Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

Duplicated from  hkunlp/instructor-large

ahmetcangunay
/
finetuned_embedder

Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
English
t5
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
prompt-retrieval
text-reranking
feature-extraction
English
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
Eval Results (legacy)
Model card Files Files and versions
xet
Community
1

Instructions to use ahmetcangunay/finetuned_embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use ahmetcangunay/finetuned_embedder with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("ahmetcangunay/finetuned_embedder")
    
    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 ahmetcangunay/finetuned_embedder with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("ahmetcangunay/finetuned_embedder")
    model = AutoModel.from_pretrained("ahmetcangunay/finetuned_embedder", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
finetuned_embedder
2.69 GB
Ctrl+K
Ctrl+K
  • 3 contributors
History: 3 commits
Ahmet Can GÜNAY
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model (#1)
6103ab8 verified over 1 year ago
  • 1_Pooling
    Duplicate from hkunlp/instructor-large over 2 years ago
  • 2_Dense
    Adding `safetensors` variant of this model (#1) over 1 year ago
  • .gitattributes
    1.48 kB
    Duplicate from hkunlp/instructor-large over 2 years ago
  • README.md
    59.9 kB
    Update README.md over 2 years ago
  • config.json
    1.53 kB
    Duplicate from hkunlp/instructor-large over 2 years ago
  • config_sentence_transformers.json
    122 Bytes
    Duplicate from hkunlp/instructor-large over 2 years ago
  • model.safetensors
    1.34 GB
    xet
    Adding `safetensors` variant of this model (#1) over 1 year ago
  • modules.json
    461 Bytes
    Duplicate from hkunlp/instructor-large over 2 years ago
  • pytorch_model.bin
    1.34 GB
    xet
    Duplicate from hkunlp/instructor-large over 2 years ago
  • sentence_bert_config.json
    53 Bytes
    Duplicate from hkunlp/instructor-large over 2 years ago
  • special_tokens_map.json
    2.2 kB
    Duplicate from hkunlp/instructor-large over 2 years ago
  • spiece.model
    792 kB
    xet
    Duplicate from hkunlp/instructor-large over 2 years ago
  • tokenizer.json
    2.42 MB
    Duplicate from hkunlp/instructor-large over 2 years ago
  • tokenizer_config.json
    2.41 kB
    Duplicate from hkunlp/instructor-large over 2 years ago