Sentence Similarity
sentence-transformers
PyTorch
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)
Instructions to use Fuurkan/chatbot-instructor-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Fuurkan/chatbot-instructor-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Fuurkan/chatbot-instructor-model") 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 Fuurkan/chatbot-instructor-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Fuurkan/chatbot-instructor-model") model = AutoModel.from_pretrained("Fuurkan/chatbot-instructor-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a06aa69f2c0ffe2b5d6b8eee6f9f3e833d82c7f854542574ea0039dcac1fc2b
- Size of remote file:
- 1.34 GB
- SHA256:
- fb7f63752cf10103be938bc50dfad0b6fa1e63bc67b963471fc827838d9bbb41
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.