Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
bert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use TaylorAI/bge-micro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TaylorAI/bge-micro with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TaylorAI/bge-micro") 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 TaylorAI/bge-micro with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TaylorAI/bge-micro") model = AutoModel.from_pretrained("TaylorAI/bge-micro") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
d0f7a44
1
Parent(s): 3921a85
Pushing sentencetransformers model
Browse filesModel distilled from bge-small-v1.5 but ~1/4 the size
- special_tokens_map.json +14 -0
special_tokens_map.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"[PAD]",
|
| 4 |
+
"[UNK]",
|
| 5 |
+
"[CLS]",
|
| 6 |
+
"[SEP]",
|
| 7 |
+
"[MASK]"
|
| 8 |
+
],
|
| 9 |
+
"cls_token": "[CLS]",
|
| 10 |
+
"mask_token": "[MASK]",
|
| 11 |
+
"pad_token": "[PAD]",
|
| 12 |
+
"sep_token": "[SEP]",
|
| 13 |
+
"unk_token": "[UNK]"
|
| 14 |
+
}
|