Commit 84be8ca3 authored by Deyan Ginev's avatar Deyan Ginev

Merge branch 'arxmliv-dataset-2019' into 'master'

Adding 2019 arXMLiv resources

See merge request !9
parents 536cdcc7 7b620040
Pipeline #1731 passed with stage
in 2 minutes and 33 seconds
---
layout: post
title: arXiv 2019 Data Set and Embeddings Released
---
The 2019 release to the arXMLiv data set has been published.
Details can be found on the corresponding [data set resource page](/resources/arxmliv-dataset-082019/) and [embeddings resource page](/resources/arxmliv-embeddings-082019).
The content of this data set is licensed to [SIGMathLing members](/member/) for research
and tool development purposes subject to the [SIGMathLing Non-Disclosure-Agreement](/nda/).
---
layout: page
title: arXMLiv 08.2017 - An HTML5 dataset for arXiv.org
title: arXMLiv 08.2017 - An HTML5 dataset for arXiv.org
---
Part of the [arXMLiv](https://kwarc.info/projects/arXMLiv/) project at the [KWARC](https://kwarc.info/) research group
### Author
- Deyan Ginev
### Current release
- 08.2017
### Release
- This page documents: 08.2017
- Latest: [08.2019](/resources/arxmliv-dataset-082019/)
### Accessibility and License
The content of this Dataset is licensed to [SIGMathLing members](/member/) for research
and tool development purposes.
and tool development purposes.
Access is restricted to [SIGMathLing members](/member/) under the
[SIGMathLing Non-Disclosure-Agreement](/nda/) as for most [arXiv](http://arxiv.org)
......@@ -26,13 +27,13 @@ articles, the right of distribution was only given (or assumed) to arXiv itself.
| subset ID | number of documents | size archived | size unpacked |
| :--- | ---: | ---: | ---: |
| no\_problem| 112,088 | 5 GB | 37 GB |
| warning | 574,638 | 71 GB | 595 GB |
| warning | 574,638 | 71 GB | 595 GB |
| error | 401,644 | 50 GB | 421 GB |
| subset file name | MD5 |
| :--- | :--- |
| `arXMLiv_08_2017_no_problem.zip` | `036945755c7cc75ea1577cf04ca4fead` |
| `arXMLiv_08_2017_warning.zip` | `c0d5c1baf626225b48264510ac4c6bd5` |
| `arXMLiv_08_2017_warning.zip` | `c0d5c1baf626225b48264510ac4c6bd5` |
| `arXMLiv_08_2017_error.zip` | `2f4e60b993d85d30523b064c19e45733` |
......@@ -40,7 +41,7 @@ articles, the right of distribution was only given (or assumed) to arXiv itself.
This is a first public release of the arXMLiv dataset generated by the [KWARC](https://kwarc.info/) research group. It contains 1,088,370 HTML5 scientific documents from the arXiv.org preprint archive, converted from their respective TeX sources.
The dataset is segmented in 3 different subsets, each corresponding to a severity level of the LaTeXML software responsible for the HTML5 conversion.
The dataset is segmented in 3 different subsets, each corresponding to a severity level of the LaTeXML software responsible for the HTML5 conversion.
- The `no_problem` set had no obvious challenges in conversion and is the safest, most reliable subset
- The `warning` set covers a variety of minor issues, from mathematical expressions unparseable by the LaTeXML grammar, to missing LaTeX packages with no apparent use in the document. The vast majority of the documents should both have a good-looking rendering, as well as data consistency for e.g. NLP tasks.
- The `error` set covers all conversions which successfully generated an HTML5 document, but had major issues during the conversion. Examples would range from unknown macros (due to limited LaTeX coverage), unexpected latex syntax, math/text mode mismatches, as well as real LaTeX errors from the original sources. This subset should be used with extra caution, though should still preserve overall data consistency and could be safely used for e.g. generating word embeddings.
