Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use omarelsayeed/DISASTER_MODEL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use omarelsayeed/DISASTER_MODEL with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("omarelsayeed/DISASTER_MODEL") 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 omarelsayeed/DISASTER_MODEL with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("omarelsayeed/DISASTER_MODEL") model = AutoModel.from_pretrained("omarelsayeed/DISASTER_MODEL") - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 256, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |