electra | training Text Encoders as Discriminators Rather | Machine Learning library
kandi X-RAY | electra Summary
kandi X-RAY | electra Summary
ELECTRA is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a GAN. At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the SQuAD 2.0 dataset. For a detailed description and experimental results, please refer to our ICLR 2020 paper ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. This repository contains code to pre-train ELECTRA, including small ELECTRA models on a single GPU. It also supports fine-tuning ELECTRA on downstream tasks including classification tasks (e.g,. GLUE), QA tasks (e.g., SQuAD), and sequence tagging tasks (e.g., text chunking). This repository also contains code for Electric, a version of ELECTRA inspired by energy-based models. Electric provides a more principled view of ELECTRA as a "negative sampling" cloze model. It can also efficiently produce pseudo-likelihood scores for text, which can be used to re-rank the outputs of speech recognition or machine translation systems. For details on Electric, please refer to out EMNLP 2020 paper Pre-Training Transformers as Energy-Based Cloze Models.
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Top functions reviewed by kandi - BETA
- Transformer model transformer
- Attention layer
- Get the shape of a tensor
- Apply dropout to input tensor
- Feature extraction
- Check if the document spans with the given position
- Improve the explanation span of an answer span
- Embedding postprocessor
- Layer norm and dropout
- Ferts the given example
- Create an optimizer
- Creates attention mask from input tensors
- Get the prediction module
- Calculate precision - recall curve
- Write train examples
- Evaluate the prediction
- Calculates the classification results
- Returns a list of examples for the given split
- Compute the raw score for each prediction
- Train or eval pretraining
- Feature a single example
- Mask the input tensor
- Runs the Finetuning
- Create a pretraining
- Tokenize text
- Embed word embedding
electra Key Features
electra Examples and Code Snippets
Lines of Code : 25
License : No License
Copyif IOSSecuritySuite.amIJailbroken() {
print("This device is jailbroken")
} else {
print("This device is not jailbroken")
}
static func amIJailbrokenWithFailedChecks() -> (jailbroken: Bool, failedChecks: [FailedCheck]) {
let status = perform * note that F1 score from the 'seqeval' package for 'max_seq_len=50' might be similar with that for 'max_seq_len=180'.
however, the final evaluation using 'conlleval.pl' should be different.
for example, with n_ctx=50.
the F1 score from 's PYTHONHASHSEED=42 python run_ust.py
--task $DATA_DIR
--model_dir $OUTPUT_DIR
--seq_len 128
--sample_scheme easy_bald_class_conf
--sup_labels 60
--valid_split 0.5
--pt_teacher TFBertModel
--pt_teacher_checkpoint bert-base-uncased
--N_base 5
--su Community Discussions
Trending Discussions on electra
QUESTION
I have the following problem, I would like to sum up a column and divide the sum every line through the sum of the whole column till a specific value is reached. so in Pseudocode it would look like that:
...ANSWER
Answered 2022-Mar-06 at 21:25Perhaps I am missing your point but your subtotal will never be equal to 70 000 if you divide by the sum of its column. The maximum value will be 1. Your incremental sum however can be equal or superior to 70 000.
QUESTION
So I am involved in a project that involves feeding a combination of text embeddings and image vectors into a DNN to arrive at the result. Now for the word embedding part, I am using TFHUB's Electra while for the image part I am using a NASNet Mobile network.
However, the issue I am facing is that while running the word embedding part, using the code shown below, the code just keeps running nonstop. It has been over 2 hours now and my training dataset has just 14900 rows of tweets.
Note - The input to the function is just a list of 14900 tweets.
...ANSWER
Answered 2022-Jan-24 at 15:19The operation performed in the code is quadratic in its nature. While I managed to execute your snippet with 10000 samples within a few minutes, a 14900 long input ran out of memory on 32GB RAM runtime. Is it possible that your runtime is experiencing swapping?
It is not clear what is the snippet trying to achieve. Do you intend to train model? In such case you can define the text_input as an Input layer and use fit to train. Here is an example: https://www.tensorflow.org/text/tutorials/classify_text_with_bert#define_your_model
QUESTION
Goal: Amend this Notebook to work with Albert and Distilbert models
Kernel: conda_pytorch_p36. I did Restart & Run All, and refreshed file view in working directory.
Error occurs in Section 1.2, only for these 2 new models.
For filenames etc., I've created a variable used everywhere:
...ANSWER
Answered 2022-Jan-13 at 14:10When instantiating AutoModel, you must specify a model_type parameter in ./MRPC/config.json file (downloaded during Notebook runtime).
List of model_types can be found here.
Code that appends model_type to config.json, in the same format:
QUESTION
I have this script and I would like to print a single title before executing the conditional if
My code
...ANSWER
Answered 2021-Dec-27 at 16:05Since there was no input example, I used your "Output I have" as input.
I also checked if the whole line contains the word terror or bird, but you can change it if you need the column where it is.
QUESTION
I have several masked language models (mainly Bert, Roberta, Albert, Electra). I also have a dataset of sentences. How can I get the perplexity of each sentence?
From the huggingface documentation here they mentioned that perplexity "is not well defined for masked language models like BERT", though I still see people somehow calculate it.
For example in this SO question they calculated it using the function
...ANSWER
Answered 2021-Dec-25 at 21:51There is a paper Masked Language Model Scoring that explores pseudo-perplexity from masked language models and shows that pseudo-perplexity, while not being theoretically well justified, still performs well for comparing "naturalness" of texts.
As for the code, your snippet is perfectly correct but for one detail: in recent implementations of Huggingface BERT, masked_lm_labels are renamed to simply labels, to make interfaces of various models more compatible. I have also replaced the hard-coded 103 with the generic tokenizer.mask_token_id. So the snippet below should work:
QUESTION
I want to do LDA (linear discriminant analysis) with the Auto dataset of the ISLR package. To start off, I am trying to take the cars with year = 75 and use it as a "test set", where cars of all other years will be used as a "training set". However, it seems that I've made a mess of things. For instance, in my code below, sequentially using the replace function for the values of mpg.year75 just results in everything being set to high:
ANSWER
Answered 2021-Sep-24 at 07:02The issue is in these 3 lines.
QUESTION
I am trying to use the rename() function of the dplyr package to change the variable mpg to mpgclass:
ANSWER
Answered 2021-Sep-23 at 07:08rename works for me, perhaps you have a function conflict with another package. Try using dplyr::rename.
To change the columns based on range of values you may use case_when or cut.
QUESTION
Not sure why but my code is getting the following error:
...ANSWER
Answered 2021-Sep-02 at 07:20The exception comes from a Django internal middleware, since it's trying to process your returned qs as a response.
You'll need to return a response, not just a queryset, e.g. this simple example to return a list of user IDs.
QUESTION
I have trained an electra model from scratch using google implementation code.
...ANSWER
Answered 2021-May-28 at 15:14It seems that @npit is right. The output of the convert_electra_original_tf_checkpoint_to_pytorch.py does not contain the configuration that I gave (hparams.json), therefore I created an ElectraConfig object -- with the same parameters -- and provided it to the from_pretrained function. That solved the issue.
QUESTION
I know that polr does not give p-values because they are not very reliable. Nevertheless, I would like to add them to my modelsummary (Vignette) output. I know to get the values as follows:
ANSWER
Answered 2021-May-05 at 13:12I think the easiest way to achieve this is to define a tidy_custom.polr method as described here in the documentation.. For instance, you could do:
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