Skip to main content

SentencePiece python wrapper

Project description

SentencePiece Python Wrapper

Python wrapper for SentencePiece. This API will offer the encoding, decoding and training of Sentencepiece.

Build and Install SentencePiece

For Linux (x64/i686), macOS, and Windows(win32/x64) environment, you can simply use pip command to install SentencePiece python module.

% pip install sentencepiece

To build and install the Python wrapper from source, please install SentencePiece C++ and try the following commands:

% python setup.py build
% sudo python setup.py install

If you don’t have write permission to the global site-packages directory or don’t want to install into it, please try:

% python setup.py install --user

Usage

See this google colab page to run sentencepiece interactively. (Note: this sample is written in old interface.)

Segmentation

% python
>>> import sentencepiece as spm
>>> sp = spm.SentencePieceProcessor(model_file='test/test_model.model')
>>> sp.encode('This is a test')
[284, 47, 11, 4, 15, 400]
>>> sp.encode(['This is a test', 'Hello world'], out_type=int)
[[284, 47, 11, 4, 15, 400], [151, 88, 21, 887]]
>>> sp.encode('This is a test', out_type=str)
['▁This', '▁is', '▁a', '▁', 't', 'est']
>>> sp.encode(['This is a test', 'Hello world'], out_type=str)
[['▁This', '▁is', '▁a', '▁', 't', 'est'], ['▁He', 'll', 'o', '▁world']]
>>> for _ in range(10):
...     sp.encode('This is a test', out_type=str, enable_sampling=True, alpha=0.1, nbest_size=-1)
... 
['▁', 'This', '▁', 'is', '▁a', '▁', 't', 'e', 'st']
['▁T', 'h', 'i', 's', '▁is', '▁a', '▁', 'te', 's', 't']
['▁T', 'h', 'is', '▁', 'is', '▁', 'a', '▁', 't', 'est']
['▁', 'This', '▁is', '▁', 'a', '▁', 't', 'e', 'st']
['▁', 'This', '▁', 'is', '▁', 'a', '▁', 't', 'e', 's', 't']
['▁This', '▁is', '▁a', '▁', 'te', 's', 't']
['▁This', '▁is', '▁', 'a', '▁', 't', 'e', 'st']
['▁', 'T', 'h', 'is', '▁', 'is', '▁', 'a', '▁', 'te', 'st']
['▁', 'This', '▁', 'i', 's', '▁a', '▁', 't', 'e', 'st']
['▁This', '▁', 'is', '▁a', '▁', 't', 'est']
>>> sp.decode([284, 47, 11, 4, 15, 400])
'This is a test'
>>> sp.decode([[284, 47, 11, 4, 15, 400], [151, 88, 21, 887]])
['This is a test', 'Hello world']
>>> sp.decode(['▁', 'This', '▁', 'is', '▁a', '▁', 't', 'e', 'st'])
'This is a test'
>>> sp.decode([['▁This', '▁is', '▁a', '▁', 't', 'est'], ['▁He', 'll', 'o', '▁world']])
['This is a test', 'Hello world']
>>> sp.get_piece_size()
1000
>>> sp.id_to_piece(2)
'</s>'
>>> sp.id_to_piece([2, 3, 4])
['</s>', '\r', '▁']
>>> sp.piece_to_id('<s>')
1
>>> sp.piece_to_id(['</s>', '\r', '▁'])
[2, 3, 4]
>>> len(sp)
1000
>>> sp['</s>']
2

Model Training

Training is performed by passing parameters of spm_train to SentencePieceTrainer.train() function.

