XLM | PyTorch original implementation of Cross-lingual Language | Natural Language Processing library
kandi X-RAY | XLM Summary
kandi X-RAY | XLM Summary
NEW: Added [XLM-R] model. PyTorch original implementation of [Cross-lingual Language Model Pretraining] Includes: - [Monolingual language model pretraining (BERT)] #i-monolingual-language-model-pretraining-bert) - [Cross-lingual language model pretraining (XLM)] #ii-cross-lingual-language-model-pretraining-xlm) - [Applications: Supervised / Unsupervised MT (NMT / UNMT)] #iii-applications-supervised—unsupervised-mt) - [Applications: Cross-lingual text classification (XNLI)] #iv-applications-cross-lingual-text-classification-xnli) - [Product-Key Memory Layers (PKM)] #v-product-key-memory-layers-pkm). XLM supports multi-GPU and multi-node training, and contains code for: - Language model pretraining: - Causal Language Model (CLM) - Masked Language Model (MLM) - Translation Language Model (TLM) - GLUE fine-tuning - XNLI fine-tuning - Supervised / Unsupervised MT training: - Denoising auto-encoder - Parallel data training - Online back-translation.
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Top functions reviewed by kandi - BETA
- Builds the command line parser .
- Generates a batch of sentences .
- Initialize distributed mode .
- Evaluate and return the evaluation .
- Check parameters .
- Registers the command line arguments .
- Builds a model for training .
- Evaluate the clm .
- Entry point for the experiment .
- Check parameters for correctness .
XLM Key Features
XLM Examples and Code Snippets
Lines of Code : 71License : Non-SPDX (NOASSERTION)
Copydata_bin=/data2/mmyin/XLM-experiments/data-bin/xlm-data-bin/zh-en-ldc-32k
export CUDA_VISIBLE_DEVICES=1,2,3,4
export NGPU=4
python -m torch.distributed.launch --nproc_per_node=$NGPU train.py \
--exp_name Supervised_MT \
--exp_id LDC_ch-en_n 
License : No LicenseCopy -h, --help show this help message and exit
--data_dir DATA_DIR The input data dir. Should contain the .tsv files (or
other data files) for the task.
--pretrained_path PRETRAINED_PATH
p export PARAM_SET=base # change to large to use the large architecture
# clone the repo
git clone https://github.com/mohammadKhalifa/xlm-roberta-ner.git
cd xlm-roberta-ner/
mkdir pretrained_models
wget -P pretrained_models https://dl.fbaipublicfiles """
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 XLM
QUESTION
I want to display the designated data that is found for a particular code match. I have a data set that will come in model. I want if the data-set, subject property has the first 2-3 characters found in it, to display the corresponding name. Based on the first 3 characters begins with LA_, which is found in the first index, only the first set of content should appear (Name: Library Arts Department: ACSF-LA Identifier: 6774). I know i would need to slice the character off, with string slice, but what if sometimes the name has like LAX_ (SO I want to be sure to check if the subjects have any that match--). So basically to check everything before the first "_"
...ANSWER
Answered 2022-Apr-10 at 23:39Create a computed property that uses Array.prototype.filter on the todos[]. The callback to filter() receives each array item, and returns true if the item should be in the result. In this callback, you can check if each item contains the leading characters (before the underscore) in the search string (LA in your example):
QUESTION
I'm using symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli pretrained model from huggingface. My task requires to use it on pretty large texts, so it's essential to know maximum input length.
The following code is supposed to load pretrained model and its tokenizer:
...ANSWER
Answered 2022-Apr-01 at 11:06Model_max_length is the maximum length of positional embedding the model can take. To check this, do
print(model.config)
you'll see "max_position_embeddings": 512 along with other configs.
how I can check the maximum input length for my model?
You can pass the max_length(as much as your model can take) when you're encoding the text sequences:
tokenizer.encode(txt, max_length=512)
QUESTION
I have a state which looks like this.
...ANSWER
Answered 2022-Mar-25 at 08:45Issue is a stale closure over the currencies state. Use a functional state update to correctly update from the previous state instead of the initial state closed over in callback scope.
Example:
QUESTION
I'm scraping website and come to the part where to put it in Dataframe. I tried to follow this answer but no expected output.
Here's my whole code
...ANSWER
Answered 2022-Feb-11 at 03:13Some how coin_name is twice as long as your other lists. Once you fix that you can do this:
QUESTION
When reading an XLSM file with pandas I'm getting the following error:
...ANSWER
Answered 2022-Jan-28 at 12:02Alright I found the solution. For anyone who has the same problem: Upgrade openpyxl!
QUESTION
I have the following layout code in my xlm, to call my recycler view in my status fragment
...ANSWER
Answered 2022-Jan-21 at 19:56I think you only forgot to declare the mySwipeToRefresh element. This is the corrected code, I implemented it inside an Activity and it triggers the myUpdateOperation() function fine.
QUESTION
Any idea why this query returns the error "SQL Error (207): Invalid column name 'BTC'"?
I'm just trying to use the WHERE clause after the JOIN staement
...ANSWER
Answered 2022-Jan-17 at 14:21You appear to be using the incorrect text qualifier in your WHERE clause - the double-quotes indicate an identifier, not a value. In other words, your WHERE clause is written in a way that SQL Server is trying to find an equality between two columns, rather than a column equal to a value.
Change your code so that your WHERE clause reads
WHERE balance_BTC.Currency = 'BTC'; and you should find that the error is resolved.
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
To make it easy to visualize, below is the following Record lookup table.
I just can't seem to find anywhere online where it tells you which of these are supposed to also contain charset=utf-8.
Should I just assume it's anything similar to text?
Take a look:
...ANSWER
Answered 2022-Jan-10 at 05:00MDN Says:
For example, for any MIME type whose main type is text, you can add the optional charset parameter to specify the character set used for the characters in the data. If no charset is specified, the default is ASCII (US-ASCII) unless overridden by the user agent's settings. To specify a UTF-8 text file, the MIME type text/plain;charset=UTF-8 is used.
So, for anything based on text/... you can optionally add the charset.
https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/MIME_types#structure_of_a_mime_type
The following update to contentType() function demonstrates one solution.
QUESTION
I want to predict the sentiment of thousands of sentences using huggingface.
...ANSWER
Answered 2022-Jan-07 at 19:52Simply add tokenizer arguments when you init the pipeline.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install XLM
To download the data required for the unsupervised MT experiments, simply run:.
Follow a similar approach than in section 1 for the 15 languages:. Downloading the Wikipedia dumps make take several hours. The get-data-wiki.sh script will automatically download Wikipedia dumps, extract raw sentences, clean and tokenize them. Note that in our experiments we also concatenated the [Toronto Book Corpus](http://yknzhu.wixsite.com/mbweb) to the English Wikipedia, but this dataset is no longer hosted. For Chinese and Thai you will need a special tokenizer that you can install using the commands below. For all other languages, the data will be tokenized with Moses scripts.
This script will download and tokenize the parallel data used for the TLM objective:.
This script will download and tokenize the XNLI corpus:.
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