đ sentence-transformers/static-retrieval-mrl-en-v1
This is a sentence-transformers model. It's fine - tuned from nomic-ai/nomic-embed-text-v1.5 on the sci_gen_colbert_triplets dataset. It maps academic text sentences to a 768 - dimensional dense vector space based on rhetorical functions like summarizing results or expressing limitations. It can be used for functional textual similarity, limitations analysis, rhetorical function classification, clustering, etc.
đ Quick Start
First, install the Sentence Transformers library:
pip install -U sentence-transformers
Then, you can load this model and run inference.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("KaiserML/RhetoriBERT")
sentences = [
'Surveys and interviews: Introducing excerpts from interview data',
"Through surveys and interviews, multiliterate teachers expressed a shared belief in the importance of fostering students' ability to navigate multiple discourse communities.",
'The authors employ a constructivist approach to learning, where students build knowledge through active engagement with multimedia texts and collaborative discussions.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
⨠Features
- Maps sentences from academic texts to a 768 - dimensional dense vector space based on rhetorical functions.
- Can be used for multiple tasks such as functional textual similarity, limitations analysis, rhetorical function classification, and clustering.
đĻ Installation
pip install -U sentence-transformers
đģ Usage Examples
Basic Usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("KaiserML/RhetoriBERT")
sentences = [
'Surveys and interviews: Introducing excerpts from interview data',
"Through surveys and interviews, multiliterate teachers expressed a shared belief in the importance of fostering students' ability to navigate multiple discourse communities.",
'The authors employ a constructivist approach to learning, where students build knowledge through active engagement with multimedia texts and collaborative discussions.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
đ Documentation
Model Details
Model Description
Property |
Details |
Model Type |
Sentence Transformer |
Base model |
nomic-ai/nomic-embed-text-v1.5 |
Maximum Sequence Length |
8192 tokens |
Output Dimensionality |
768 dimensions |
Similarity Function |
Cosine Similarity |
Training Dataset |
sci_gen_colbert_triplets |
Language |
en |
License |
apache - 2.0 |
Model Sources
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
Evaluation
Metrics
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.9 |
cosine_accuracy@3 |
0.9452 |
cosine_accuracy@5 |
0.9642 |
cosine_accuracy@10 |
0.9853 |
cosine_precision@1 |
0.9 |
cosine_precision@3 |
0.3151 |
cosine_precision@5 |
0.1928 |
cosine_precision@10 |
0.0985 |
cosine_recall@1 |
0.9 |
cosine_recall@3 |
0.9452 |
cosine_recall@5 |
0.9642 |
cosine_recall@10 |
0.9853 |
cosine_ndcg@10 |
0.9415 |
cosine_mrr@10 |
0.9276 |
cosine_map@100 |
0.9284 |
Training Details
Training Dataset
- Dataset: sci_gen_colbert_triplets at 44071bd
- Size: 35,934 training samples
- Columns:
query
, positive
, and negative
- Approximate statistics based on the first 1000 samples:
|
query |
positive |
negative |
type |
string |
string |
string |
details |
- min: 5 tokens
- mean: 10.24 tokens
- max: 23 tokens
|
- min: 2 tokens
- mean: 39.86 tokens
- max: 80 tokens
|
- min: 18 tokens
- mean: 40.41 tokens
- max: 88 tokens
|
- Samples:
query |
positive |
negative |
Previous research: highlighting negative outcomes |
Despite the widespread use of seniority - based wage systems in labor contracts, previous research has highlighted their negative outcomes, such as inefficiencies and demotivating effects on workers. |
This paper, published in 1974, was among the first to establish the importance of rank - order tournaments as optimal labor contracts in microeconomics. |
Synthesising sources: contrasting evidence or ideas |
Despite the observed chronic enterocolitis in Interleukin - 10 - deficient mice, some studies suggest that this cytokine plays a protective role in intestinal inflammation in humans (Kurimoto et al., 2001). |
Chronic enterocolitis developed in Interleukin - 10 - deficient mice, characterized by inflammatory cell infiltration, epithelial damage, and increased production of pro - inflammatory cytokines. |
Previous research: Approaches taken |
Previous research on measuring patient - relevant outcomes in osteoarthritis has primarily relied on self - reported measures, such as the Western Ontario and McMaster Universities Arthritis Index (WOMAC) (Bellamy et al., 1988). |
The WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) questionnaire has been widely used in physical therapy research to assess the impact of antirheumatic drug therapy on patient - reported outcomes in individuals with hip or knee osteoarthritis. |
- Loss:
MatryoshkaLoss
with these parameters:{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
384,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
Evaluation Dataset
- Dataset: sci_gen_colbert_triplets at 44071bd
- Size: 4,492 evaluation samples
- Columns:
query
, positive
, and negative
- Approximate statistics based on the first 1000 samples:
|
query |
positive |
negative |
type |
string |
string |
string |
details |
- min: 5 tokens
- mean: 10.23 tokens
- max: 23 tokens
|
- min: 18 tokens
- mean: 39.83 tokens
- max: 84 tokens
|
- min: 8 tokens
- mean: 39.89 tokens
- max: 84 tokens
|
- Samples:
query |
positive |
negative |
Providing background information: reference to the purpose of the study |
This study aimed to investigate the impact of socioeconomic status on child development, specifically focusing on cognitive, language, and social - emotional domains. |
Children from high socioeconomic status families showed significantly higher IQ scores (M = 112.5, SD = 5.6) compared to children from low socioeconomic status families (M = 104.3, SD = 6.2) in the verbal IQ subtest. |
Providing background information: reference to the literature |
According to previous studies using WinGX suite for small - molecule single - crystal crystallography, the optimization of crystal structures leads to improved accuracy in determining atomic coordinates. |
This paper describes the WinGX suite, a powerful tool for small - molecule single - crystal crystallography that significantly advances the field of crystallography by streamlining data collection and analysis. |
General comments on the relevant literature |
Polymer brushes have gained significant attention in the field of polymer science due to their unique properties, such as controlled thickness, high surface density, and tunable interfacial properties. |
Despite previous reports suggesting that polymer brushes with short grafting densities exhibit poorer performance in terms of adhesion and stability compared to those with higher grafting densities (Liu et al., 2010), our results indicate that the opposite |
đ License
The model is licensed under the apache - 2.0 license.