Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:73579
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use GozdeA/tennis-multi-return-categorizer-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use GozdeA/tennis-multi-return-categorizer-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GozdeA/tennis-multi-return-categorizer-v1") sentences = [ "Tell me about gaining control for Gauff", "how many winners?", "Show me gaining control", "Tell me about gaining momentum for Gauff" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Upload label_mapping.json with huggingface_hub
Browse files- label_mapping.json +8 -0
label_mapping.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"0": "biographics",
|
| 3 |
+
"1": "live",
|
| 4 |
+
"2": "logistics",
|
| 5 |
+
"3": "match_statistics",
|
| 6 |
+
"4": "player_statistics",
|
| 7 |
+
"5": "predictions"
|
| 8 |
+
}
|