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:page_facing_up: A PyTorch implementation of Paragraph Vectors (doc2vec).

License: MIT License

Python 100.00%

deep-learning doc2vec machine-learning neural-networks nlp pytorch unsupervised-learning

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paragraph-vectors's Issues

Implement the inference stage

I.e. training in which only the paragraph matrix is updated (word vectors are pre-trained and frozen), as described in Q. V. Le et al., Distributed Representations of Sentences and Documents.

Prior word vector from word2vec or glove

I would like to volunteer for the implementation of "prior word vector from word2vec or glove"
I think the library could use the pre-trained word vector as an initialization instead of just random vector.

Run batch generation on multiple cores/CPUs

Batch generator (paragraphvec.data.NCEGenerator) is implemented in a thread-safe manner (edit: needs fixing). The training procedure should take advantage of this (i.e. simultaneously construct new batches on CPU while the model is trained on a GPU).

Implement subsampling of frequent words

A subsampling approach described in Distributed Representations of Words and Phrases and their Compositionality, T. Mikolov et al.

Fix multiprocessing code coverage

Coverage of data.py, which uses multiprocessing is incorrect. Currently, data.py is simply ignored.

AttibuteError on calling train

PyTorch and CUDA versions

torch version : 0.1.9_2.
cuda is not installed.

Description

I am running the train.py as it is mentioned in the description with the "example.csv" file and the same parameters. I am running into

AttributeError: 'module' object has no attribute 'HalfTensor'

Additional info (stack trace, etc.)

Traceback (most recent call last):
File "train.py", line 8, in
from paragraphvec.data import load_dataset, NCEData
File "/home/prathmesh/paragraph-vectors/paragraphvec/data.py", line 11, in
from torchtext.data import Field, TabularDataset
File "/usr/local/lib/python2.7/dist-packages/torchtext/init.py", line 1, in
from . import data
File "/usr/local/lib/python2.7/dist-packages/torchtext/data/init.py", line 4, in
from .field import RawField, Field, ReversibleField, SubwordField
File "/usr/local/lib/python2.7/dist-packages/torchtext/data/field.py", line 61, in
class Field(RawField):
File "/usr/local/lib/python2.7/dist-packages/torchtext/data/field.py", line 117, in Field
torch.HalfTensor: float,
AttributeError: 'module' object has no attribute 'HalfTensor'

can Ichange dataset to pickle file

I have tested this project in windows 10 , I am very interested in your field of deep learning by pytorch and Doc2vec
I am running Python 3.10. On Windows 10.

Description

My issue is in data.py
Is it possible to use the dataset file in Pickle format instead of the text file in CSV format?

Thanks!

Implement the PV-DBOW version of the model

Implement the Distributed Bag of Words version of the model as proposed by Q. V. Le et al., Distributed Representations of Sentences and Documents.

Benchmark the implementation

Compare the implementation with Genism's, identify bottlenecks.

File "paragraphvec/train.py", line 8, in <module> from paragraphvec.data import load_dataset, NCEData ModuleNotFoundError: No module named 'paragraphvec'

How should I fix this error?

[jalal@goku paragraph-vectors]$ python paragraphvec/train.py start --data_file_name 'data/example.csv' --num_epochs 10 --batch_size 32 --num_noise_words 2 --vec_dim 100 --lr 1e-3
Traceback (most recent call last):
  File "paragraphvec/train.py", line 8, in <module>
    from paragraphvec.data import load_dataset, NCEData
ModuleNotFoundError: No module named 'paragraphvec'

$ python -V
Python 3.6.4 :: Anaconda custom (64-bit)

Implement document vectors lookup

After the model is trained, it should be possible to easily get trained paragraph vectors based on document index (index of the row in the dataset).

TypeError in data.py - process.start()

PyTorch and CUDA versions

pytorch - 0.4.1
CUDA - 9.0

Description

I am having an issue with data.py. on line 119, process.start() yields the following error:

TypeError: 'generator' object is not callable. Could you please help?

Implement training diagnostics

Save loss value after each epoch and implement a function for visualization.

Sample Data, Parameters

Thanks for making this available. Would you happen to have some sample data and good initial value of the parameters to test this with?

Device-side assert related to out of bounds indexing when using CUDA

Hi! I run into an error when trying to train a model.
My PyTorch version is 0.2.0_4, and the cuda version is 8.0.
Can you give me some insights about what's happening? Here is the trace:

(paravec) [zhangsheng@bbox1 paragraphvec]$ python train.py start --data_file_name 'example.csv' --num_epochs 100 --batch_size 32 --context_size 4 --num_noise_words 5 --vec_dim 150 --lr 1e-4
Dataset comprised of 4 documents.
Vocabulary size is 109.