>>> import sentencepiece as spm
>>> spm.SentencePieceTrainer.train(input='test/botchan.txt', model_prefix='m', vocab_size=1000, user_defined_symbols=['foo', 'bar'])
sentencepiece_trainer.cc(73) LOG(INFO) Starts training with : 
trainer_spec {
  input: test/botchan.txt
  .. snip
unigram_model_trainer.cc(500) LOG(INFO) EM sub_iter=1 size=1188 obj=10.2839 num_tokens=32182 num_tokens/piece=27.0892
unigram_model_trainer.cc(500) LOG(INFO) EM sub_iter=0 size=1100 obj=10.4269 num_tokens=33001 num_tokens/piece=30.0009
unigram_model_trainer.cc(500) LOG(INFO) EM sub_iter=1 size=1100 obj=10.4069 num_tokens=33002 num_tokens/piece=30.0018
trainer_interface.cc(595) LOG(INFO) Saving model: m.model
trainer_interface.cc(619) LOG(INFO) Saving vocabs: m.vocab
>>>

Training without local filesystem

Sentencepiece trainer can receive any iterable object to feed training sentences. You can also pass a file object (instance with write() method) to emit the output model to any devices. These features are useful to run sentencepiece on environment that have limited access to the local file system (e.g., Google colab.)

import urllib.request
import io
import sentencepiece as spm

# Loads model from URL as iterator and stores the model to BytesIO.
model = io.BytesIO()
with urllib.request.urlopen(
    'https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt'
) as response:
  spm.SentencePieceTrainer.train(
      sentence_iterator=response, model_writer=model, vocab_size=1000)

# Serialize the model as file.
# with open('out.model', 'wb') as f:
#   f.write(model.getvalue())

# Directly load the model from serialized model.
sp = spm.SentencePieceProcessor(model_proto=model.getvalue())
print(sp.encode('this is test'))

Segmentation (old interface)

% python
>>> import sentencepiece as spm
>>> sp = spm.SentencePieceProcessor()
>>> sp.Load("test/test_model.model")
True
>>> sp.EncodeAsPieces("This is a test")
['\xe2\x96\x81This', '\xe2\x96\x81is', '\xe2\x96\x81a', '\xe2\x96\x81', 't', 'est']
>>> sp.EncodeAsIds("This is a test")
[284, 47, 11, 4, 15, 400]
>>> sp.DecodePieces(['\xe2\x96\x81This', '\xe2\x96\x81is', '\xe2\x96\x81a', '\xe2\x96\x81', 't', 'est'])
'This is a test'
>>> sp.NBestEncodeAsPieces("This is a test", 5)
[['\xe2\x96\x81This', '\xe2\x96\x81is', '\xe2\x96\x81a', '\xe2\x96\x81', 't', 'est'], ['\xe2\x96\x81This', '\xe2\x96\x81is', '\xe2\x96\x81a', '\xe2\x96\x81', 'te', 'st'], ['\xe2\x96\x81This', '\xe2\x96\x81is', '\xe2\x96\x81a', '\xe2\x96\x81', 'te', 's', 't'], ['\xe2\x96\x81This', '\xe2\x96\x81is', '\xe2\x96\x81a', '\xe2\x96\x81', 't', 'e', 'st'], ['\xe2\x96\x81This', '\xe2\x96\x81is', '\xe2\x96\x81a', '\xe2\x96\x81', 't', 'es', 't']]
>>> for x in range(10):
...     sp.SampleEncodeAsPieces("This is a test", -1, 0.1)
...
['\xe2\x96\x81', 'T', 'h', 'i', 's', '\xe2\x96\x81', 'is', '\xe2\x96\x81a', '\xe2\x96\x81', 't', 'e', 's', 't']
['\xe2\x96\x81T', 'h', 'is', '\xe2\x96\x81is', '\xe2\x96\x81', 'a', '\xe2\x96\x81', 't', 'est']
['\xe2\x96\x81This', '\xe2\x96\x81is', '\xe2\x96\x81', 'a', '\xe2\x96\x81', 't', 'e', 'st']
['\xe2\x96\x81This', '\xe2\x96\x81is', '\xe2\x96\x81a', '\xe2\x96\x81', 't', 'e', 'st']
['\xe2\x96\x81This', '\xe2\x96\x81is', '\xe2\x96\x81a', '\xe2\x96\x81', 't', 'e', 's', 't']
['\xe2\x96\x81T', 'h', 'is', '\xe2\x96\x81', 'i', 's', '\xe2\x96\x81a', '\xe2\x96\x81', 'te', 's', 't']
['\xe2\x96\x81This', '\xe2\x96\x81', 'is', '\xe2\x96\x81a', '\xe2\x96\x81', 'te', 's', 't']
['\xe2\x96\x81This', '\xe2\x96\x81', 'i', 's', '\xe2\x96\x81a', '\xe2\x96\x81', 't', 'e', 'st']
['\xe2\x96\x81This', '\xe2\x96\x81', 'is', '\xe2\x96\x81', 'a', '\xe2\x96\x81', 't', 'e', 'st']
['\xe2\x96\x81This', '\xe2\x96\x81', 'i', 's', '\xe2\x96\x81', 'a', '\xe2\x96\x81', 'te', 's', 't']
>>> sp.DecodeIds([284, 47, 11, 4, 15, 400])
'This is a test'
>>> sp.GetPieceSize()
1000
>>> sp.IdToPiece(2)
'</s>'
>>> sp.PieceToId('</s>')
2
>>> len(sp)
1000
>>> sp['</s>']
2

