This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model- it just wraps around the class. It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more. Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Take a look at the documentation of sentence-transformers if you want to use this model for more than what we…
Open-weight model · Sentence similarity
lt-wikidata-comp-en
by Dell Research Harvard dell-research-harvard/lt-wikidata-comp-en
This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model- it just wraps around the class. It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package.
Parameters109M
Context514
Weights438.0 MB
License—
AccessOpen weights
Monthly Downloads839
Runs On
What it takes to serve lt-wikidata-comp-en (109M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.2 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
Model Card
This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model- it just wraps around the class. It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more. Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Take a look at the documentation of sentence-transformers if you want to use this model for more than what we…
Excerpt from the card by Dell Research Harvard.
Configuration
- Architecture
- MPNetModel
- Context length (tokens)
- 514
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 30,527
- Stored precision
- float32
- Model type
- mpnet
Identity and Version
- Repository
- dell-research-harvard/lt-wikidata-comp-en
- Publisher
- Dell Research Harvard
- Task
- Sentence similarity
- Modality
- Text
- Library
- sentence-transformers
- Parameters
- 109M parameters
- Languages
- en
- Revision
- af65d96525b7cb3e125a52b89928ae78b39c6d21
- First published
- 2023-08-11
- Last updated
- 2024-03-20
Files and Weights
19 files, 452.2 MB in total. The weights are 1 file totalling 438.0 MB in safetensors.
Weights1 file · 438.0 MB
Configuration7 files · 3.3 KB
Tokenizer3 files · 944.1 KB
Documentation1 file · 5.7 KB
Other6 files · 13.3 MB
Repository1 file · 472 B
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 438.0 MB | 4d88dbbf624b |
| 1_Pooling/config.json | Configuration | 270 B | — |
| LT_training_config.json | Configuration | 1.0 KB | — |
| config.json | Configuration | 622 B | — |
| config_sentence_transformers.json | Configuration | 116 B | — |
| modules.json | Configuration | 229 B | — |
| sentence_bert_config.json | Configuration | 53 B | — |
| special_tokens_map.json | Configuration | 962 B | — |
| README.md | Documentation | 5.7 KB | — |
| Information-Retrieval_evaluation_eval_results.csv | Other | 582 B | — |
| Information-Retrieval_evaluation_test_results.csv | Other | 575 B | — |
| eval/Information-Retrieval_evaluation_eval_results.csv | Other | 58.9 KB | — |
| linkage_en_aliases_train.csv | Other | 6.8 MB | d2f83e1d4ae0 |
| test_data.pickle | Other | 728.9 KB | 44d740112e42 |
| val_data.pickle | Other | 5.7 MB | 0b6468956478 |
| .gitattributes | Repository | 472 B | — |
| tokenizer.json | Tokenizer | 710.9 KB | — |
| tokenizer_config.json | Tokenizer | 1.6 KB | — |
| vocab.txt | Tokenizer | 231.5 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 438.0 MB
Download from Dell Research Harvard
Released by Dell Research Harvard through its official repository on Hugging Face.
Built From
- Described by arXiv:2309.00789
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 438.0 MB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About lt-wikidata-comp-en
How much GPU memory does lt-wikidata-comp-en need?
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (109M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run lt-wikidata-comp-en on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
What is lt-wikidata-comp-en's context length?
514 tokens, from the maximum position embeddings in its published configuration.
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