Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use pritamdeka/S-PubMedBert-MS-MARCO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use pritamdeka/S-PubMedBert-MS-MARCO with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("pritamdeka/S-PubMedBert-MS-MARCO") 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 pritamdeka/S-PubMedBert-MS-MARCO with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pritamdeka/S-PubMedBert-MS-MARCO") model = AutoModel.from_pretrained("pritamdeka/S-PubMedBert-MS-MARCO", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
| { | |
| "calculate_probabilities": false, | |
| "language": "english", | |
| "low_memory": false, | |
| "min_topic_size": 10, | |
| "n_gram_range": [ | |
| 1, | |
| 1 | |
| ], | |
| "nr_topics": null, | |
| "seed_topic_list": null, | |
| "top_n_words": 10, | |
| "verbose": true, | |
| "zeroshot_min_similarity": 0.7, | |
| "zeroshot_topic_list": null, | |
| "embedding_model": "sentence-transformers/all-MiniLM-L6-v2" | |
| } |