Instructions to use malteos/scincl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use malteos/scincl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("malteos/scincl") 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] - Transformers
How to use malteos/scincl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="malteos/scincl", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("malteos/scincl") model = AutoModel.from_pretrained("malteos/scincl", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -24,7 +24,7 @@ Paper: [Neighborhood Contrastive Learning for Scientific Document Representation
|
|
| 24 |
|
| 25 |
Code: https://github.com/malteos/scincl
|
| 26 |
|
| 27 |
-
PubMedNCL:
|
| 28 |
|
| 29 |
## How to use the pretrained model
|
| 30 |
|
|
|
|
| 24 |
|
| 25 |
Code: https://github.com/malteos/scincl
|
| 26 |
|
| 27 |
+
PubMedNCL: Working with biomedical papers? Try [PubMedNCL](https://huggingface.co/malteos/PubMedNCL).
|
| 28 |
|
| 29 |
## How to use the pretrained model
|
| 30 |
|