Model Training (old interface)

Training is performed by passing parameters of spm_train to SentencePieceTrainer.Train() function.

>>> import sentencepiece as spm
>>> spm.SentencePieceTrainer.Train('--input=test/botchan.txt --model_prefix=m --vocab_size=1000')
unigram_model_trainer.cc(494) LOG(INFO) Starts training with : 
input: "test/botchan.txt"
model_prefix: "m"
model_type: UNIGRAM
..snip..
unigram_model_trainer.cc(529) LOG(INFO) EM sub_iter=0 size=1239 obj=10.4055 num_tokens=36256 num_tokens/piece=29.2623
unigram_model_trainer.cc(529) LOG(INFO) EM sub_iter=1 size=1239 obj=10.3187 num_tokens=36256 num_tokens/piece=29.2623
unigram_model_trainer.cc(529) LOG(INFO) EM sub_iter=0 size=1100 obj=10.5285 num_tokens=37633 num_tokens/piece=34.2118
unigram_model_trainer.cc(529) LOG(INFO) EM sub_iter=1 size=1100 obj=10.4973 num_tokens=37630 num_tokens/piece=34.2091
trainer_interface.cc(284) LOG(INFO) Saving model: m.model
trainer_interface.cc(293) LOG(INFO) Saving vocabs: m.vocab
>>>

Python2/3 String/Unicode compatibility

Sentencepiece python wrapper accepts both Unicode string and legacy byte string. The output string type is determined by the input string type. The output type of IdToPiece/DecodeIds methods is str, but note that it is a legacy byte string in Python2 and Unicode string in Python3 respectively.

  • Python2:
>>> sp.EncodeAsPieces('吾輩は猫である')
['\xe2\x96\x81', '\xe5\x90\xbe', '\xe8\xbc\xa9', '\xe3\x81\xaf', '\xe7\x8c\xab', '\xe3\x81\xa7\xe3\x81\x82\xe3\x82\x8b']
>>> sp.EncodeAsPieces(u'吾輩は猫である')
[u'\u2581', u'\u543e', u'\u8f29', u'\u306f', u'\u732b', u'\u3067\u3042\u308b']
>>> sp.EncodeAsPieces(u'吾輩は猫である'.encode('utf-8'))
['\xe2\x96\x81', '\xe5\x90\xbe', '\xe8\xbc\xa9', '\xe3\x81\xaf', '\xe7\x8c\xab', '\xe3\x81\xa7\xe3\x81\x82\xe3\x82\x8b']
>>> sp.IdToPiece(10)
'\xe3\x81\xab'
>>> type(sp.IdToPiece(10))
<type 'str'>
  • Python3:
>>> sp.EncodeAsPieces('吾輩は猫である')
['▁', '吾', '輩', 'は', '猫', 'である']
>>> sp.EncodeAsPieces('吾輩は猫である'.encode('utf-8'))
[b'\xe2\x96\x81', b'\xe5\x90\xbe', b'\xe8\xbc\xa9', b'\xe3\x81\xaf', b'\xe7\x8c\xab', b'\xe3\x81\xa7\xe3\x81\x82\xe3\x82\x8b']
>>>
>>> sp.IdToPiece(10)
'に'
>>> type(sp.IdToPiece(10))
<class 'str'>

