Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
dpr
text-embeddings-inference
Instructions to use headlesstech/semantic_xlmr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use headlesstech/semantic_xlmr with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("headlesstech/semantic_xlmr") sentences = [ "আমি বাংলায় গান গাই", "I sing in Bangla", "I sing in Bengali", "I sing in English", "আমি গান গাই না " ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Transformers
How to use headlesstech/semantic_xlmr with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("headlesstech/semantic_xlmr") model = AutoModel.from_pretrained("headlesstech/semantic_xlmr") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0e7b39b4accf676f661b5c03422793f43d64f80cd6feb98550b794315d18df83
|
| 3 |
+
size 1112201288
|