Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:2806
loss:TripletLoss
text-embeddings-inference
Instructions to use ainativestudio/locomo-e5-large-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ainativestudio/locomo-e5-large-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ainativestudio/locomo-e5-large-finetuned") sentences = [ "What kind of interests do Joanna and Nate share?", "[4:21 pm on 16 July, 2023] John: John scored 40 points in a game last week, his highest ever.", "[2:01 pm on 23 January, 2022] Nate: Thanks! The turtles might be small, but both sure have big personalities. I really reccomend having something like these little guys for times of stress.", "[11:54 am on 2 May, 2022] Joanna: Joanna has been working on some projects and testing dairy-free dessert recipes for friends and family." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 578 Bytes
92c404c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"architectures": [
"BertModel"
],
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"dtype": "float32",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.57.6",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
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