xlnet | Generalized Autoregressive Pretraining for Language | Natural Language Processing library
kandi X-RAY | xlnet Summary
kandi X-RAY | xlnet Summary
XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context. Overall, XLNet achieves state-of-the-art (SOTA) results on various downstream language tasks including question answering, natural language inference, sentiment analysis, and document ranking.
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Top functions reviewed by kandi - BETA
- Convert examples to features
- Encode pieces into pieces
- Preprocess text
- Encode piece ids
- Augment model_fn
- Enqueue infeed ops and dequeue them
- Get the object
- Invoke input_fn on each host
- Preprocess training data
- Reads a squad example file
- Create training data
- Construct input_fn
- Build a file - based input function
- Calculate precision - recall curve
- Runs predictions on the model
- Initialize TPU
- Process a single feature
- Wrap the computation in a while loop
- Return the ids of the given text
- Get examples from a set of examples
- Read a squad example file
- Create examples from QNLI
- Configure TPU
- Inserts a stopping signal
- Call input_fn
- Writes predictions to file
- Transformer transformer
- Train the model
xlnet Key Features
xlnet Examples and Code Snippets
Lines of Code : 228License : Permissive (MIT)
Copypython ensemble_squad.py \
--name ensemble-weighted \
--model_type placeholder \
--model_name_or_path placeholder \
--do_train \
--do_eval \
--do_weighted_ensemble \
--do_lower_case \
--train_file data/train-v2.0.json \
--per_gpu_tr export SQUAD_DIR=/path/to/SQUAD
python run_squad.py \
--model_type bert \
--model_name_or_path bert-base-cased \
--do_train \
--do_eval \
--do_lower_case \
--train_file $SQUAD_DIR/train-v1.1.json \
--predict_file $SQUAD_DIR/dev-v1.1.js CUDA_VISIBLE_DEVICES=0,1 python run.py \
--model_type xlnet \
--model_name_or_path xlnet-large-cased \
--do_test \
--do_train \
--do_eval \
--data_dir data \
--output_dir xlnet_large_commonsenseQA \
--max_seq_length 256 \
--eval_steps 200 \
--per_gpu """
The script shows how to train Augmented SBERT (Domain-Transfer/Cross-Domain) strategy for STSb-QQP dataset.
For our example below we consider STSb (source) and QQP (target) datasets respectively.
Methodology:
Three steps are followed for AugSBER """
The script shows how to train Augmented SBERT (In-Domain) strategy for STSb dataset with BM25 sampling.
We utlise easy and practical elasticsearch (https://www.elastic.co/) for BM25 sampling.
Installations:
For this example, elasticsearch to be """
The script shows how to train Augmented SBERT (In-Domain) strategy for STSb dataset with Semantic Search Sampling.
Methodology:
Three steps are followed for AugSBERT data-augmentation strategy with Semantic Search -
1. Fine-tune cross-enco Community Discussions
Trending Discussions on xlnet
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'm using Spacy-Transformers to build some NLP models.
The Spacy-Transformers docs say:
spacy-transformers
spaCy pipelines for pretrained BERT, XLNet and GPT-2
The sample code on that page shows:
...ANSWER
Answered 2021-Aug-28 at 05:16The en_core_web_trf uses a specific Transformers model, but you can specify arbitrary ones using the TransformerModel wrapper class from spacy-transformers. See the docs for that. An example config:
QUESTION
I am using huggingface pipeline to extract embeddings of words in a sentence. As far as I know, first a sentence will be turned into a tokenized strings. I think the length of the tokenized string might not be equal to the number of words in the original sentence. I need to retrieve word embedding of a particular sentence.
For example, here is my code:
...ANSWER
Answered 2021-Aug-18 at 08:10As you may know, huggingface tokenizer contains frequent subwords as well as complete ones. So if you are willing to extract word embeddings for some tokens you should consider that may contain more than one vector! In addition, huggingface pipelines encode input sentences at the first steps and this would be performed by adding special tokens to beginning & end of the actual sentence.
QUESTION
I am trying XLnet over Jigsaw toxic dataset.
When I train my data with
...ANSWER
Answered 2021-Aug-06 at 08:21I think the problem is that the training dataset's d['input_ids'] was of size 4*512 = 2048 so it could be divided into 4 and 512.
But the testing dataset's d['input_ids'] is of size 1024, which cannot be divided into 4 and 512.
Since you haven't given the model description, i can't say if you should change it to (-1, 512) or (4, -1) [using -1 in reshape tells numpy to figure that dimension out automatically.
e.g. reshaping an array of 2048 elements into (4, 512) can be done by reshape(4,512) and reshape(-1, 512) and reshape(4, -1) as well.
QUESTION
I am trying to use XLNET through transformers. however i keep getting the issue "AttributeError: 'NoneType' object has no attribute 'tokenize'". I am unsure of how to proceed. if anyone could point me in the right direction it would be appreciated.
...ANSWER
Answered 2021-Jun-01 at 11:20I assume that:
QUESTION
I'm trying to do something like this in XLNet but I can't find this part in the documentation, any help would be valuable, thanks!
...ANSWER
Answered 2021-May-17 at 21:16To solve this, let's first see what exactly hides behind the bert.bert property. For this, we can inspect the source code of the library for the TFBertModel class. There, we can see that it is defined as
QUESTION
Following this link, I am trying to use my own data to do sentiment analysis. But I get this error:
...ANSWER
Answered 2021-Feb-01 at 05:28However you haven't posted your sample data but it is evident that how you are using your reshape function. With respect to your question to reshape d["input_ids"] into shape (4,64) then d["input_ids"] should be an array of size 256 but actually in your dataset which you are feeding the model with is of size 64
So you need to reshape d["input_ids"] with something like (1,64) or (2,32) or (4,16) etc as per how your data is, whose multiple is 64.
Just illustrate the same:
QUESTION
I want to train an XLNET language model from scratch. First, I have trained a tokenizer as follows:
...ANSWER
Answered 2021-Feb-20 at 16:21Instead of
QUESTION
I am trying to train an XLNET model as the following. I want to set the hyperparameters by myself without using any pretrained models.
...ANSWER
Answered 2021-Feb-18 at 12:33You should use a TFRecord dataset instead of a text file.
QUESTION
I am using H2O DAI 1.9.0.6. I am tring to load custom recipe (BERT pretained model using custom recipe) on Expert settings. I am using local file to upload. However upload is not happning. No error, no progress nothing. After that activity I am not able to see this model under RECIPE tab.
Took Sample Recipe from below URL and Modified for my need. Thanks for the person who created this Recipe.
https://github.com/h2oai/driverlessai-recipes/blob/master/models/nlp/portuguese_bert.py
Custom Recipe
...ANSWER
Answered 2021-Feb-08 at 02:28Check that your custom recipe has is_enabled() returning True.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install xlnet
You can use xlnet 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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