natural-language-processing

Natural language processing (NLP) is a field of computer science that studies how computers and humans interact. In the 1950s, Alan Turing published an article that proposed a measure of intelligence, now called the Turing test. More modern techniques, such as deep learning, have produced results in the fields of language modeling, parsing, and natural-language tasks.

Model description

Swin Transformer V2: Scaling Up Capacity and Resolution

repo origin: Swin Transformer V2

repo timm: Swin Transformer V2

all the model the pretrain is ready

Open sourc

Change tensor.data to tensor.detach() due to
pytorch/pytorch#6990 (comment)
tensor.detach() is more robust than tensor.data.

Although the results look nice and ideal in all TensorFlow plots and are consistent across all frameworks, there is a small difference (more of a consistency issue). The result training loss/accuracy plots look like they are sampling on a lesser number of points. It looks more straight and smooth and less wiggly as compared to PyTorch or MXNet.

It can be clearly seen in chapter 6([CNN Lenet](ht

Describe the bug

Streaming Datasets can't be pickled, so any interaction between them and multiprocessing results in a crash.

Steps to reproduce the bug

import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets

ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True).with_format("

In gensim/models/fasttext.py:

    model = FastText(
        vector_size=m.dim,
        vector_size=m.dim,
        window=m.ws,
        window=m.ws,
        epochs=m.epoch,
        epochs=m.epoch,
        negative=m.neg,
        negative=m.neg,
        # FIXME: these next 2 lines read in unsupported FB FT modes (loss=3 softmax or loss=4 onevsall,
        # or model=3 supervi

Is your feature request related to a problem? Please describe.
I typically used compressed datasets (e.g. gzipped) to save disk space. This works fine with AllenNLP during training because I can write my dataset reader to load the compressed data. However, the predict command opens the file and reads lines for the Predictor. This fails when it tries to load data from my compressed files.

Checking the Python files in NLTK with "python -m doctest" reveals that many tests are failing. In many cases, the failures are just cosmetic discrepancies between the expected and the actual output, such as missing a blank line, or unescaped linebreaks. Other cases may be real bugs.

If these failures could be avoided, it would become possible to improve CI by running "python -m doctest" each t

Some ideas for figures to add to the PPT

Apr	MAY	Jun
	17
2021	2022	2023

natural-language-processing

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huggingface / transformers

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Open sourc

d2l-ai / d2l-zh

google-research / bert

GokuMohandas / MadeWithML

hankcs / HanLP

explosion / spaCy

sebastianruder / NLP-progress

eugeneyan / applied-ml

oxford-cs-deepnlp-2017 / lectures

ShusenTang / Dive-into-DL-PyTorch

RasaHQ / rasa

d2l-ai / d2l-en

huggingface / datasets

Describe the bug

Steps to reproduce the bug

RaRe-Technologies / gensim

flairNLP / flair

allenai / allennlp

nltk / nltk

graykode / nlp-tutorial

chiphuyen / stanford-tensorflow-tutorials

kmario23 / deep-learning-drizzle

stanfordnlp / CoreNLP

ludwig-ai / ludwig

clips / pattern

sloria / TextBlob

lazyprogrammer / machine_learning_examples

languagetool-org / languagetool

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stanfordnlp / stanza