Sentence Similarity
sentence-transformers
ONNX
Safetensors
English
qwen2
medical
clinicaltrials
cancer
feature-extraction
Generated from Trainer
dataset_size:1395384
loss:OnlineContrastiveLoss
loss:MultipleNegativesRankingLoss
custom_code
text-embeddings-inference
Instructions to use jim-bo/TrialSpace with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jim-bo/TrialSpace with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jim-bo/TrialSpace", trust_remote_code=True) 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] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from jim-bo/TrialSpace: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/jim-bo/TrialSpace/resolve/main/tokenizer.json
- Command line
-
hf download hf://jim-bo/TrialSpace/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/jim-bo/TrialSpace/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 6cd1dde720bf0ede085c72eb784d078beb15b76a7b3e6f7831ba437494284714
- Size of remote file:
- 11.4 MB
- SHA256:
- d8372feaa064372d176aff57e8f1e64f194814bb074519104f64c66a2825f091
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