Feature Extraction
sentence-transformers
Safetensors
Transformers
Russian
English
t5
mteb
Eval Results (legacy)
Instructions to use LeoCristt/hackathon-embedding-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LeoCristt/hackathon-embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LeoCristt/hackathon-embedding-model") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use LeoCristt/hackathon-embedding-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LeoCristt/hackathon-embedding-model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LeoCristt/hackathon-embedding-model") model = AutoModel.from_pretrained("LeoCristt/hackathon-embedding-model") - Notebooks
- Google Colab
- Kaggle
File size: 824 Bytes
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"architectures": [
"T5EncoderModel"
],
"bos_token_id": 1,
"classifier_dropout": 0.0,
"d_ff": 4096,
"d_kv": 64,
"d_model": 1536,
"decoder_start_token_id": 0,
"dense_act_fn": "gelu_new",
"dropout_rate": 0.1,
"dtype": "float32",
"eos_token_id": 2,
"feed_forward_proj": "gated-gelu",
"gradient_checkpointing": false,
"initializer_factor": 1.0,
"is_encoder_decoder": false,
"is_gated_act": true,
"layer_norm_epsilon": 1e-06,
"model_type": "t5",
"num_decoder_layers": 24,
"num_heads": 24,
"num_layers": 24,
"output_past": true,
"pad_token_id": 0,
"relative_attention_max_distance": 128,
"relative_attention_num_buckets": 32,
"tie_word_embeddings": false,
"transformers_version": "4.57.0",
"use_cache": false,
"vocab_size": 93651
}
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