| Name | insigen JSON |
| Version |
0.1.8
JSON |
| download |
| home_page | https://github.com/4RCAN3/insigen |
| Summary | Generates Insights from text pieces such as Documents or Articles |
| upload_time | 2024-08-07 21:39:57 |
| maintainer | None |
| docs_url | None |
| author | Aaryan Tyagi |
| requires_python | >=3.11 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. 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Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
| keywords |
nlp
topic modelling
text analysis
summary extraction
keyword extraction
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
absl-py
aiohttp
aiosignal
asttokens
astunparse
async-timeout
attrs
backcall
beautifulsoup4
cachetools
certifi
charset-normalizer
click
colorama
comm
contourpy
cycler
Cython
datasets
debugpy
decorator
dill
evaluate
executing
filelock
flatbuffers
fonttools
frozenlist
fsspec
gast
gensim
google-auth
google-auth-oauthlib
google-pasta
grpcio
h5py
hdbscan
huggingface
huggingface-hub
idna
ipykernel
ipython
jedi
Jinja2
joblib
jupyter_client
jupyter_core
keras
kiwisolver
libclang
llvmlite
Markdown
MarkupSafe
matplotlib
matplotlib-inline
mpmath
multidict
multiprocess
nest-asyncio
networkx
nltk
numba
numpy
oauthlib
opt-einsum
packaging
pandas
parso
pickleshare
Pillow
platformdirs
prompt-toolkit
protobuf
psutil
pure-eval
pyarrow
pyasn1
pyasn1-modules
Pygments
pynndescent
pyparsing
PyPDF2
python-dateutil
pytz
pywin32
PyYAML
pyzmq
regex
requests
requests-oauthlib
responses
rsa
safetensors
scikit-learn
scipy
sentence-transformers
sentencepiece
six
smart-open
soupsieve
stack-data
sympy
tensorboard
tensorboard-data-server
tensorflow
tensorflow-estimator
tensorflow-hub
tensorflow-intel
tensorflow-io-gcs-filesystem
termcolor
threadpoolctl
tokenizers
torch
torchvision
tornado
tqdm
traitlets
transformers
typing_extensions
tzdata
umap-learn
urllib3
wcwidth
Werkzeug
wordcloud
wrapt
xxhash
yarl
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<h1 align="center">InsiGEN</h1>
<p align="center"><i>A state of the art NLP model to generate insights from a text piece</i></p>
## Features of topic modelling
- Generating a distribution of generalized topics covered in a document/articler
- Extracting contextualized keywords from the text piece
- Generating a summary of the text
- Trained on a corpus of 6000 wikipedia articles for generalized topics
- Can be trained on custom data for more specific topics
## How to use the model
- Clone this repository
- Install the dependencies from the `requirements.txt`
- Basic Usage:
### Get a topic distribution
```python
from insigen import insigen
model = insigen()
topic_distribution = model.get_distribution(document)
```
#### Important parameters for `insigen`:
* `use_pretrained_embeds`: Setting this parameter to False will allow you to train your own embeddings. Further parameters need to be specified for training
* `embed_file`: This parameter should be used when you've trained your own embeddings. Specify the path to your sentence embeddings.
* `dataset_file`: This parameter should be used when you've trained your own embeddings. Specify the path to your own dataset.
* `embedding_model`: (Default = all-mpnet-base-v2) Insigen uses sentence bert models to train it's embeddings. Valid models are:
all-distilroberta-v1
all-mpnet-base-v2
all-MiniLM-L12-v2
all-MiniLM-L6-v2
Important parameters for `get_distribution`
* `document`: The text for which the topic distribution is to be generated
* `metric`: This metric defines how the topics will be found. Can be set to 'threshold', to get all the topics above a similarity threshold. Defaults to 'max'. 'Max' metric gets the top "n" topics
* `max_count`: This argument should be used with max metric. It specifies the top x amount of topics that get fetched. Defaults to 1.
* `threshold`: This argument should be used with threshold metric. It specifies the threshold similarity over which all topics will be fetched. Defaults to 0.5.
