DeBERTa | The implementation of DeBERTa | Natural Language Processing library

by microsoft Python Version: 0.1.12 License: MIT

X-Ray Key Features Code Snippets(5)Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | DeBERTa Summary

DeBERTa is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Pytorch, Tensorflow, Bert, Neural Network, Transformer applications. DeBERTa has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install DeBERTa' or download it from GitHub, PyPI.

DeBERTa (Decoding-enhanced BERT with disentangled attention) improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency of model pre-training and performance of downstream tasks.

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DeBERTa has a medium active ecosystem.

It has 1542 star(s) with 183 fork(s). There are 43 watchers for this library.

It had no major release in the last 12 months.

There are 51 open issues and 56 have been closed. On average issues are closed in 69 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of DeBERTa is 0.1.12

Quality

DeBERTa has 0 bugs and 0 code smells.

Security

DeBERTa has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

DeBERTa code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DeBERTa is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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DeBERTa releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

DeBERTa saves you 1882 person hours of effort in developing the same functionality from scratch.

It has 4150 lines of code, 383 functions and 46 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed DeBERTa and discovered the below as its top functions. This is intended to give you an instant insight into DeBERTa implemented functionality, and help decide if they suit your requirements.

Create an xoptimizer
Create an optimizer for an optimizer
Get world size
Build the argument parser
Train the model
Calculate the loss
Cleanup gradients
Set adversarial mode
Evaluate the prediction
Merge data_list into chunks
Compute the attention layer
Forward computation
Runs a prediction on a given model
The worker loop
Perform a single step
Set global logger
Run pre load hook
Perform forward computation
Apply pre - trained embedding
Work around worker manager
Tokenize a text file
Setup distributed group
Tokenize text
Decode a sequence of tokens
Loads a vocabulary
This tests the distribution
Set the logger

Get all kandi verified functions for this library.

DeBERTa Key Features

No Key Features are available at this moment for DeBERTa.

DeBERTa Examples and Code Snippets

WhiteningBERT,Usage

Python

Lines of Code : 20

License : Permissive (MIT)

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python evaluation_stsbenchmark.py \
			--pooling aver \
			--layer_num 1,12 \
			--whitening \
			--encoder_name bert-base-cased

python evaluation_stsbenchmark_layer2.py \
			--pooling aver \
			--whitening \
			--encoder_name bert-base-cased

pytho

Pretrained Cross-Encoders-NLI

Python

Lines of Code : 7

License : Permissive (Apache-2.0)

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from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely

CoNLL 2003 (English),experiments summary

Python

Lines of Code : 6

License : No License

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* GPU / CPU     : Elapsed time/example(ms), GPU / CPU, [Tesla V100 1 GPU, Intel(R) Xeon(R) Gold 5120 CPU @ 2.20GHz, 2 CPU, 14CORES/1CPU, HyperThreading]
* F1            : conll2003 / conll++
* (truecase) F1 : conll2003_truecase / conll++_truecase
* O

NER Classification Deberta Tokenizer error : You need to instantiate DebertaTokenizerFast

Python

Lines of Code : 8

License : Strong Copyleft (CC BY-SA 4.0)

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input_ids = [1, 31414, 6, 42, 16, 65, 3645, 328, 2]
input_ids  = ','.join(map(str, input_ids ))


input_ids = ["Hello", ",", "this", "is", "one", "sentence", "split", "into", "words", "."]
input_ids  = ','.join(map(str, input_ids ))
input_

ValueError: Unrecognized model in ./MRPC/. Should have a `model_type` key in its config.json, or contain one of the following strings in its name

Python

Lines of Code : 34

License : Strong Copyleft (CC BY-SA 4.0)

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import json

json_filename = './MRPC/config.json'

with open(json_filename) as json_file:
    json_decoded = json.load(json_file)

json_decoded['model_type'] = # !!

with open(json_filename, 'w') as json_file:
    json.dump(json_decoded, j

Community Discussions

Trending Discussions on DeBERTa

How to use the DeBERTa model by He et al. (2022) on Spyder?

