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 inesaltemir/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use inesaltemir/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("inesaltemir/MNLP_M2_document_encoder", 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 inesaltemir/MNLP_M2_document_encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("inesaltemir/MNLP_M2_document_encoder", trust_remote_code=True) model = AutoModel.from_pretrained("inesaltemir/MNLP_M2_document_encoder", trust_remote_code=True) - Transformers.js
How to use inesaltemir/MNLP_M2_document_encoder with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'inesaltemir/MNLP_M2_document_encoder'); - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +3 -3
config.json
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"architectures": [
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"NomicBertModel"
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],
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"attn_pdrop": 0.
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"auto_map": {
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"AutoConfig": "configuration_hf_nomic_bert.NomicBertConfig",
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"AutoModel": "modeling_hf_nomic_bert.NomicBertModel",
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"prenorm": false,
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"qkv_proj_bias": false,
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.
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"rotary_emb_base": 1000,
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"rotary_emb_fraction": 1.0,
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"rotary_emb_interleaved": false,
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"architectures": [
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"NomicBertModel"
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],
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"attn_pdrop": 0.1,
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"auto_map": {
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"AutoConfig": "configuration_hf_nomic_bert.NomicBertConfig",
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"AutoModel": "modeling_hf_nomic_bert.NomicBertModel",
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"prenorm": false,
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"qkv_proj_bias": false,
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.1,
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"rotary_emb_base": 1000,
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"rotary_emb_fraction": 1.0,
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"rotary_emb_interleaved": false,
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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