Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:42280
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Netizine/icis_commodity_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Netizine/icis_commodity_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Netizine/icis_commodity_embedding") sentences = [ "How is demand from blown film converters trending for natural-colour rLDPE pellets sourced from production scrap in Germany?", "For a tender closing Friday, market participants indicated post-industrial, food-grade HDPE bales could be workable around €1,030-1,110/t DAP Valencia for prompt-to-March delivery, depending on lot size and delivery flexibility.", "Demand from German blown-film converters for natural rLDPE pellets sourced from production scrap was steady to slightly firmer week on week, though buyers continued to push back on offers above the low-to-mid €1,200s/t FCA level.", "Europe recycled high-density polyethylene (R-HDPE) blow-moulding natural pellet demand continues to increase on the back of new packaging projects and increased recycled content use from the packaging sector." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 312 Bytes
4676c9e | 1 2 3 4 5 6 7 8 9 10 | {
"word_embedding_dimension": 768,
"pooling_mode_cls_token": false,
"pooling_mode_mean_tokens": true,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false,
"pooling_mode_weightedmean_tokens": false,
"pooling_mode_lasttoken": false,
"include_prompt": true
} |