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-un-data-fine-coarse-es
by Dell Research Harvard dell-research-harvard/lt-un-data-fine-coarse-es
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.
Parameters110M
Context512
Weights439.4 MB
License—
AccessOpen weights
Monthly Downloads78
Runs On
What it takes to serve lt-un-data-fine-coarse-es (110M 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
- BertModel
- Context length (tokens)
- 512
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 31,002
- Stored precision
- float32
- Model type
- bert
Identity and Version
- Repository
- dell-research-harvard/lt-un-data-fine-coarse-es
- Publisher
- Dell Research Harvard
- Task
- Sentence similarity
- Modality
- Text
- Library
- sentence-transformers
- Parameters
- 110M parameters
- Languages
- es
- Revision
- 702d2dba008e3abe84c08411e72b60146e7165a7
- First published
- 2023-08-28
- Last updated
- 2024-03-20
Files and Weights
18 files, 440.9 MB in total. The weights are 1 file totalling 439.4 MB in safetensors.
Weights1 file · 439.4 MB
Configuration7 files · 2.8 KB
Tokenizer3 files · 972.8 KB
Documentation1 file · 5.9 KB
Other5 files · 523.5 KB
Repository1 file · 407 B
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 439.4 MB | ea87e6be6f24 |
| 1_Pooling/config.json | Configuration | 270 B | — |
| LT_training_config.json | Configuration | 1.3 KB | — |
| config.json | Configuration | 703 B | — |
| config_sentence_transformers.json | Configuration | 123 B | — |
| modules.json | Configuration | 229 B | — |
| sentence_bert_config.json | Configuration | 53 B | — |
| special_tokens_map.json | Configuration | 125 B | — |
| README.md | Documentation | 5.9 KB | — |
| Information-Retrieval_evaluation_eval_results.csv | Other | 1.5 KB | — |
| Information-Retrieval_evaluation_test_results.csv | Other | 1.4 KB | — |
| eval/Information-Retrieval_evaluation_eval_results.csv | Other | 246.5 KB | — |
| test_data.pickle | Other | 42.4 KB | 20dc4b792591 |
| val_data.pickle | Other | 231.7 KB | 9d513cbf6df1 |
| .gitattributes | Repository | 407 B | — |
| tokenizer.json | Tokenizer | 729.6 KB | — |
| tokenizer_config.json | Tokenizer | 1.4 KB | — |
| vocab.txt | Tokenizer | 241.8 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 439.4 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 | 439.4 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-un-data-fine-coarse-es
How much GPU memory does lt-un-data-fine-coarse-es need?
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (110M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run lt-un-data-fine-coarse-es 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-un-data-fine-coarse-es's context length?
512 tokens, from the maximum position embeddings in its published configuration.
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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…
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