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DChak2000
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qwen3-8b-coliee-trace-align

Sentence Similarity
sentence-transformers
Safetensors
qwen3
feature-extraction
Generated from Trainer
dataset_size:8132
loss:CosineSimilarityLoss
text-embeddings-inference
Model card Files
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SentenceTransformer based on Qwen/Qwen3-Embedding-8B

This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-8B. It maps sentences & paragraphs to a 4096-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Base model: Qwen/Qwen3-Embedding-8B
  • Maximum Sequence Length: 40960 tokens
  • Output Dimensionality: 4096 dimensions
  • Similarity Function: Cosine Similarity
  • Supported Modality: Text

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'Qwen3Model'})
  (1): Pooling({'embedding_dimension': 4096, 'pooling_mode': 'lasttoken', 'include_prompt': True})
  (2): Normalize({})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("DChak2000/qwen3-8b-coliee-trace-align")
# Run inference
queries = [
    'The person is deemed to have died when the danger had passed.',
]
documents = [
    'AND(SUBJECT_TO(person, declaration_disappearance_para1), DEEMED_DIED(person, time_period_end))',
    'REQUEST(public_prosecutor, curatorship)',
    'OR(NOT EXERCISE(right, three_years_from(KNOW(victim_or_legal_rep, damage_and_perpetrator))), NOT EXERCISE(right, twenty_years_from(tortious_act)))',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 4096] [3, 4096]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.4691, -0.0578,  0.0996]])

Training Details

Training Dataset

Unnamed Dataset

  • Size: 8,132 training samples
  • Columns: sentence_0, sentence_1, and label
  • Approximate statistics based on the first 100 samples:
    sentence_0 sentence_1 label
    type string string float
    modality text text
    details
    • min: 7 tokens
    • mean: 22.08 tokens
    • max: 78 tokens
    • min: 6 tokens
    • mean: 20.26 tokens
    • max: 133 tokens
    • min: 0.0
    • mean: 0.88
    • max: 1.0
  • Samples:
    sentence_0 sentence_1 label
    The obligor makes payment for the claim. PERMIT(PAYMENT(obligor, japanese_currency)) 0.0
    According to Article 96, a manifestation of intention based on fraud or duress is voidable. QUERY(Article_96) 1.0
    The offeror is in a constant state wherein they lack mental capacity. LACK_CAPACITY(offeror) 1.0
  • Loss: CosineSimilarityLoss with these parameters:
    {
    "loss_fct": "torch.nn.modules.loss.MSELoss",
    "cos_score_transformation": "torch.nn.modules.linear.Identity"
}

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • prediction_loss_only: True
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 5e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1
  • num_train_epochs: 3
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.0
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • fp16: False
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: False
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • skip_memory_metrics: True
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: None
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • include_for_metrics: []
  • eval_do_concat_batches: True
  • fp16_backend: auto
  • push_to_hub_model_id: None
  • push_to_hub_organization: None
  • mp_parameters:
  • auto_find_batch_size: False
  • full_determinism: False
  • torchdynamo: None
  • ray_scope: last
  • ddp_timeout: 1800
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • include_tokens_per_second: False
  • include_num_input_tokens_seen: False
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • eval_on_start: False
  • use_liger_kernel: False
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: round_robin
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Epoch Step Training Loss
0.9823 500 0.08
1.9646 1000 0.0392
2.9470 1500 0.0305

Training Time

  • Training: 1.7 hours

Framework Versions

  • Python: 3.10.20
  • Sentence Transformers: 5.5.1
  • Transformers: 4.52.4
  • PyTorch: 2.5.1+cu124
  • Accelerate: 1.7.0
  • Datasets: 3.1.0
  • Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers 

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
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