SentenceTransformer based on BAAI/bge-reasoner-embed-qwen3-8b-0923

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: BAAI/bge-reasoner-embed-qwen3-8b-0923
• Maximum Sequence Length: 40960 tokens
• Output Dimensionality: 4096 dimensions
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 40960, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
  (1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

• Dataset: ir_parent_grandparent_combined
• Evaluated with InformationRetrievalEvaluator

Triplet

• Datasets: orig_vs_parent, orig_vs_grandparent, sib_vs_parent and sib_vs_grandparent
• Evaluated with TripletEvaluator

Training Details

Training Dataset

Unnamed Dataset

• Size: 161,986 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 42 tokens mean: 78.81 tokens max: 124 tokens	min: 43 tokens mean: 70.13 tokens max: 108 tokens

• Samples:

  anchor	positive
  LLZO is stable against Li metal and against a high voltage cathode. However, oxides generally require high sintering temperatures to remove grain boundaries to achieve the reported conductivity values. They also tend to be brittle, which makes it harder (relative to the sulfides) to maintain solid-solid interfacial contact and also to process.	Oxide-based solid electrolytes often show excellent electrochemical stability with both lithium metal anodes and high-voltage cathodes, but exploiting their full ionic conductivity generally requires aggressive high-temperature sintering to suppress grain boundary resistance. The resulting brittle ceramic bodies can be challenging to process and to integrate mechanically, particularly when maintaining intimate solid–solid interfacial contact with electrodes is critical.
  LLZO is stable against Li metal and against a high voltage cathode. However, oxides generally require high sintering temperatures to remove grain boundaries to achieve the reported conductivity values. They also tend to be brittle, which makes it harder (relative to the sulfides) to maintain solid-solid interfacial contact and also to process.	Oxide-based solid electrolytes often show excellent electrochemical stability with both lithium metal anodes and high-voltage cathodes, but exploiting their full ionic conductivity generally requires aggressive high-temperature sintering to suppress grain boundary resistance. The resulting brittle ceramic bodies can be challenging to process and to integrate mechanically, particularly when maintaining intimate solid–solid interfacial contact with electrodes is critical.
  LLZO is stable against Li metal and against a high voltage cathode. However, oxides generally require high sintering temperatures to remove grain boundaries to achieve the reported conductivity values. They also tend to be brittle, which makes it harder (relative to the sulfides) to maintain solid-solid interfacial contact and also to process.	Oxide-based solid electrolytes often show excellent electrochemical stability with both lithium metal anodes and high-voltage cathodes, but exploiting their full ionic conductivity generally requires aggressive high-temperature sintering to suppress grain boundary resistance. The resulting brittle ceramic bodies can be challenging to process and to integrate mechanically, particularly when maintaining intimate solid–solid interfacial contact with electrodes is critical.

• Loss: MultipleNegativesRankingLoss with these parameters:
  json

  1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• learning_rate: 1e-05
• num_train_epochs: 1
• warmup_ratio: 0.05
• bf16: True
• disable_tqdm: True
• load_best_model_at_end: True
• push_to_hub: True
• hub_model_id: shahafvl/bge-reasoner-scientific-parent-prompt
• hub_private_repo: False
• auto_find_batch_size: True
• prompts: {'anchor': 'Retrieve the broader scientific generalization or context for the given specific text.\nQuery: '}

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.14.2
• Sentence Transformers: 5.1.2
• Transformers: 4.57.3
• PyTorch: 2.9.1+cu128
• Accelerate: 1.12.0
• Datasets: 4.4.1
• Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss