---
layout: page
title: arXMLiv 08.2018 - 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: 08.2018
- 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 08.2018 dataset, including subformula lexemes
- `token_model.zip`
- 300 dimensional GloVe word embeddings for the arXMLiv 08.2018 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-082018/)
#### Token Model Statistics
| subset | documents | paragraphs | sentences |
| ---------- | --------: | ---------: | ----------: |
| no_problem | 137,864 | 4,646,203 | 21,533,963 |
| warning | 705,095 | 45,797,794 | 183,246,777 |
| error | 389,225 | 26,759,524 | 99,641,978 |
| complete | 1,232,184 | 77,203,521 | 304,422,718 |
| subset | words | formulas | inline cite | numeric literals |
| ---------- | ------------: | ----------: | ----------: | ---------------: |
| no_problem | 430,217,995 | 20,910,732 | 3,709,520 | 11,177,753 |
| warning | 3,175,663,430 | 281,832,412 | 25,337,574 | 83,606,897 |
| error | 1,731,971,035 | 153,186,264 | 13,145,561 | 43,399,720 |
| complete | 5,337,852,460 | 455,929,408 | 42,192,655 | 138,184,370 |
#### GloVe Model Statistics
| subset | tokens | unique words | unique words (freq 5+ ) |
| ---------- | -------------: | -----------: | ----------------------: |
| no_problem | 622,968,267 | 715,433 | 219,304 |
| warning | 7,203,536,205 | 3,478,235 | 666,317 |
| error | 3,691,805,321 | 2,444,532 | 574,467 |
| complete | 11,518,309,793 | 5,285,379 | 1,000,295 |
### 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-082018/#citing-this-resource)
### Download
[Download link](https://gl.kwarc.info/SIGMathLing/embeddings-arXMLiv-08-2018)
([SIGMathLing members](/member/) only)
### Generated via
- [llamapun 0.2.0](https://github.com/KWARC/llamapun/releases/tag/0.2.0),
- [GloVe 1.2, 2018](https://github.com/stanfordnlp/GloVe/tree/07d59d5e6584e27ec758080bba8b51fce30f69d8)
### 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 \
token_model_error.txt > token_model_complete.txt
```
* [llamapun v0.2](https://github.com/KWARC/llamapun/releases/tag/0.2), `corpus_token_model` example used for token model extraction
* processed logical paragraphs only (excluded non-textual modalities, e.g. tables, figures and their captions, bibliographies, as well as abstracts)
* excluded paragraphs containing latexml errors (marked via a `ltx_ERROR` HTML class)
* used llamapun math-aware sentence and word tokenization, with subformula math lexemes
* marked up inline citations replaced with `citationelement` token
* numeric literals replaced with `NUM` token (both in text and formulas)
* ignored sentences with unnaturally long words (>25 characters) - almost always due to latexml conversion errors
* [GloVe repository at sha 07d59d](https://github.com/stanfordnlp/GloVe/tree/07d59d5e6584e27ec758080bba8b51fce30f69d8)
* build/vocab_count -min-count 5
* build/cooccur -memory 32.0 -window-size 15
* build/shuffle -memory 32.0
* build/glove -threads 16 -x-max 100 -iter 25 -vector-size 300 -binary 2
### Examples and baselines
#### GloVe in-built evaluation (non-expert tasks e.g. language, relationships, geography)
1. no_problem
* Total accuracy: 27.47% (4028/14663)
* Highest score: "gram3-comparative.txt", 75.30% (1003/1332)
2. warning
* Total accuracy: 31.97% (5351/16736)
* Highest score: "gram3-comparative.txt", 77.40% (1031/1332)
3. error
* Total accuracy: 29.67% (4910/16549)
* Highest score: "gram3-comparative.txt", 72.75% (969/1332)
4. complete
* Total accuracy: 35.48% (6298/17750)
* Highest score: "gram3-comparative.txt", 76.65% (1021/1332)
5. 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)
**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:
```
python2 eval/python/word_analogy.py --vocab_file vocab.arxmliv.txt --vectors_file glove.arxmliv.11B.300d.txt
```
1. `abelian` is to `group` as `disjoint` is to `?`
* Top hit: `union`, cosine distance `0.653377`
2. `convex` is to `concave` as `positive` is to `?`
* Top hit: `negative`, cosine distance `0.786877`
3. `finite` is to `infinte` as `abelian` is to `?`
* Top hit: `nonabelian`, cosine distance `0.676042`
4. `quantum` is to `classical` as `bottom` is to `?`
* Top hit: `top`, cosine distance `0.734896`
5. `eq` is to `proves` as `figure` is to `?`
* Top hit: `showing`, cosine distance `0.668502`
6. `italic_x` is to `italic_y` as `italic_a` is to `?`
* Top hit: `italic_b`, cosine distance `0.902608`
#### Nearest word vectors
In a cloned GloVe repository, start via:
```
python2 eval/python/distance.py --vocab_file vocab.arxmliv.txt --vectors_file glove.arxmliv.11B.300d.txt
```
1. **lattice**
```
Word: lattice Position in vocabulary: 488
Word Cosine distance
---------------------------------------------------------
lattices 0.853103
triangular 0.637767
honeycomb 0.626426
crystal 0.624397
finite 0.614720
spacing 0.603067
```
2. **entanglement**
```
Word: entanglement Position in vocabulary: 1568
Word Cosine distance
---------------------------------------------------------
entangled 0.780425
multipartite 0.730968
concurrence 0.691708
negativity 0.649595
tripartite 0.647623
quantum 0.640395
fidelity 0.640285
teleportation 0.616797
discord 0.613752
entropy 0.612341
bipartite 0.608034
coherence 0.606859
nonlocality 0.601337
```
3. **forgetful**
```
Word: forgetful Position in vocabulary: 11740
Word Cosine distance
---------------------------------------------------------
functor 0.723472
functors 0.656184
morphism 0.598965
```
4. **eigenvalue**
```
Word: eigenvalue Position in vocabulary: 1448
Word Cosine distance
---------------------------------------------------------
eigenvalues 0.893073
eigenvector 0.768380
eigenvectors 0.765241
eigenfunction 0.754222
eigenfunctions 0.686141
eigenspace 0.666098
eigen 0.641422
matrix 0.616723
eigenmode 0.613117
eigenstate 0.612188
laplacian 0.611396
largest 0.606122
smallest 0.605342
eigenmodes 0.604839
```
5. **riemannian**
```
Word: riemannian Position in vocabulary: 2285
Word Cosine distance
---------------------------------------------------------
manifolds 0.765827
manifold 0.760806
metric 0.719817
finsler 0.687826
curvature 0.676100
ricci 0.664770
metrics 0.660804
riemmanian 0.651666
euclidean 0.644686
noncompact 0.643878
conformally 0.638984
riemanian 0.633814
kahler 0.632680
endowed 0.622035
submanifold 0.613868
submanifolds 0.612716
geodesic 0.604488
```