Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen3
text-generation
feature-extraction
qwen
recruitment
LoRA
text-embeddings-inference
Instructions to use JayThinkDiff/CRE-1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JayThinkDiff/CRE-1.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JayThinkDiff/CRE-1.1") 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 JayThinkDiff/CRE-1.1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("JayThinkDiff/CRE-1.1") model = AutoModelForCausalLM.from_pretrained("JayThinkDiff/CRE-1.1") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -19,7 +19,7 @@ base_model:
|
|
| 19 |
---
|
| 20 |
|
| 21 |
### ζ΄ζ°ζ₯εΏ (Release Notes)
|
| 22 |
-
* **
|
| 23 |
* **2025/03/28**: εεΈ **CRE-0.5** εε§ηζ¬εζζ―ζ₯εγ
|
| 24 |
|
| 25 |
### π ζζ―θζ― (Technical Report Summary)
|
|
|
|
| 19 |
---
|
| 20 |
|
| 21 |
### ζ΄ζ°ζ₯εΏ (Release Notes)
|
| 22 |
+
* **2025/06/28**: εεΈ **CRE-1.1**οΌδΌειΏζζ¬ηΉεΎζεδΈζ¨ηζ§θ½γ
|
| 23 |
* **2025/03/28**: εεΈ **CRE-0.5** εε§ηζ¬εζζ―ζ₯εγ
|
| 24 |
|
| 25 |
### π ζζ―θζ― (Technical Report Summary)
|