Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
Transformers.js
English
nomic_bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use Karmukilan/nomic-embed-text-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Karmukilan/nomic-embed-text-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Karmukilan/nomic-embed-text-v1", 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] - Transformers
How to use Karmukilan/nomic-embed-text-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Karmukilan/nomic-embed-text-v1", trust_remote_code=True) model = AutoModel.from_pretrained("Karmukilan/nomic-embed-text-v1", trust_remote_code=True) - Transformers.js
How to use Karmukilan/nomic-embed-text-v1 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'Karmukilan/nomic-embed-text-v1'); - Notebooks
- Google Colab
- Kaggle
File size: 2,029 Bytes
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"activation_function": "swiglu",
"architectures": ["NomicBertModel"],
"attn_pdrop": 0.0,
"attention_probs_dropout_prob": 0.0,
"auto_map": {
"AutoConfig": "nomic-ai/nomic-bert-2048--configuration_hf_nomic_bert.NomicBertConfig",
"AutoModel": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertModel",
"AutoModelForMaskedLM": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertForPreTraining"
},
"bos_token_id": null,
"causal": false,
"classifier_dropout": null,
"dense_seq_output": true,
"embd_pdrop": 0.0,
"eos_token_id": null,
"fused_bias_fc": true,
"fused_dropout_add_ln": true,
"head_dim": 64,
"hidden_act": "silu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_epsilon": 1e-12,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 8192,
"mlp_fc1_bias": false,
"mlp_fc2_bias": false,
"model_type": "nomic_bert",
"n_embd": 768,
"n_head": 12,
"n_inner": 3072,
"n_layer": 12,
"n_positions": 8192,
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"pad_vocab_size_multiple": 64,
"parallel_block": false,
"parallel_block_tied_norm": false,
"prenorm": false,
"qkv_proj_bias": false,
"reorder_and_upcast_attn": false,
"resid_pdrop": 0.0,
"rope_parameters": {
"rope_theta": 1000.0,
"rope_type": "dynamic",
"factor": 2.0
},
"rotary_emb_base": 1000,
"rotary_emb_fraction": 1.0,
"rotary_emb_interleaved": false,
"rotary_emb_scale_base": null,
"rotary_scaling_factor": 2,
"scale_attn_by_inverse_layer_idx": false,
"scale_attn_weights": true,
"summary_activation": null,
"summary_first_dropout": 0.1,
"summary_proj_to_labels": true,
"summary_type": "cls_index",
"summary_use_proj": true,
"torch_dtype": "float32",
"transformers_version": "5.3.0.dev0",
"type_vocab_size": 2,
"use_cache": true,
"use_flash_attn": true,
"use_rms_norm": false,
"use_xentropy": true,
"vocab_size": 30528
}
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