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sentencepiece-0.1.96.tar.gz (508.6 kB view details)

Uploaded Source

Built Distributions

sentencepiece-0.1.96-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

sentencepiece-0.1.96-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (1.3 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

sentencepiece-0.1.96-cp310-cp310-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.10 Windows x86-64

sentencepiece-0.1.96-cp310-cp310-win32.whl (1.0 MB view details)

Uploaded CPython 3.10 Windows x86

sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl (1.2 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ s390x

sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl (1.2 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ppc64le

sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl (1.3 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ i686

sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.2 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

sentencepiece-0.1.96-cp39-cp39-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.9 Windows x86-64

sentencepiece-0.1.96-cp39-cp39-win32.whl (1.0 MB view details)

Uploaded CPython 3.9 Windows x86

sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl (1.2 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ s390x

sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl (1.2 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ppc64le

sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl (1.3 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ i686

sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.2 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

sentencepiece-0.1.96-cp39-cp39-macosx_10_6_x86_64.whl (1.1 MB view details)

Uploaded CPython 3.9 macOS 10.6+ x86-64

sentencepiece-0.1.96-cp38-cp38-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.8 Windows x86-64

sentencepiece-0.1.96-cp38-cp38-win32.whl (1.0 MB view details)

Uploaded CPython 3.8 Windows x86

sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl (1.2 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ s390x

sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl (1.2 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ppc64le

sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl (1.3 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ i686

sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.2 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

sentencepiece-0.1.96-cp38-cp38-macosx_10_6_x86_64.whl (1.1 MB view details)

Uploaded CPython 3.8 macOS 10.6+ x86-64

sentencepiece-0.1.96-cp37-cp37m-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.7m Windows x86-64

sentencepiece-0.1.96-cp37-cp37m-win32.whl (1.0 MB view details)

Uploaded CPython 3.7m Windows x86

sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl (1.2 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ s390x

sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl (1.2 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ppc64le

sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl (1.3 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ i686

sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.2 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

sentencepiece-0.1.96-cp37-cp37m-macosx_10_6_x86_64.whl (1.1 MB view details)

Uploaded CPython 3.7m macOS 10.6+ x86-64

sentencepiece-0.1.96-cp36-cp36m-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.6m Windows x86-64

sentencepiece-0.1.96-cp36-cp36m-win32.whl (1.0 MB view details)

Uploaded CPython 3.6m Windows x86

sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ x86-64

sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl (1.2 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ s390x

sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl (1.2 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ ppc64le

sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl (1.3 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ i686

sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.2 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ ARM64

sentencepiece-0.1.96-cp36-cp36m-macosx_10_6_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.6m macOS 10.6+ x86-64

sentencepiece-0.1.96-cp35-cp35m-macosx_10_6_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.5m macOS 10.6+ x86-64

File details

Details for the file sentencepiece-0.1.96.tar.gz.

File metadata

  • Download URL: sentencepiece-0.1.96.tar.gz
  • Upload date:
  • Size: 508.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.9.2

File hashes

Hashes for sentencepiece-0.1.96.tar.gz
Algorithm Hash digest
SHA256 9bdf097d5bd1d8ce42dfee51f6ff05f5578b96e48c6f6006aa4eff69edfa3639
MD5 57c080b032f64042f4e50288b492c621
BLAKE2b-256 aa71bb7d64dcd80a6506146397bca7310d5a8684f0f9ef035f03affb657f1aec

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 48c6d13b3bfff08060c138248e85df60f6fad11135ad7a8fc2ef6005aacca839
MD5 126a845f18ea73b2d3345c0f360ed81f
BLAKE2b-256 b3f8c710ac2968dc0991ce633be034f2d159286000b401f8127c6f7060b6a887

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 dadccb2e49244b6e64b4527d13ec14d5e094a90b41cf9b963e457e64182f1941
MD5 e5c143e7893f7468593ba6ef05f6e41a
BLAKE2b-256 39b761798ea1817917f55e82066b4a916c5574cf0cdd346d1d92dbfc2ad0ea5f

