Cross-lingual alignment
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Tel Aviv University,Massachusetts Institute of Technology (MIT)
- Organisation type
- Academia,Academia
- Country
- Israel, United States of America
- Published
- 4 April 2019
- Authors
- Tal Schuster, Ori Ram, Regina Barzilay, and Amir Globerson.
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Translation
- Base model
- ELMo
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 2.6 × 10¹⁸ FLOP
- How it was established
- Hardware
From author communication: Precision: float32 Hardware: 4 GPU NVIDIA 1080Ti NVIDIA 1080Ti: 1.06E+13 Compute: 7 GPU-days 0.4 * 1.06E+13 FLOP/s * 7 days * 24h/day * 3600s/h = 2.56E+18
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce GTX 1080 Ti
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
MIT license https://github.com/TalSchuster/CrossLingualContextualEmb
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Speculative
- Citations
- 221
"our method consistently outperforms the previous state-of-the-art on 6 tested languages" "Table 3 summarizes the results for our zero-shot, multi-source experiments on six languages from Google universal dependency treebank version 2.0." "Table 5 summarizes the results, showing that our algorithm outperforms the best model from the shared task by 5.05 LAS points and improves by over 10 points over a FASTTEXT baseline"
Sources
Where this record came from and when it was last checked.
- Reference
- Cross-lingual alignment of contextual word embeddings, with applications to zero- shot dependency parsing.
- Last updated
- 25 May 2026
What the numbers mean
About this model
Cross-lingual alignment was published by Tel Aviv University,Massachusetts Institute of Technology (MIT), in the country recorded as Israel, during April 2019. The publishing organisation is categorised as academia,Academia.
It works in the domain of Language, and is recorded as performing the task of translation.
Its starting point was an existing base model, ELMo. That is the usual way a specialised model is produced.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.
What went into building it
Producing it required arithmetic totalling around 2.6 × 10¹⁸ FLOP, on hardware recorded as NVIDIA GeForce GTX 1080 Ti. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Its inclusion criterion: sOTA improvement.
Answers
Cross-lingual alignment — common questions
Cross-lingual alignment— how much compute was used to train it?
Training consumed around 2.6 × 10¹⁸ FLOP, on hardware recorded as NVIDIA GeForce GTX 1080 Ti. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Cross-lingual alignment— what GPU do I need to run it?
We cannot say. It has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Cross-lingual alignment— is it open source?
Its weights are published, so it can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
Cross-lingual alignment— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Cross-lingual alignment— who created it?
It was published by Tel Aviv University,Massachusetts Institute of Technology (MIT), based in Israel, an organisation categorised as academia,Academia.
Cross-lingual alignment— when was it released?
It was published in April 2019. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Cross-lingual alignment— what is it used for?
It works in the domain of Language, and is recorded as handling the task of translation. These are the areas it was designed around; they describe intent rather than a hard boundary.
Cross-lingual alignment— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.