......@@ -57,9 +58,9 @@ The dataset should be referenced in all academic publications that present resul
obtained with its help. The reference should contain the identifier `arXMLiv:08.2017` in
the title, the author, year, a reference to SIGMathLing, and the URL of the resource
description page. For convenience, we supply some records for bibTeX and EndNote below. To
cite a particular part of the dataset use the subset identifiers in the ciation;
cite a particular part of the dataset use the subset identifiers in the ciation;
e.g. `\cite[no_problem subset]{arXMLiv:08.2017}` or just explain it in the text using the
concrete identifier.
concrete identifier.
#### pure bibTeX
```
......@@ -95,7 +96,7 @@ concrete identifier.
### Download
[Download link](https://gl.kwarc.info/SIGMathLing/dataset-arXMLiv-08-2017)
([SIGMathLing members](/member/) only)
### Generated via
- [LaTeXML 0.8.2](https://github.com/brucemiller/LaTeXML/releases/tag/v0.8.2),
- [LaTeXML 0.8.2](https://github.com/brucemiller/LaTeXML/releases/tag/v0.8.2),
- [CorTeX 0.2](https://github.com/dginev/CorTeX/releases/tag/0.2.0)
---
layout: page
title: arXMLiv 08.2018 - An HTML5 dataset for arXiv.org
title: arXMLiv 08.2018 - An HTML5 dataset for arXiv.org
---
Part of the [arXMLiv](https://kwarc.info/projects/arXMLiv/) project at the [KWARC](https://kwarc.info/) research group
### Author
- Deyan Ginev
### Current release
- 08.2018
### Release
- This page documents: 08.2018
- Latest: [08.2019](/resources/arxmliv-dataset-082019/)
### Accessibility and License
The content of this Dataset is licensed to [SIGMathLing members](/member/) for research
and tool development purposes.
and tool development purposes.
Access is restricted to [SIGMathLing members](/member/) under the
[SIGMathLing Non-Disclosure-Agreement](/nda/) as for most [arXiv](http://arxiv.org)
......@@ -26,13 +27,13 @@ articles, the right of distribution was only given (or assumed) to arXiv itself.
| subset ID | number of documents | size archived | size unpacked |
| :--- | ---: | ---: | ---: |
| no\_problem| 137,864 | 6.9 GB | 53 GB |
| warning | 705,095 | 104 GB | 896 GB |
| warning | 705,095 | 104 GB | 896 GB |
| error | 389,227 | 58 GB | 506 GB |
| subset file name | MD5 |
| :--- | :--- |
| `arXMLiv_08_2018_no_problem.zip` | `0e19fd0a68f18f6bb05b3d7d1127a072` |
| `arXMLiv_08_2018_warning.zip` | `8ea32275e9cf197f8eb592d539206a6f` |
| `arXMLiv_08_2018_warning.zip` | `8ea32275e9cf197f8eb592d539206a6f` |
| `arXMLiv_08_2018_error.zip` | `5880d4fac417f5fdad6e8cec0e3bfe2a` |
**Note:** the archives preserve a leading path `./data/datasets/dataset-arXMLiv-08-2018`, which can be safely ignored, and is an artefact of the current release that will be avoided in the future.
......@@ -41,7 +42,7 @@ articles, the right of distribution was only given (or assumed) to arXiv itself.
This is a second public release of the arXMLiv dataset generated by the [KWARC](https://kwarc.info/) research group. It contains 1,232,186 HTML5 scientific documents from the arXiv.org preprint archive, converted from their respective TeX sources. A 13% increase in available articles over the 08.2017 release.
The dataset is segmented in 3 different subsets, each corresponding to a severity level of the LaTeXML software responsible for the HTML5 conversion.
The dataset is segmented in 3 different subsets, each corresponding to a severity level of the LaTeXML software responsible for the HTML5 conversion.
- The `no_problem` set had no obvious challenges in conversion and is the safest, most reliable subset
- The `warning` set covers a variety of minor issues, from mathematical expressions unparseable by the LaTeXML grammar, to missing LaTeX packages with no apparent use in the document. The vast majority of the documents should both have a good-looking rendering, as well as data consistency for e.g. NLP tasks.