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 5513298d62fe63dd0862d08a6eb52a9aa3537006f597f2386184e3f95bb88889
MD5 ffeb3da05ef2ad3f8182921edddab9e6
BLAKE2b-256 944a1c057d9d2a956a7bb1cf14e3909ecc8d71ed273460c31e50dfae316f831b

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 colorama/0.4.4 importlib-metadata/4.6.4 keyring/23.5.0 pkginfo/1.8.2 readme-renderer/24.0 requests-toolbelt/0.9.1 requests/2.25.1 rfc3986/1.5.0 tqdm/4.57.0 urllib3/1.26.5 CPython/3.9.10

File hashes

Hashes for sentencepiece-0.1.96-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 203443a7bd4295b6a3695787235abe0e77d4c369d7156a6b9a397c540a38bd27
MD5 520dd88f8a023000ca76eec6f906c489
BLAKE2b-256 4e3099b9201155aa05cedc8db0e290cd0ab7d69331e3cfefa7c6594cda64f965

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp310-cp310-win32.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp310-cp310-win32.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 colorama/0.4.4 importlib-metadata/4.6.4 keyring/23.5.0 pkginfo/1.8.2 readme-renderer/24.0 requests-toolbelt/0.9.1 requests/2.25.1 rfc3986/1.5.0 tqdm/4.57.0 urllib3/1.26.5 CPython/3.9.10

File hashes

Hashes for sentencepiece-0.1.96-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 3028699bdb2fb0230804f3b8a617fe3af22f5c5a56416419b31a7da5e7bf83bc
MD5 a1099cafdb2acd36d5c1b12b13629908
BLAKE2b-256 afb6042a1fe8b4fef1a3589957f27df6101b41a8a85d41903482bfa639b1057d

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1dac8c2ad02b5ebc1179c0a14cbc7d7c6f4fd73d4dd51820626402d0aefc974e
MD5 15e28fef5b753d4b86ae14bc6b67302c
BLAKE2b-256 7f217a88f09026e1a69358b845d6b5deb36a2a54457ba7317f8f1047d9bbb643

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl
Algorithm Hash digest
SHA256 b77d27f59d515c43b61745b8173fbe7c7b3014b14b3702a75bf1793471e7def6
MD5 c8588ca8f373f01354051fd43abc1385
BLAKE2b-256 0032f0020d7aebf4a4b22be3b993e3b2afac9ed465948e26379ce619a908972a

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
Algorithm Hash digest
SHA256 e9e9fe8094ca57549d801e9a2017ac5c24108bbf485ea4f8994a72e8e96ee135
MD5 82db8323acc3179dd2e20256adae716a
BLAKE2b-256 d4e3b6aa91ea0a25f5bbf86ffae19234947e4fdf7a43e0d7bc1f096b47dbdaa7

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 36e9ff61e7b67c5b7ee96733613622620b4802fc8cf188a4dbc1f355b03dde02
MD5 b1ecdd637b7f5b51614c1311f6409551
BLAKE2b-256 6963022d8d5a27deddd4b131e8225614a0dc37e17570b3cdbaf9b1d49af535dc

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 cc969e6694fb27fba7cee2953f350804faf03913f25ae1ee713a7b8a1bc08018
MD5 33f2b33f7f9be64e3999489b98533f8a
BLAKE2b-256 3337e200305045eaac9aef4f2077428fe3d67840f1f3b73395e09858258ab813

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.9.2

File hashes

Hashes for sentencepiece-0.1.96-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 d45e3f78e746aa161bc9f5a31c6a2839c512101113a4065f4d2e7a3ab8198d8c
MD5 dbcaf8df3b6e454ebc0d531df2018b40
BLAKE2b-256 ac2ee451116a475ffcb8670394df1592ddb8b8b283328895ff73ba04be29a082

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp39-cp39-win32.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp39-cp39-win32.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.9.2

File hashes

Hashes for sentencepiece-0.1.96-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 b8b1dd2712f8a7de5b4c8ec912e6c041d25750bf03e1ce325cdba43bae0944ae
MD5 38c05e92065e1c4d5344ece2dab596dd
BLAKE2b-256 6c53094d455c06b1444052e3d16e3e5184023b579b401b7aa9d3d3b9df78a2c5

