Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:100
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use leonweber/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use leonweber/checkpoints with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("leonweber/checkpoints") sentences = [ "<start> FTYGHYHHYHGGTTGRREEEEEEEEDEEEE <end>", "on", "later", "The" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 696 Bytes
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"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "LABEL_0"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"LABEL_0": 0
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.52.4",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30524
}
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