Training started.
Epoch 1 - 20%/opt/conda/conda-bld/pytorch_1503970438496/work/torch/lib/THC/THCTensorIndex.cu:378: long calculateOffset(IndexType, LinearIndexCalcData<IndexType, Dims>) [with IndexType = unsigned int, Dims = 2U]: block: [9,0,0], thread: [64,0,0] Assertion `indexAtDim < data.baseSizes[dim]` failed.
/opt/conda/conda-bld/pytorch_1503970438496/work/torch/lib/THC/THCTensorIndex.cu:378: long calculateOffset(IndexType, LinearIndexCalcData<IndexType, Dims>) [with IndexType = unsigned int, Dims = 2U]: block: [9,0,0], thread: [65,0,0] Assertion `indexAtDim < data.baseSizes[dim]` failed.
/opt/conda/conda-bld/pytorch_1503970438496/work/torch/lib/THC/THCTensorIndex.cu:378: long calculateOffset(IndexType, LinearIndexCalcData<IndexType, Dims>) [with IndexType = unsigned int, Dims = 2U]: block: [9,0,0], thread: [66,0,0] Assertion `indexAtDim < data.baseSizes[dim]` failed.
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/opt/conda/conda-bld/pytorch_1503970438496/work/torch/lib/THC/THCTensorIndex.cu:378: long calculateOffset(IndexType, LinearIndexCalcData<IndexType, Dims>) [with IndexType = unsigned int, Dims = 2U]: block: [9,0,0], thread: [121,0,0] Assertion `indexAtDim < data.baseSizes[dim]` failed.
/opt/conda/conda-bld/pytorch_1503970438496/work/torch/lib/THC/THCTensorIndex.cu:378: long calculateOffset(IndexType, LinearIndexCalcData<IndexType, Dims>) [with IndexType = unsigned int, Dims = 2U]: block: [9,0,0], thread: [122,0,0] Assertion `indexAtDim < data.baseSizes[dim]` failed.
/opt/conda/conda-bld/pytorch_1503970438496/work/torch/lib/THC/THCTensorIndex.cu:378: long calculateOffset(IndexType, LinearIndexCalcData<IndexType, Dims>) [with IndexType = unsigned int, Dims = 2U]: block: [9,0,0], thread: [123,0,0] Assertion `indexAtDim < data.baseSizes[dim]` failed.
/opt/conda/conda-bld/pytorch_1503970438496/work/torch/lib/THC/THCTensorIndex.cu:378: long calculateOffset(IndexType, LinearIndexCalcData<IndexType, Dims>) [with IndexType = unsigned int, Dims = 2U]: block: [9,0,0], thread: [124,0,0] Assertion `indexAtDim < data.baseSizes[dim]` failed.
/opt/conda/conda-bld/pytorch_1503970438496/work/torch/lib/THC/THCTensorIndex.cu:378: long calculateOffset(IndexType, LinearIndexCalcData<IndexType, Dims>) [with IndexType = unsigned int, Dims = 2U]: block: [9,0,0], thread: [125,0,0] Assertion `indexAtDim < data.baseSizes[dim]` failed.
/opt/conda/conda-bld/pytorch_1503970438496/work/torch/lib/THC/THCTensorIndex.cu:378: long calculateOffset(IndexType, LinearIndexCalcData<IndexType, Dims>) [with IndexType = unsigned int, Dims = 2U]: block: [9,0,0], thread: [126,0,0] Assertion `indexAtDim < data.baseSizes[dim]` failed.
/opt/conda/conda-bld/pytorch_1503970438496/work/torch/lib/THC/THCTensorIndex.cu:378: long calculateOffset(IndexType, LinearIndexCalcData<IndexType, Dims>) [with IndexType = unsigned int, Dims = 2U]: block: [9,0,0], thread: [127,0,0] Assertion `indexAtDim < data.baseSizes[dim]` failed.
THCudaCheck FAIL file=/opt/conda/conda-bld/pytorch_1503970438496/work/torch/lib/THC/generated/../THCReduceAll.cuh line=334 error=59 : device-side assert triggered
Traceback (most recent call last):
  File "train.py", line 195, in <module>
    fire.Fire()
  File "/home/zhangsheng/anaconda2/envs/paravec/lib/python3.6/site-packages/fire/core.py", line 127, in Fire
    component_trace = _Fire(component, args, context, name)
  File "/home/zhangsheng/anaconda2/envs/paravec/lib/python3.6/site-packages/fire/core.py", line 366, in _Fire
    component, remaining_args)
  File "/home/zhangsheng/anaconda2/envs/paravec/lib/python3.6/site-packages/fire/core.py", line 542, in _CallCallable
    result = fn(*varargs, **kwargs)
  File "train.py", line 95, in start
    save_all)
  File "train.py", line 143, in _run
    x = cost_func.forward(x)
  File "/export/ssd/sheng/projects/paragraph-vectors/paragraphvec/loss.py", line 26, in forward
    + torch.sum(self._log_sigmoid(-scores[:, 1:]), dim=1) / k
  File "/home/zhangsheng/anaconda2/envs/paravec/lib/python3.6/site-packages/torch/autograd/variable.py", line 476, in sum
    return Sum.apply(self, dim, keepdim)
  File "/home/zhangsheng/anaconda2/envs/paravec/lib/python3.6/site-packages/torch/autograd/_functions/reduce.py", line 16, in forward
    return input.new((input.sum(),))
RuntimeError: cuda runtime error (59) : device-side assert triggered at /opt/conda/conda-bld/pytorch_1503970438496/work/torch/lib/THC/generated/../THCReduceAll.cuh:334
terminate called without an active exception
Aborted (core dumped)