- The `error` set covers all conversions which successfully generated an HTML5 document, but had major issues during the conversion. Examples would range from unknown macros (due to limited LaTeX coverage), unexpected latex syntax, math/text mode mismatches, as well as real LaTeX errors from the original sources. This subset should be used with extra caution, though should still preserve overall data consistency and could be safely used for e.g. generating word embeddings.
......@@ -56,9 +57,9 @@ The dataset should be referenced in all academic publications that present resul
obtained with its help. The reference should contain the identifier `arXMLiv:08.2018` in
the title, the author, year, a reference to SIGMathLing, and the URL of the resource
description page. For convenience, we supply some records for bibTeX and EndNote below. To
cite a particular part of the dataset use the subset identifiers in the ciation;
cite a particular part of the dataset use the subset identifiers in the ciation;
e.g. `\cite[no_problem subset]{arXMLiv:08.2018}` or just explain it in the text using the
concrete identifier.
concrete identifier.
#### pure bibTeX
```
......@@ -94,7 +95,7 @@ concrete identifier.
### Download
[Download link](https://gl.kwarc.info/SIGMathLing/dataset-arXMLiv-08-2018)
([SIGMathLing members](/member/) only)
### Generated via
- [LaTeXML 0.8.3](https://github.com/brucemiller/LaTeXML/releases/tag/v0.8.3),
- [LaTeXML 0.8.3](https://github.com/brucemiller/LaTeXML/releases/tag/v0.8.3),
- [CorTeX 0.3.1](https://github.com/dginev/CorTeX/releases/tag/0.3.1)
---
layout: page
title: arXMLiv 08.2019 - An HTML5 dataset for arXiv.org
---
Part of the [arXMLiv](https://kwarc.info/projects/arXMLiv/) project at the [KWARC](https://kwarc.info/) research group
### Author
- Deyan Ginev
### Release
- This page documents: 08.2019
- Latest: [08.2019](/resources/arxmliv-dataset-082019/)
### Accessibility and License
The content of this Dataset is licensed to [SIGMathLing members](/member/) for research
and tool development purposes.
Access is restricted to [SIGMathLing members](/member/) under the
[SIGMathLing Non-Disclosure-Agreement](/nda/) as for most [arXiv](http://arxiv.org)
articles, the right of distribution was only given (or assumed) to arXiv itself.
### Contents
- 1,374,539 HTML5 documents
- Four separate archive bundles, separated by LaTeXML conversion severity
- derivative **word embeddings** and a **token model** are available separately [here](/resources/arxmliv-embeddings-082019/)
| subset ID | number of documents | size archived | size unpacked |
| :--- | ---: | ---: | ---: |
| no\_problem | 150,701 | 7.4 GB | 57 GB |
| warning_1 | 500,000 | 75 GB | 641 GB |
| warning_2 | 328,127 | 50 GB | 429 GB |
| error | 395,711 | 60 GB | 521 GB |
| subset file name | MD5 |
| :--- | :--- |
| `arXMLiv_08_2019_no_problem.zip` | `b70535d607ec916d9f6456b2b1fef421` |
| `arXMLiv_08_2019_warning_1.zip` | `fd4496504020a256f4e4f4200cb731fc` |
| `arXMLiv_08_2019_warning_2.zip` | `5d3ce062a768ce439bd7447f8f011e2b` |
| `arXMLiv_08_2019_error.zip` | `74c91c3b187d151f8bce7bb9936c050f` |
### Description
This is the third public release of the arXMLiv dataset generated by the [KWARC](https://kwarc.info/) research group. It contains 1,374,539 HTML5 scientific documents from the arXiv.org preprint archive, converted from their respective TeX sources. An 11% increase in available articles over the 08.2018 release.
The dataset is segmented in 4 subsets, corresponding to three severity levels of the HTML conversion.
- The `no_problem` set had no obvious challenges in conversion and is the safest, most reliable subset
- The `warning_1` and `warning_2` sets cover a variety of minor issues, from mathematical expressions unparseable by the LaTeXML grammar, to missing LaTeX packages with no apparent use in the document. The vast majority of the documents should both have a good-looking rendering, as well as data consistency for e.g. NLP tasks.