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d501713a8396193883aa526f48dc609f5f031a5df1afbafa561cf9ab492ffc76
MD5 cef542eacc0af1c0167738912c0a4797
BLAKE2b-256 7e4042458c8c0014c7ef2ad9a33d38de334e245bbfbc71968fd7c0b631628a2e

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl
Algorithm Hash digest
SHA256 3c703e68ea192e45b65c5d5836f6980849d828a18da4189899d7150fad82dc9e
MD5 5175a5929f154f2ac77645f57418d4a4
BLAKE2b-256 3cd633914202e058392d7296c9827842b77ac3551e4a9ed2280e9f7adee7e3ad

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
Algorithm Hash digest
SHA256 384148cead5cdab34a4d74fe1fb6a5a8abaafed25eaa4a7698b49dd9482e4c4e
MD5 1bae188c1ff790e729916dcec664666b
BLAKE2b-256 f215fc0b3e53686ab56024c4fe1294540784b5b08bedc51d7ec2bf6cfc61d98c

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 940a6999c7d3f55e9d7b194fd5e1f41a7dbed26d3519fb95333216292a39599e
MD5 16303a509af6397d73d6de570ce86d5f
BLAKE2b-256 98e7359158300b95ac9eb588f585042c1c165d7c099085cd34ab9e223442728b

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b1c24c1d9405b2148184ff27c062493d5e3be5c144575f95b5a0d7c660a515af
MD5 5663bbac632898a9194237250314c64a
BLAKE2b-256 8ddfd819bdacef199affeb9faad7df88f6a1c4f67b3dde55256a5dab7e7d7631

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp39-cp39-macosx_10_6_x86_64.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp39-cp39-macosx_10_6_x86_64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.9, macOS 10.6+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.9.2

File hashes

Hashes for sentencepiece-0.1.96-cp39-cp39-macosx_10_6_x86_64.whl
Algorithm Hash digest
SHA256 78e18d9106c36dcca929e18fd2c412378deac661d47fa3ee25defc55eef8a215
MD5 1f6d7a5a931ea98e9f89d87fd59f1283
BLAKE2b-256 020c131a34b8ea87a7c4841f97b1b4b0126714cb124f46128329d9d77de18ded

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.9.2

File hashes

Hashes for sentencepiece-0.1.96-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 eba0471ab0bb2e07ed06d91ecf5185d402c83d194155a41d8e2aa547d187712e
MD5 083330e9937aaf0d3d683f17b688596d
BLAKE2b-256 5db581c33f060b365ff3f753fdc24a373e19baa04eda4260c096be3ae2ac9481

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp38-cp38-win32.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp38-cp38-win32.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.9.2

File hashes

Hashes for sentencepiece-0.1.96-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 81bb77ba3651114943b2f8f77829cf764137dff06e38f4bf7fa43efea12c7f84
MD5 15f15daba3b103bed534d4b0d60c6f68
BLAKE2b-256 0584fe3fb4ca177ea15664399350a1fec83a8ef7ba48f0493d13517a8c926ddd

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a336575463d75d3aac1f7e32470b8998643ccd9a73786bd726f6b0470520b6b4
MD5 da16d00d745d6cf23ed8f4541b5cbe50
BLAKE2b-256 6891ded0f64f90abfc5413c620fc345a0aef1e7ff5addda8704cc6b3bf589c64

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl
Algorithm Hash digest
SHA256 e88354b61f59dfdeb41023f7be8ae31dc627c2dc2dacbc2de8b2d82a0997135c
MD5 80151422aac62e95ce6d276523d3dfff
BLAKE2b-256 4346701b54cc3a257b4fc81ccb784820a86ae05c4b4e96e2b73b0c1aba8b99c0

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
Algorithm Hash digest
SHA256 f8cb24d8d0b2f8b7463815a59183eb81ec1d7a06e3217bed456063f3303eddfb
MD5 ade8890bc3d566eb826eb69e7adc18db
BLAKE2b-256 be7e47e4a050e1cbbe5b5b8e3284b53661ae4ad79d17a44b91ffdda4c45cc97d

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 466e381f0a812da8fda97a9707498cef3210ea8385a3421bcbadcb5384063969
MD5 93213ab8ef8a47397bf4dc9f63cad795
BLAKE2b-256 275e93a3a94e1ef7396bb3b517268cef858c644249bd0cd4f3e0aa14c0742e43

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 8179785883b556cd517416cdbda6244745414b00ec83132cfe1d26000971f3ae
MD5 b2d34011fea7e896a6650707082cfdc0
BLAKE2b-256 36248227d81110171979819ccc87d2b8e3bb45d8a6f3f203ad5dfeb51c2d6e38

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp38-cp38-macosx_10_6_x86_64.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp38-cp38-macosx_10_6_x86_64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.8, macOS 10.6+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.9.2

File hashes

Hashes for sentencepiece-0.1.96-cp38-cp38-macosx_10_6_x86_64.whl
Algorithm Hash digest
SHA256 e811984b0908c14c56de7d8226fdd494d87a7ccb75af8ac3a07423037aaafc35
MD5 8bf59b1094c480c474a43080eaee5eaa
BLAKE2b-256 f830a47fb2ab4b1b214f3177efe700af7032b4ac229edae148a4f06016c8541a

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.9.2

File hashes

Hashes for sentencepiece-0.1.96-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 335bf84d72112cc91f3c3b691d61802fc963503b7772fd8280d20368048b8f3e
MD5 35fcedcaf0654d35afb88a22404bead8
BLAKE2b-256 0121b78bb71b7fbab906eb1d10f67d1ba69761892016cf13c4f0c5dde123bb07

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp37-cp37m-win32.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.9.2

File hashes

Hashes for sentencepiece-0.1.96-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 fd907a8f744e5337de7fc532dd800c4416b571ea47f8c3c66be10cd1bc67c925
MD5 83902caf4fd6f238319ceef21a27e106
BLAKE2b-256 ca3d907fe29f255406ba11b9430e82554a41e005edfcefd99f7c0b70f0a0a0e3

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d954d25a8705f972e8bfc1dea5464d7e697dd6f4ade092f1a487387e6d6c829a
MD5 d764417653adf8211db4ffceceb8970b
BLAKE2b-256 acaa1437691b0c7c83086ebb79ce2da16e00bef024f24fec2a5161c35476f499

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl
Algorithm Hash digest
SHA256 89c038da7f827a6e2ca4c73aeb4e4b25b99d981ce47dd61b04d446c8200cba1e
MD5 23efa168437e73094f38b55518ed49d1
BLAKE2b-256 a8d903b4d850f3e2689fdf3aad4cdb8d0d13a2e6e7b590931b15f7869b05fe6c

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
Algorithm Hash digest
SHA256 ef59ba19340dc1d002ce5713b911c0ef23c577b08f8ed57998ee3c8e62c5bf6e
MD5 ca96cfeb15a19cbbc7328f69db90fdf2
BLAKE2b-256 d59d306c3b904db42b2ac06e3af1629df0b24d09b41e6aa0f209f2a84a95d149

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 3e61e0757e49c306fff78ea75d6b75773418fe22214b4a460959203be934e834
MD5 8d7fe8c861ff34a692f2f4755ca8d881
BLAKE2b-256 097da70c3f8d6516179fcc5939b940a645cb960cf400b2a2130aed37156c1d60

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ff7d752a7f82d87711ec1a95c2262cb74f98be5b457f0300d81a1aefe5be2a95
MD5 437524291687a2bbde767af1b9b3fe86
BLAKE2b-256 bb3c0159798ecd439ab9de11d5e3862d2e1eeb3e1362590e0fc31764e9eac1de

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp37-cp37m-macosx_10_6_x86_64.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp37-cp37m-macosx_10_6_x86_64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.7m, macOS 10.6+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.9.2

