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-patent-inventor-linking
by Matthew Lee Chen matthewleechen/lt-patent-inventor-linking
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. This model has been fine-tuned on the model: sentence-transformers/all-mpnet-base-v2.
Parameters109M
Context514
Weights438.0 MB
License—
AccessOpen weights
Monthly Downloads72
Runs On
What it takes to serve lt-patent-inventor-linking (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. This model has been fine-tuned on the model: sentence-transformers/all-mpnet-base-v2. It is pretrained for the language: en. To use this model using sentence-transformers: To use this model for clustering with LinkTransformer installed: We evaluate using the standard LinkTransformer information retrieval metrics. Our test set evaluations are available here. The model was trained with the parameters: torch.utils.data.dataloader.DataLoader of length 31 with parameters: linktransformer.modifiedsbert.losses.SupConLosswandb If you use our…
Excerpt from the card by Matthew Lee Chen.
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
- matthewleechen/lt-patent-inventor-linking
- Publisher
- Matthew Lee Chen
- Task
- Sentence similarity
- Modality
- Text
- Library
- sentence-transformers
- Parameters
- 109M parameters
- Languages
- en
- Revision
- f26d8931ce28a324d7fa77a43f6fe2337bc28c62
- First published
- 2025-01-08
- Last updated
- 2025-06-20
Files and Weights
16 files, 439.5 MB in total. The weights are 1 file totalling 438.0 MB in safetensors.
Weights1 file · 438.0 MB
Configuration7 files · 3.1 KB
Tokenizer3 files · 944.1 KB
Documentation1 file · 4.0 KB
Other3 files · 597.4 KB
Repository1 file · 1.6 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 438.0 MB | 5786a63dc055 |
| 1_Pooling/config.json | Configuration | 270 B | — |
| LT_training_config.json | Configuration | 770 B | — |
| config.json | Configuration | 587 B | — |
| config_sentence_transformers.json | Configuration | 116 B | — |
| modules.json | Configuration | 349 B | — |
| sentence_bert_config.json | Configuration | 53 B | — |
| special_tokens_map.json | Configuration | 964 B | — |
| README.md | Documentation | 4.0 KB | — |
| Information-Retrieval_evaluation_eval_results.csv | Other | 55.3 KB | — |
| Information-Retrieval_evaluation_test_results.csv | Other | 528 B | — |
| labelled_data.csv | Other | 541.5 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| 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 Matthew Lee Chen
Released by Matthew Lee Chen through its official repository on Hugging Face.
Built From
- Described by arXiv:2309.00789
- Described by arXiv:2401.12345
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-patent-inventor-linking
How much GPU memory does lt-patent-inventor-linking 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-patent-inventor-linking 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-patent-inventor-linking's context length?
514 tokens, from the maximum position embeddings in its published configuration.
Similar Models
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…
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…
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. This model has been fine-tuned on the model: sentence-transformers/all-mpnet-base-v2. It is pretrained for the language: en. To use this model using sentence-transformers: To use this model for clustering with LinkTransformer installed: We evaluate using the standard LinkTransformer information retrieval metrics. Our test set evaluations are available here. The model was trained with the parameters: torch.utils.data.dataloader.DataLoader of length 31 with parameters: linktransformer.modifiedsbert.losses.SupConLosswandb If you use our…
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…
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…