sentence-transformers/all-nli
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How to use antarge/minilm-finetuned with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("antarge/minilm-finetuned")
model = AutoModel.from_pretrained("antarge/minilm-finetuned", device_map="auto")How to use antarge/minilm-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("antarge/minilm-finetuned")
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]Finetuned version of sentence-transformers/all-MiniLM-L6-v2 for sentence embedding.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("antarge/minilm-finetuned")
import torch
ckpt = torch.load("pytorch_model.bin", weights_only=False, map_location="cpu")
This is a finetuned version of sentence-transformers/all-MiniLM-L6-v2.
MIT
Base model
nreimers/MiniLM-L6-H384-uncased