File hashes

Hashes for sentencepiece-0.1.96-cp37-cp37m-macosx_10_6_x86_64.whl
Algorithm Hash digest
SHA256 a697257a2cd7581732d7741a8d32a06927f0311c3d277dbc47fa1043350c9d17
MD5 696f6e62e11660026c18c45e959bdc81
BLAKE2b-256 18b461c100a53fa950fda87a8b9cd16ff7e9b8f41b9e47e6bd5e7775288cf742

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.9.2

File hashes

Hashes for sentencepiece-0.1.96-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 4997c7ccf2ae462320250314aa5709a88d8a09fa271d073458a07bebf33f8e7c
MD5 a55ed77ba1045a35e6af514247f3d225
BLAKE2b-256 a02ba0b983a1daa6c703a9a2496f5220809d813a9f30e5129ff6b3a23369f93c

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp36-cp36m-win32.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.6m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.9.2

File hashes

Hashes for sentencepiece-0.1.96-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 bedf0355117fb4e9b1fc9fc92b4d5ee743a7d468be9f6196e3b94447710ea589
MD5 a3e61ce3e249622c25888b62cdea940d
BLAKE2b-256 7be9d448ff51dad50583818dff6f897ca1a5a9d5e7e971f7ffd7e2161d70fb80

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a92e1932ee8fd500680ccbe1bf53eb33228f4c9d6524ed6f300bcc80ac359f27
MD5 c87389feaf38494fc9a45cf3016b4472
BLAKE2b-256 5b492155d4078e9918003e77b6032a83d71995656bd05707d96e06a44cd6edf6

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl
Algorithm Hash digest
SHA256 5388882bb24d083f6cc8cffc5c435f3694a7772b018e06ea6fd84d1044009efb
MD5 e1313e0c07474596248bca0d30922ded
BLAKE2b-256 c040c068b1cbfcde18e124630b2daec4135505db8f7461da892793cd988aa377

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
Algorithm Hash digest
SHA256 26d20d713b3ba1b7a19205336afb1e93a4327c372b2f795e907b8dc2315ac92e
MD5 b05f330dd5aefd36d7753172e23960a1
BLAKE2b-256 daa8dcd3060d56de32afe04a71dfdfd16514147a50df8c52deab2ed457e73bd8

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 f8c90df663cd9759b2cf8dd29998b63140ac39e51ada2e739dc13bdac0b4f001
MD5 dceb6fea28317044d8e729b106255723
BLAKE2b-256 a27cd027bd2180932df70cf0d1ecf44a4a93598426928929c66c903822ddf9c7

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for sentencepiece-0.1.96-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 aeb090ad462833df03af1debce4ae607a2766ef861f992003ad0c56d074ab805
MD5 c6fb8c2fb562b0490fd0fa3522ec8f32
BLAKE2b-256 e23e5d2fb6d396b81ebac3204a718530539465f34c127cfbf2c9258e0c4e6e4c

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp36-cp36m-macosx_10_6_x86_64.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp36-cp36m-macosx_10_6_x86_64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.6m, macOS 10.6+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.9.2

File hashes

Hashes for sentencepiece-0.1.96-cp36-cp36m-macosx_10_6_x86_64.whl
Algorithm Hash digest
SHA256 99ea2d9db19e63a2d17d5dc64f9ace83fb9308a735be05a1aaf98eb4b496fba7
MD5 e002094c3e35486ed589751e3734fe69
BLAKE2b-256 02e8d6433a09a4ad0f1735e8297fab2aea9195f2a9267e3f780cafe1eab58884

See more details on using hashes here.

File details

Details for the file sentencepiece-0.1.96-cp35-cp35m-macosx_10_6_x86_64.whl.

File metadata

  • Download URL: sentencepiece-0.1.96-cp35-cp35m-macosx_10_6_x86_64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.5m, macOS 10.6+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.9.2

File hashes

Hashes for sentencepiece-0.1.96-cp35-cp35m-macosx_10_6_x86_64.whl
Algorithm Hash digest
SHA256 e8ec5bb6777e2060e1499750c50e1b69dca5a0f80f90f2c66656c5f3e5244593
MD5 b81f4c4d0e460d5713d472e885221ccf
BLAKE2b-256 1f50605e4edc4e01330ea65559af7c6f1aa96ad9310f492951403f8a1656702e

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page