### Get keyword frequency
```python
frequency = model.get_keyword_frequency(document, min_len=2, max_len=3)
#Generate a wordcloud using the frequency
cloud = model.generate_wordcloud(frequency)
```
#### Important parameters for `get_keyword_frequency`
* `document`: The text for which the keyword frequency is to be generated
* `frequency_threshold`: minimum frequency of a n-gram to be considered in the keywords (`min_len` and `max_len` are also used to adjust the length of n-grams in the text)
### Generate Summary
```python
summary = model.generate_summary(article, topic_match=relevant_topic))
# To get a list of topics, use this
#print(model.unique_topics)
```
#### Import parameters for `generate_summary`
* `document`:The text for which the summary is to be generated
* `topic_match`: a topic that can match with the text. This adds additional weight to sentences that are more related to the topic. use `model.unique_topics` to get a list of topics that can match. Defaults to None, in which case weightage to related sentence will not be given.
* `topic_weight`: Adds weightage to the topic similarity score. Increasing this parameter results to more topic oriented summary. Defaults to 1.
* `similarity_weight`: Adds weightage to sentence similarity score. Increasing this parameter results in extracting more co-related sentences. Defaults to 1.
* `position_weight`: Adds weightage to the position of the sentences. Increasing this parameter results to more position oriented summary; i.e Texts present early in the document are given more weightatge. Defaults to 10.
* `num_sentences`: This specifies the number of sentences that are to be included in the summary. Defaults to 10.
### Train on your dataset
```python
embeddings = model.train_embeds(dataset)
```
#### Important parameters for `train_embeds`
* `dataset`: A pandas dataframe for the dataset to be trained
* `batch_size`: Batches to divide the dataset into. Defaults to 32.
## How does the model work?
### Topic Distribution
- Create embedded vectors of labelled training articles
- Find mean embeddings of each topic in the corpus to create topic vectors and create clusters of articles\
- Use KNN to place new articles in the topic vector cluster\
- Chunking each article and finding relevant topic from the topic vectors\
### Keyword extraction
- N-grams and keywords are filtered from the text
- Contextually similar keywords to the article are given higher scoring
- A threshold is applied to the filtered list of keywords to get the final list of keywords
### Summary Extraction
- The PageRank algorithm is used to create a similarity matrix for sentences in the text
- Additionally, sentences are scored based on their position in the text and their similarity to a relevant topic
- Top N sentences from the similarity matrix are extracted to create a summary.
Raw data
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"home_page": "https://github.com/4RCAN3/insigen",
"name": "insigen",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.11",
"maintainer_email": null,
"keywords": "NLP, Topic Modelling, Text analysis, Summary Extraction, Keyword Extraction",
"author": "Aaryan Tyagi",
"author_email": "Aaryan Tyagi <tyagiaaryan00@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/50/28/3d85efff12d001084a2c7b8ad26b6fb84cff67d47d4de137426e7afd3d10/insigen-0.1.8.tar.gz",
"platform": null,
"description": "<h1 align=\"center\">InsiGEN</h1>\r\n<p align=\"center\"><i>A state of the art NLP model to generate insights from a text piece</i></p>\r\n\r\n## Features of topic modelling\r\n- Generating a distribution of generalized topics covered in a document/articler\r\n- Extracting contextualized keywords from the text piece\r\n- Generating a summary of the text\r\n- Trained on a corpus of 6000 wikipedia articles for generalized topics\r\n- Can be trained on custom data for more specific topics\r\n\r\n## How to use the model\r\n- Clone this repository\r\n- Install the dependencies from the `requirements.txt`\r\n- Basic Usage:\r\n\r\n### Get a topic distribution\r\n\r\n```python\r\nfrom insigen import insigen\r\nmodel = insigen()\r\ntopic_distribution = model.get_distribution(document)\r\n```\r\n\r\n#### Important parameters for `insigen`:\r\n* `use_pretrained_embeds`: Setting this parameter to False will allow you to train your own embeddings. Further parameters need to be specified for training\r\n* `embed_file`: This parameter should be used when you've trained your own embeddings. Specify the path to your sentence embeddings.