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
| { | |
| "_sliding_window_pattern": 6, | |
| "architectures": [ | |
| "Gemma3TextModel" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "attn_logit_softcapping": null, | |
| "bos_token_id": 2, | |
| "dtype": "float32", | |
| "eos_token_id": 1, | |
| "final_logit_softcapping": null, | |
| "head_dim": 256, | |
| "hidden_activation": "gelu_pytorch_tanh", | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1152, | |
| "layer_types": [ | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention" | |
| ], | |
| "max_position_embeddings": 2048, | |
| "model_type": "gemma3_text", | |
| "num_attention_heads": 3, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 1, | |
| "pad_token_id": 0, | |
| "query_pre_attn_scalar": 256, | |
| "rms_norm_eps": 1e-06, | |
| "rope_local_base_freq": 10000.0, | |
| "rope_scaling": null, | |
| "rope_theta": 1000000.0, | |
| "sliding_window": 257, | |
| "transformers_version": "4.57.0.dev0", | |
| "use_bidirectional_attention": true, | |
| "use_cache": true, | |
| "vocab_size": 262144 | |
| } | |