VDCNN (on Amazon Review Full dataset)
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
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
- Facebook AI Research,University of Le Mans
- Organisation type
- Industry,Academia
- Country
- United States of America, France
- Published
- 27 January 2017
- Authors
- Alexis Conneau, Holger Schwenk, Loïc Barrault, Yann Lecun
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Text classification
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.
- Parameters
- 7.8M
- Training data
- tokens
- Epochs
- 15
- Batch size
- 128
7.8 M from Table 2.
Table 3: Amazon Review Full 3 000k
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
- 5.7 × 10¹⁷ FLOP
- How it was established
- Hardware
5050000000000 [peak FLOPs] * 15 [epochs] * 7 [h] * 3600 [s] * 0.3 [assumed utilization rate] = 5.7267e+17
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 Tesla K40s
- Chips used
- 1
- Power draw
- 282 W
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Very Deep Convolutional Networks for Text Classification
- Last updated
- 11 February 2026
What the numbers mean
Background
VDCNN (on Amazon Review Full dataset) was published by Facebook AI Research,University of Le Mans, in United States of America, in January 2017. The organisation is categorised as industry,Academia.
It works in Language, and is recorded as doing text classification.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
The training run consumed about 5.7 × 10¹⁷ FLOP, on NVIDIA Tesla K40s. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
VDCNN (on Amazon Review Full dataset) — common questions
When was VDCNN (on Amazon Review Full dataset) released?
VDCNN (on Amazon Review Full dataset) was published in January 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is VDCNN (on Amazon Review Full dataset) used for?
VDCNN (on Amazon Review Full dataset) works in Language, and is recorded as handling text classification. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
How much compute was used to train VDCNN (on Amazon Review Full dataset)?
Around 5.7 × 10¹⁷ FLOP, on NVIDIA Tesla K40s. 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.
What GPU do I need to run VDCNN (on Amazon Review Full dataset)?
None. VDCNN (on Amazon Review Full dataset) is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is VDCNN (on Amazon Review Full dataset) open source?
The licensing for VDCNN (on Amazon Review Full dataset) was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does VDCNN (on Amazon Review Full dataset) have?
VDCNN (on Amazon Review Full dataset) has 7.8M parameters. 7.8 M from Table 2. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created VDCNN (on Amazon Review Full dataset)?
VDCNN (on Amazon Review Full dataset) was published by Facebook AI Research,University of Le Mans, based in United States of America, categorised as industry,Academia.
The other direction
Looking at it from the other side?
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