the ideology with "An infinite, parallel (multiprocess) batch generator" in NCEData?

Hi there!

I kind of don't seem to understand the idealogy behind the class NCEData in data.py. The description seems to tell "An infinite, parallel (multiprocess) batch generator fo rnoise-contrastive estimation of word vector models". Can I have some more resources on it and some tips on using/implementing it?

Thanks :)

How to use the model file created

The model name ends with the extension .pth.tar

and given an error "The archive is either in unknown format or damaged"

most of the packages needed for this si too old and have been rmoved from the main website itself

PyTorch and CUDA versions

Description

Additional info (stack trace, etc.)

Steps to reproduce the behaviour

Loss becomes nan after some epochs

PyTorch and CUDA versions

PyTorch: 0.2.0.post3
CUDA: 8.0

Description

Loss suddenly becomes nan (was somewhere between 0.4 and 0.5 in the previous epoch) when training on a dataset comprised of 74219 documents with vocabulary size 91417.

Additional info (stack trace, etc.)

Training on a GPU with the following parameters:
--data_file_name 'arxiv_min.csv' --num_epochs 500 --batch_size 512 --context_size 4 --num_noise_words 10 --vec_dim 300 --lr 0.001 --max_generated_batches 500 --num_workers 8

How to get the vectors for each document?

I did all the steps you mentioned but I felt the step that would show how to create the vector for a given document was missing.

Basically, I am interested in outputing example_vectors.txt/.csv that in each line has a vector for each document in example.csv.

How should I do that? Can you please add it to your tutorial?

PyTorch and CUDA versions

Python version = 3.7
CUDA = 9.0

Description

When I try "pip install -e ." I failed to install the code.

Additional info (stack trace, etc.)

error: Command "gcc -pthread -B /home/jikunkang/anaconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.linux-x86_64-3.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/home/jikunkang/anaconda3/include/python3.7m -Ibuild/src.linux-x86_64-3.7/numpy/core/src/private -Ibuild/src.linux-x86_64-3.7/numpy/core/src/npymath -Ibuild/src.linux-x86_64-3.7/numpy/core/src/private -Ibuild/src.linux-x86_64-3.7/numpy/core/src/npymath -Ibuild/src.linux-x86_64-3.7/numpy/core/src/private -Ibuild/src.linux-x86_64-3.7/numpy/core/src/npymath -c numpy/random/mtrand/mtrand.c -o build/temp.linux-x86_64-3.7/numpy/random/mtrand/mtrand.o -MMD -MF build/temp.linux-x86_64-3.7/numpy/random/mtrand/mtrand.o.d" failed with exit status 1

    ----------------------------------------
Command "/home/jikunkang/git/paragraph-vectors/env/bin/python3 -u -c "import setuptools, tokenize;__file__='/tmp/pip-install-qn2takz9/numpy/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record /tmp/pip-record-cf3tjp63/install-record.txt --single-version-externally-managed --compile --install-headers /home/jikunkang/git/paragraph-vectors/env/include/site/python3.7/numpy" failed with error code 1 in /tmp/pip-install-qn2takz9/numpy/

Steps to reproduce the behaviour

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PyTorch and CUDA versions

Description

Additional info (stack trace, etc.)

Description

PyTorch and CUDA versions

Description

PyTorch and CUDA versions

Description

Additional info (stack trace, etc.)

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PyTorch and CUDA versions

Description

Additional info (stack trace, etc.)

PyTorch and CUDA versions

Description

Additional info (stack trace, etc.)

Steps to reproduce the behaviour

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