLoader to train a new model from scratch.

However, I am getting an error

AttributeError: 'ByteLevelBPETokenizer' object has no attribute 'pad_token_id'

when running the following code

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ANSWER

Answered 2021-Mar-27 at 16:25

The error tells you that the tokenizer needs an attribute called pad_token_id. You can either wrap the ByteLevelBPETokenizer into a class with such an attribute (... and met other missing attributes down the road) or use the wrapper class from the transformers library:

Source https://stackoverflow.com/questions/66824985

QUESTION

Looping through columns to analyse different dependent variable

Asked 2021-Mar-19 at 17:19

Here my data frame (reproducible example)

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ANSWER

Answered 2021-Mar-19 at 16:56

We can use a loop. Subset the column names i.e. column names that starts with 'VD' followed by some digis, then loop over those 'nm1', create a formula with paste, apply lmer and get the summary

Source https://stackoverflow.com/questions/66712325

QUESTION

Using functions and lapply to make properly labelled histograms

Asked 2021-Mar-12 at 22:34

I'm interested in Using functions and lapply to make properly labelled histograms.

But when I try to use a function and lapply to create histograms that display the spread of data, the xlab doesn't give the text of the variable of interest. Instead it uses the first value of the variable of interest. How to I fix this issue?

The code I used is below:

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ANSWER

Answered 2021-Mar-12 at 22:34

You're passing the data vector to xlab so it just truncates it to the first value. You want to pass a string.

Modify your function to take a label value and then use mapply

Source https://stackoverflow.com/questions/66606350

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