Sentence Similarity
sentence-transformers
Safetensors
English
phi
mteb
custom_code
Eval Results (legacy)
Instructions to use tanmaylaud/ret-phi2-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tanmaylaud/ret-phi2-v0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tanmaylaud/ret-phi2-v0", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 891 Bytes
a9624b3 | 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 26 27 28 29 30 31 32 33 34 35 | {
"_name_or_path": "microsoft/phi-2",
"architectures": [
"PhiModel"
],
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "microsoft/phi-2--configuration_phi.PhiConfig",
"AutoModelForCausalLM": "microsoft/phi-2--modeling_phi.PhiForCausalLM"
},
"bos_token_id": 50256,
"embd_pdrop": 0.0,
"eos_token_id": 50256,
"hidden_act": "gelu_new",
"hidden_size": 2560,
"initializer_range": 0.02,
"intermediate_size": 10240,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 2048,
"model_type": "phi",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 32,
"partial_rotary_factor": 0.4,
"qk_layernorm": false,
"resid_pdrop": 0.1,
"rope_scaling": null,
"rope_theta": 10000.0,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.36.2",
"use_cache": true,
"vocab_size": 51200
}
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