Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Fantasim/logg-grouping-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Fantasim/logg-grouping-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Fantasim/logg-grouping-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 Fantasim/logg-grouping-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Fantasim/logg-grouping-model") model = AutoModel.from_pretrained("Fantasim/logg-grouping-model") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 1, | |
| "global_step": 200, | |
| "best_val_f1": 0.7815, | |
| "best_threshold": 0.76, | |
| "val_metrics": { | |
| "threshold": 0.76, | |
| "f1": 0.7815, | |
| "precision": 0.823, | |
| "recall": 0.744, | |
| "n_clusters_pred": 80, | |
| "n_clusters_true": 30 | |
| } | |
| } |