- The `error` set covers all conversions which successfully generated an HTML5 document, but had major issues during the conversion. Examples would range from unknown macros (due to limited LaTeX coverage), unexpected latex syntax, math/text mode mismatches, as well as real LaTeX errors from the original sources. This subset should be used with extra caution, though should still preserve overall data consistency and could be safely used for e.g. generating word embeddings.
This version of the dataset has had minimal manual quality control, and we offer no additional warranty beyond the latexml severity reported.
We welcome community feedback on all of: data quality, representation issues, need for auxiliary resources (e.g. figures, token models), as well as organization and archival best practices. The conversion, build system, and data redistribution efforts are all ongoing projects at the [KWARC research group](http://kwarc.info).
### Citing this Resource
The dataset should be referenced in all academic publications that present results
obtained with its help. The reference should contain the identifier `arXMLiv:08.2019` in
the title, the author, year, a reference to SIGMathLing, and the URL of the resource
description page. For convenience, we supply some records for bibTeX and EndNote below. To
cite a particular part of the dataset use the subset identifiers in the ciation;
e.g. `\cite[no_problem subset]{arXMLiv:08.2019}` or just explain it in the text using the
concrete identifier.
#### pure bibTeX
```
@MISC{SML:arXMLiv:08.2019,
author = {Deyan Ginev},
title = {arXMLiv:08.2019 dataset, an HTML5 conversion of arXiv.org},
howpublished = {hosted at \url{https://sigmathling.kwarc.info/resources/arxmliv-dataset-082019/}},
note = {SIGMathLing -- Special Interest Group on Math Linguistics},
year = 2019}
```
#### bibTeX for the bibLaTeX package (preferred)
```
@online{SML:arXMLiv:08.2019,
author = {Deyan Ginev},
title = {arXMLiv:08.2019 dataset, an HTML5 conversion of arXiv.org},
url = {https://sigmathling.kwarc.info/resources/arxmliv-dataset-082019/},
note = {SIGMathLing -- Special Interest Group on Math Linguistics},
year = 2019}
```
#### EndNote
```
%0 Generic
%T arXMLiv:08.2019 dataset, an HTML5 conversion of arXiv.org
%A Ginev, Deyan
%D 2019
%I hosted at https://sigmathling.kwarc.info/resources/arxmliv-dataset-082019/
%F SML:arXMLiv:08.2019b
%O SIGMathLing – Special Interest Group on Math Linguistics
```
### Download
[Download link](https://gl.kwarc.info/SIGMathLing/dataset-arXMLiv-08-2019)
([SIGMathLing members](/member/) only)
### Generated via
- [LaTeXML 0.8.4](https://github.com/brucemiller/LaTeXML/releases/tag/v0.8.4),
- [CorTeX 0.4.2](https://github.com/dginev/CorTeX/releases/tag/0.4.2)
......@@ -6,12 +6,14 @@ Part of the [arXMLiv](https://kwarc.info/projects/arXMLiv/) project at the [KWAR
### Author
- Deyan Ginev
### Current release
- 08.2017
### Release
- This page documents: 08.2017
- Latest: [08.2019](/resources/arxmliv-embeddings-082019/)
### Accessibility and License
The content of this Dataset is licensed to [SIGMathLing members](/member/) for research
and tool development purposes.
and tool development purposes.
Access is restricted to [SIGMathLing members](/member/) under the
[SIGMathLing Non-Disclosure-Agreement](/nda/) as for most [arXiv](http://arxiv.org)
......@@ -46,7 +48,7 @@ complete | 4,815,801,412 |405,434,997 | 37,612,439 | 126,938,500 |
subset | tokens | unique words | unique words (freq 5+ )
-----------|--------------:|-------------:|-----------------------:
no_problem | 384,951,086 | 490,134 | 170,615
no_problem | 384,951,086 | 490,134 | 170,615
warning | 2,817,734,902 | 1,200,887 | 422,524
error | 2,180,119,361 | 1,889,392 | 518,609
complete | 5,382,805,349 | 2,573,974 | 746,673
......@@ -58,14 +60,14 @@ Please cite the main dataset when using the word embeddings, as they are generat
### Download
[Download link](https://gl.kwarc.info/SIGMathLing/embeddings-arXMLiv-08-2017)
([SIGMathLing members](/member/) only)
### Generated via
- [llamapun 0.1](https://github.com/KWARC/llamapun/releases/tag/0.1),
- [llamapun 0.1](https://github.com/KWARC/llamapun/releases/tag/0.1),
- [GloVe 1.2](https://github.com/stanfordnlp/GloVe/tree/765074642a6544e47849bb85d8dc2e11e44c2922)
### Generation Parameters
* token model distributed as 3 subsets - no_problem, warning and error. complete model is derived via:
```
cat token_model_no_problem.txt \
token_model_warning.txt \
......@@ -108,13 +110,13 @@ Please cite the main dataset when using the word embeddings, as they are generat
* Total accuracy: 23.91% (4262/17827)
* Highest score: "capital-common-countries.txt", 62.65% (317/506)
**Evaluation note:** These in-built evlauation runs are provided as a sanity check that the generated GloVe models pass a basic baseline against the non-expert tasks in the default GloVe suite.
**Evaluation note:** These in-built evlauation runs are provided as a sanity check that the generated GloVe models pass a basic baseline against the non-expert tasks in the default GloVe suite.
One would need a scienctific discourse tailored set of test cases to evaluate the arXiv-based models competitively.
#### Measuring word analogy
In a cloned GloVe repository, start via:
```
python eval/python/word_analogy.py --vocab_file vocab.arxmliv.txt --vectors_file glove.arxmliv.5B.300d.txt
python eval/python/word_analogy.py --vocab_file vocab.arxmliv.txt --vectors_file glove.arxmliv.5B.300d.txt
```
1. `abelian` is to `group` as `disjoint` is to `?`
......@@ -133,29 +135,29 @@ python eval/python/word_analogy.py --vocab_file vocab.arxmliv.txt --vectors_file
* Top hit: `shows`, cosine distance `0.674743`
#### Nearest word vectors
#### Nearest word vectors
In a cloned GloVe repository, start via:
```
python eval/python/distance.py --vocab_file vocab.arxmliv.txt --vectors_file glove.arxmliv.5B.300d.txt
python eval/python/distance.py --vocab_file vocab.arxmliv.txt --vectors_file glove.arxmliv.5B.300d.txt
```
1. **lattice**
1. **lattice**
```
Word: lattice Position in vocabulary: 311
Word Cosine distance
-----------------------------------------------------
lattices 0.811057
honeycomb 0.657262
finite 0.625146
triangular 0.608218
spacing 0.605435
```
......@@ -163,103 +165,103 @@ python eval/python/distance.py --vocab_file vocab.arxmliv.txt --vectors_file glo
```
Word: entanglement Position in vocabulary: 1293
Word Cosine distance
-----------------------------------------------------
entangled 0.763964
multipartite 0.730231
fidelity 0.653443
concurrence 0.652454
environemtnal 0.646705
negativity 0.646165
quantum 0.639032
discord 0.624222
nonlocality 0.610661
tripartite 0.609896
```
3. **forgetful**
```
Word: forgetful Position in vocabulary: 10697
Word Cosine distance
-----------------------------------------------------
functor 0.723019
functors 0.653969
morphism 0.626222
```
4. **eigenvalue**
```
Word: eigenvalue Position in vocabulary: 1212
Word Cosine distance
-----------------------------------------------------
eigenvalues 0.878527
eigenvector 0.766371
eigenfunction 0.761923
eigenvectors 0.747451
eigenfunctions 0.707346
eigenspace 0.661539
corresponding 0.629746
laplacian 0.627187
operator 0.627130
eigen 0.620933
```
5. **riemannian**
```
Word: riemannian Position in vocabulary: 2026
Word Cosine distance
-----------------------------------------------------
manifold 0.766196
manifolds 0.745785
metric 0.714120
curvature 0.672975
metrics 0.670006
finsler 0.665079
ricci 0.657058
euclidean 0.650198
endowed 0.626307
riemmanian 0.621626
riemanian 0.618022
```
\ No newline at end of file
......@@ -7,8 +7,9 @@ Part of the [arXMLiv](https://kwarc.info/projects/arXMLiv/) project at the [KWAR
### Author
- Deyan Ginev
### Current release
- 08.2018
### Release
- This page documents: 08.2018
- Latest: [08.2019](/resources/arxmliv-embeddings-082019/)
### Accessibility and License
The content of this Dataset is licensed to [SIGMathLing members](/member/) for research
......
---
layout: page
title: arXMLiv 08.2019 - Word Embeddings; Token Model
---
Part of the [arXMLiv](https://kwarc.info/projects/arXMLiv/) project at the [KWARC](https://kwarc.info/) research group
### Author
- Deyan Ginev
### Release
- This page documents: 08.2019
- Latest: [08.2019](/resources/arxmliv-embeddings-082019/)
### Accessibility and License
The content of this Dataset is licensed to [SIGMathLing members](/member/) for research
and tool development purposes.
Access is restricted to [SIGMathLing members](/member/) under the
[SIGMathLing Non-Disclosure-Agreement](/nda/) as for most [arXiv](http://arxiv.org)
articles, the right of distribution was only given (or assumed) to arXiv itself.
### Contents
- An 11.5 billion token model for the arXMLiv 08.2019 dataset, including subformula lexemes
- `token_model.zip`
- 300 dimensional GloVe word embeddings for the arXMLiv 08.2019 dataset
- `glove.arxmliv.11B.300d.zip` and `vocab.arxmliv.zip`
- 300d GloVe word embeddings for individual subsets
- `glove.subsets.zip`
- Embeddings and vocabulary with math lexemes omitted
- `glove.arxmliv.nomath.11B.300d.zip` and `vocab.arxmliv.nomath.zip`
- added on July 20, 2019
- used as a control when evaluating the contribution of formula lexemes
- the main arXMLiv dataset is available separately [here](/resources/arxmliv-dataset-082019/)
#### Token Model Statistics
| subset | documents | paragraphs |
| ---------- | --------: | ---------: |
| no_problem | 150,701 | 6,071,920 |
| warning_1 | 500,000 | 36,130,694 |
| warning_2 | 328,127 | 24,285,351 |
| error | 395,711 | 31,155,136 |
| complete | 1,374,539 | 97,643,101 |
| subset | words | formulas | inline cite |
| ---------- | ------------: | ----------: | ----------: |
| no_problem | 619,051,536 | 25,210,637 | 4,248,840 |
| warning_1 | 2,917,283,935 | 212,113,899 | 18,553,611 |
| warning_2 | 1,937,516,458 | 140,094,708 | 12,590,335 |
| error | 2,307,007,544 | 163,290,748 | 14,200,445 |
| complete | 7,780,859,473 | 540,709,992 | 49,593,231 |
#### GloVe Model Statistics
| subset | tokens | unique words | unique words (freq 5+ ) |
| ---------- | -------------: | -----------: | ----------------------: |
| complete | 15,214,964,673 | 2,868,070 | 1,013,106 |
### Citing this Resource
Please cite the main dataset when using the word embeddings, as they are generated and distributed jointly. [Instructions here](/resources/arxmliv-dataset-082019/#citing-this-resource)
### Download
[Download link](https://gl.kwarc.info/SIGMathLing/embeddings-arXMLiv-08-2019)
([SIGMathLing members](/member/) only)
### Generated via
- [llamapun 0.2.0](https://github.com/KWARC/llamapun/releases/tag/0.2.0),
- [GloVe 1.2, 2019](https://github.com/stanfordnlp/GloVe/tree/07d59d5e6584e27ec758080bba8b51fce30f69d8)
### Generation Parameters
* token model distributed as 4 subsets - no_problem, warning_1, warning_2 and error. complete model is derived via:
```
cat token_model_no_problem.txt \
token_model_warning_1.txt token_model_warning_2.txt \
token_model_error.txt > token_model_complete.txt
```
* [llamapun v0.3.3](https://github.com/KWARC/llamapun/releases/tag/0.3.3), `corpus_token_model` example used for token model extraction
* processed logical paragraphs, abstracts, captions and keywords, ignore all other content (e.g. tables, bibliography, others)
* excluded paragraphs containing latexml errors (marked via a `ltx_ERROR` HTML class); also excluded when words over 25 characters were encountered.