NER Classification Deberta Tokenizer error : You need to instantiate DebertaTokenizerFast

ValueError: Unrecognized model in ./MRPC/. Should have a `model_type` key in its config.json, or contain one of the following strings in its name

How to use SciBERT in the best manner?

QUESTION

How to use the DeBERTa model by He et al. (2022) on Spyder?

Asked 2022-Apr-16 at 05:22

I have recently successfully analyzed text-based data using sentence transformers based on the BERT model. Inspired by the book by Kulkarni et al. (2022), my code looked like this:

...

ANSWER

Answered 2022-Apr-16 at 05:22

Welcome to SO ;) When you call encode() method it would tokenize the input then encode it to the tensors a transformer model expects, then pass it through model architecture. When you're using transformers you must do the steps manually.

Source https://stackoverflow.com/questions/71878447

QUESTION

NER Classification Deberta Tokenizer error : You need to instantiate DebertaTokenizerFast

Asked 2022-Jan-21 at 10:23

I'm trying to perform a NER Classification task using Deberta, but I'm stacked with a Tokenizer error. This is my code (my input sentence must be splitted word by word by ",:):

...

ANSWER

Answered 2022-Jan-21 at 10:23

Lets try this:

Source https://stackoverflow.com/questions/70799226

QUESTION

ValueError: Unrecognized model in ./MRPC/. Should have a `model_type` key in its config.json, or contain one of the following strings in its name

Asked 2022-Jan-13 at 14:10

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 14:10

Explanation:

When 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.

Solution:

Code that appends model_type to config.json, in the same format:

Source https://stackoverflow.com/questions/70697470

QUESTION

How to use SciBERT in the best manner?

Asked 2021-Oct-03 at 14:21

I'm trying to use BERT models to do text classification. As the text is about scientific texts, I intend to use the SicBERT pre-trained model: https://github.com/allenai/scibert

I have faced several limitations which I want to know if there is any solutions for them:

When I want to do tokenization and batching, it only allows me to use max_length of <=512. Is there any way to use more tokens. Doen't this limitation of 512 mean that I am actually not using all the text information during training? Any solution to use all the text?
I have tried to use this pretrained library with other models such as DeBERTa or RoBERTa. But it doesn't let me. I has only worked with BERT. Is there anyway I can do that?
I know this is a general question, but any suggestion that I can improve my fine tuning (from data to hyper parameter, etc)? Currently, I'm getting ~75% accuracy. Thanks

Codes:

...

ANSWER

Answered 2021-Oct-03 at 14:21

When I want to do tokenization and batching, it only allows me to use max_length of <=512. Is there any way to use more tokens. Doen't this limitation of 512 mean that I am actually not using all the text information during training? Any solution to use all the text?

Yes, you are not using the complete text. And this is one of the limitations of BERT and T5 models, which limit to using 512 and 1024 tokens resp. to the best of my knowledge.

I can suggest you to use Longformer or Bigbird or Reformer models, which can handle sequence lengths up to 16k, 4096, 64k tokens respectively. These are really good for processing longer texts like scientific documents.

I have tried to use this pretrained library with other models such as DeBERTa or RoBERTa. But it doesn't let me. I has only worked with BERT. Is there anyway I can do that?

SciBERT is actually a pre-trained BERT model. See this issue for more details where they mention the feasibility of converting BERT to ROBERTa:

Since you're working with a BERT model that was pre-trained, you unfortunately won't be able to change the tokenizer now from a WordPiece (BERT) to a Byte-level BPE (RoBERTa).

I know this is a general question, but any suggestion that I can improve my fine tuning (from data to hyper parameter, etc)? Currently, I'm getting ~79% accuracy.

I would first try to tune the most important hyperparameter learning_rate. I would then explore different values for hyperparameters of AdamW optimizer and num_warmup_steps hyperparamter of the scheduler.

Source https://stackoverflow.com/questions/69406937

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