Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:7004
loss:SoftmaxLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Deehan1866/finetuned-bert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Deehan1866/finetuned-bert-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Deehan1866/finetuned-bert-large") sentences = [ "The valve will open 100% when the set point is reached and will remain open until a certain blow down factor is reached.", "Having raised $17,000,000 in a standard matter, one of the first speculative IPOs, Tucker needed more money to continue development of the car.", "The valve will open 100% when the tennis scoring protocol is reached and will remain open until a certain blow down factor is reached.", "But the government of PML (N) gave it the complete exponential of a Tehsil." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |