# Question-Answer Matching
Reranker MiniLM L6 H384 Uncased Gooaq 5 Epoch 1995000
Apache-2.0
This is a cross-encoder model fine-tuned from nreimers/MiniLM-L6-H384-uncased, designed for computing scores of text pairs, suitable for text re-ranking and semantic search tasks.
Text Embedding English
R
ayushexel
24
0
Reranker ModernBERT Base Gooaq 1 Epoch 1995000
Apache-2.0
This is a cross-encoder model fine-tuned from ModernBERT-base, designed for calculating scores of text pairs, suitable for text reordering and semantic search tasks.
Text Embedding English
R
ayushexel
30
0
Reranker ModernBERT Base Gooaq Bce
Apache-2.0
This is a cross-encoder model fine-tuned from ModernBERT-base for text re-ranking and semantic search tasks.
Text Embedding English
R
tomaarsen
483
2
Multilingual Text Semantic Search Siamese BERT V1
A multilingual text semantic search model based on Siamese-BERT architecture, trained on 215 million (question, answer) pairs to generate 384-dimensional normalized embedding vectors
Text Embedding
M
SeyedAli
166
4
Distilroberta Base Sentence Transformer
This is a DistilRoBERTa-based sentence transformer model capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks like semantic search and clustering.
Text Embedding
Transformers

D
embedding-data
30
1
English Phrases Bible
Apache-2.0
A sentence embedding model based on DistilBert TAS-B, optimized for semantic search tasks, capable of mapping text to a 768-dimensional vector space
Text Embedding
Transformers

E
iamholmes
28
0
Qnli Distilroberta Base
Apache-2.0
This model is a cross-encoder trained on distilroberta-base for determining whether a given passage can answer a specific question, trained on the GLUE QNLI dataset.
Question Answering System English
Q
cross-encoder
1,526
0
Qnli Electra Base
Apache-2.0
This is a cross-encoder model based on the ELECTRA architecture, specifically designed for natural language inference (NLI) in question-answering tasks, determining whether a given question can be answered by a specific paragraph.
Question Answering System
Transformers English

Q
cross-encoder
6,172
3
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