Feature Extraction
sentence-transformers
Safetensors
English
Portuguese
lexical_embedding
custom_code
Instructions to use cnmoro/LexicalEmbed-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cnmoro/LexicalEmbed-Base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cnmoro/LexicalEmbed-Base", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -14,6 +14,7 @@ This is a model trained on [cnmoro/LexicalTriplets](https://huggingface.co/datas
|
|
| 14 |
This can be used to compute lexical similarity between words or phrases.
|
| 15 |
|
| 16 |
Concept:
|
|
|
|
| 17 |
"Some text" will be similar to "Sm txt"
|
| 18 |
|
| 19 |
"King" will **not** be similar to "Queen" or "Royalty"
|
|
@@ -22,8 +23,6 @@ Concept:
|
|
| 22 |
|
| 23 |
"Doge" will be similar to "Dog"
|
| 24 |
|
| 25 |
-
This will be trained for 2 epochs. The current model here is the first one.
|
| 26 |
-
|
| 27 |
```python
|
| 28 |
import torch, re, unicodedata
|
| 29 |
from transformers import AutoModel, AutoTokenizer
|
|
|
|
| 14 |
This can be used to compute lexical similarity between words or phrases.
|
| 15 |
|
| 16 |
Concept:
|
| 17 |
+
|
| 18 |
"Some text" will be similar to "Sm txt"
|
| 19 |
|
| 20 |
"King" will **not** be similar to "Queen" or "Royalty"
|
|
|
|
| 23 |
|
| 24 |
"Doge" will be similar to "Dog"
|
| 25 |
|
|
|
|
|
|
|
| 26 |
```python
|
| 27 |
import torch, re, unicodedata
|
| 28 |
from transformers import AutoModel, AutoTokenizer
|