Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use ToolBench/ToolBench_IR_bert_based_uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ToolBench/ToolBench_IR_bert_based_uncased with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ToolBench/ToolBench_IR_bert_based_uncased") 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 ToolBench/ToolBench_IR_bert_based_uncased with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ToolBench/ToolBench_IR_bert_based_uncased") model = AutoModel.from_pretrained("ToolBench/ToolBench_IR_bert_based_uncased") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2d890e9be009e45862598bb18b401370c0dcc433344cf7ea8a23b22e47329ce
- Size of remote file:
- 438 MB
- SHA256:
- 4d126eb20188ddc4bd6e11fc61c472c2cf81713e973433d06b4fb86264b48e15
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