* used llamapun math-aware word tokenization, with sub-formula math lexemes (improved robustness since 2018)
* marked up inline citations replaced with `citationelement` token
* numeric literals replaced with `NUM` token (both in text and formulas)
* internal references replaced with `ref` token (e.g. `Figure ref`)
* textual punctuation is included as-is, while mathematical punctuation is annotated via the latexml-generated lexemes.
* words are downcased, while math content is kept cased, to mitigate lexical ambiguity.
* [GloVe repository at sha 07d59d](https://github.com/stanfordnlp/GloVe/tree/07d59d5e6584e27ec758080bba8b51fce30f69d8)
* build/vocab_count -min-count 5
* build/cooccur -memory 48.0 -window-size 15
* build/shuffle -memory 48.0
* build/glove -threads 30 -x-max 100 -iter 50 -vector-size 300 -binary 2
### Examples and baselines
#### GloVe in-built evaluation (non-expert tasks e.g. language, relationships, geography)
1. NEW; 2019 model
* Total accuracy: 38.30% (7017/18322)
* Highest score: "gram3-comparative.txt", 78.60% (1047/1332)
2. 2018 [GloVe embeddings](/resources/arxmliv-embeddings-082018/)
* Total accuracy: 35.48% (6298/17750)
* Highest score: "gram3-comparative.txt", 76.65% (1021/1332)
3. demo baseline: text8 demo (first 100M characters of Wikipedia)
* Total accuracy: 23.62% (4211/17827)
* Highest score: "gram6-nationality-adjective.txt", 58.65% (892/1521)
#### Measuring word analogy
In a cloned GloVe repository, start via:
```
python2 eval/python/word_analogy.py --vocab_file vocab.arxmliv.txt --vectors_file glove.arxmliv.15B.300d.txt
```
1. `abelian` is to `group` as `disjoint` is to `?`
* Top hit: `union`, cosine distance `0.618853`
2. `convex` is to `concave` as `positive` is to `?`
* Top hit: `negative`, cosine distance `0.806679`
3. `finite` is to `infinte` as `abelian` is to `?`
* Top hit: `nonabelian`, cosine distance `0.698089`
4. `quantum` is to `classical` as `bottom` is to `?`
* Top hit: `middle`, cosine distance `0.769180`
* Close second: `top`, cosine distance `0.765937`
5. `eq` is to `proves` as `figure` is to `?`
* Top hit: `showing`, cosine distance `0.689938`
6. `italic_x` is to `italic_y` as `italic_a` is to `?`
* Top hit: `italic_b`, cosine distance `0.915467`
#### Nearest word vectors
In a cloned GloVe repository, start via:
```
python2 eval/python/distance.py --vocab_file vocab.arxmliv.txt --vectors_file glove.arxmliv.15B.300d.txt
```
1. **lattice**
```
Word: lattice Position in vocabulary: 515
Word Cosine distance
---------------------------------------------------------
lattices 0.865888
honeycomb 0.677004
finite 0.650216
triangular 0.632165
crystal 0.627800
sublattice 0.619792
cubic 0.609822
```
2. **entanglement**
```
Word: entanglement Position in vocabulary: 1603
Word Cosine distance
---------------------------------------------------------
entangled 0.803443
multipartite 0.744602
negativity 0.698730
concurrence 0.693703
tripartite 0.669840
discord 0.660572
fidelity 0.657391