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
| { | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "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 | |
| } | |