\r\n* `dataset_file`: This parameter should be used when you've trained your own embeddings. Specify the path to your own dataset.\r\n* `embedding_model`: (Default = all-mpnet-base-v2) Insigen uses sentence bert models to train it's embeddings. Valid models are:\r\n\r\n all-distilroberta-v1\r\n all-mpnet-base-v2\r\n all-MiniLM-L12-v2\r\n all-MiniLM-L6-v2\r\n\r\nImportant parameters for `get_distribution`\r\n* `document`: The text for which the topic distribution is to be generated\r\n* `metric`: This metric defines how the topics will be found. Can be set to 'threshold', to get all the topics above a similarity threshold. Defaults to 'max'. 'Max' metric gets the top \"n\" topics\r\n* `max_count`: This argument should be used with max metric. It specifies the top x amount of topics that get fetched. Defaults to 1.\r\n* `threshold`: This argument should be used with threshold metric. It specifies the threshold similarity over which all topics will be fetched. Defaults to 0.5.\r\n\r\n\r\n### Get keyword frequency\r\n\r\n```python\r\nfrequency = model.get_keyword_frequency(document, min_len=2, max_len=3)\r\n\r\n#Generate a wordcloud using the frequency\r\ncloud = model.generate_wordcloud(frequency)\r\n```\r\n\r\n#### Important parameters for `get_keyword_frequency`\r\n* `document`: The text for which the keyword frequency is to be generated\r\n* `frequency_threshold`: minimum frequency of a n-gram to be considered in the keywords (`min_len` and `max_len` are also used to adjust the length of n-grams in the text)\r\n\r\n### Generate Summary\r\n\r\n```python\r\nsummary = model.generate_summary(article, topic_match=relevant_topic))\r\n\r\n# To get a list of topics, use this\r\n#print(model.unique_topics)\r\n```\r\n\r\n#### Import parameters for `generate_summary`\r\n* `document`:The text for which the summary is to be generated\r\n* `topic_match`: a topic that can match with the text. This adds additional weight to sentences that are more related to the topic. use `model.unique_topics` to get a list of topics that can match. Defaults to None, in which case weightage to related sentence will not be given.\r\n* `topic_weight`: Adds weightage to the topic similarity score. Increasing this parameter results to more topic oriented summary. Defaults to 1.\r\n* `similarity_weight`: Adds weightage to sentence similarity score. Increasing this parameter results in extracting more co-related sentences. Defaults to 1.\r\n* `position_weight`: Adds weightage to the position of the sentences. Increasing this parameter results to more position oriented summary; i.e Texts present early in the document are given more weightatge. Defaults to 10.\r\n* `num_sentences`: This specifies the number of sentences that are to be included in the summary. Defaults to 10.\r\n\r\n### Train on your dataset\r\n\r\n```python\r\nembeddings = model.train_embeds(dataset)\r\n```\r\n\r\n#### Important parameters for `train_embeds`\r\n* `dataset`: A pandas dataframe for the dataset to be trained\r\n* `batch_size`: Batches to divide the dataset into. Defaults to 32.\r\n\r\n\r\n## How does the model work? \r\n\r\n### Topic Distribution\r\n- Create embedded vectors of labelled training articles\r\n- Find mean embeddings of each topic in the corpus to create topic vectors and create clusters of articles\\\r\n \r\n\r\n- Use KNN to place new articles in the topic vector cluster\\\r\n \r\n\r\n- Chunking each article and finding relevant topic from the topic vectors\\\r\n \r\n\r\n### Keyword extraction\r\n- N-grams and keywords are filtered from the text\r\n- Contextually similar keywords to the article are given higher scoring\r\n- A threshold is applied to the filtered list of keywords to get the final list of keywords\r\n\r\n\r\n### Summary Extraction\r\n- The PageRank algorithm is used to create a similarity matrix for sentences in the text\r\n- Additionally, sentences are scored based on their position in the text and their similarity to a relevant topic\r\n- Top N sentences from the similarity matrix are extracted to create a summary.\r\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets \"[]\" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same \"printed page\" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ",
"summary": "Generates Insights from text pieces such as Documents or Articles",
"version": "0.1.8",
"project_urls": {
"Homepage": "https://github.com/4RCAN3/insigen",
"Issues": "https://github.com/4RCAN3/insigen/issues"
},
"split_keywords": [
"nlp",
" topic modelling",
" text analysis",
" summary extraction",
" keyword extraction"
],
"urls": [
{
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