README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:3156
- loss:CosineSimilarityLoss
base_model: Qwen/Qwen3-Embedding-8B
widget:
- source_sentence: The goal nonvar('2019-12-31') succeeded, indicating that the date
    2019‑12‑31 is instantiated.
  sentences:
  - 'Succeeded: day_to_stamp("2019-12-31",1577836800.0)'
  - 'Failed: s1_c_iii(22895,3538)'
  - 'Failed: agent_(alice_dies,bob)'
- source_sentence: The date September 1, 2015 corresponds to the Unix timestamp 1441152000.0.
  sentences:
  - 'Failed: son_(_20022)'
  - 'Succeeded: day_to_stamp("2015-09-01",1441152000.0)'
  - 'Failed: s1_c_iv(102268,27225)'
- source_sentence: Alice is the employer of Bob.
  sentences:
  - 'Succeeded: agent_(alice_employer,bob)'
  - 'Succeeded: day_to_stamp("2019-10-10",1570752000.0)'
  - 'Failed: s7703_a(alice,_18952,_18954,2016)'
- source_sentence: The first day of tax year 2014 is January 1, 2014.
  sentences:
  - 'Succeeded: nonvar("2019-10-10")'
  - 'Succeeded: first_day_year(2018,"2018-01-01")'
  - 'Succeeded: first_day_year(2014,"2014-01-01")'
- source_sentence: Under section 1(a)(iv), the tax on $164,612 of taxable income is
    $44,789.
  sentences:
  - 'Succeeded: 2019 is 2018+1'
  - 'Succeeded: var(_21490)'
  - 'Succeeded: 44789 is round(35928.5+(164612-140000)*0.36)'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on Qwen/Qwen3-Embedding-8B

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B). It maps sentences & paragraphs to a 4096-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Qwen/Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) <!-- at revision 1d8ad4ca9b3dd8059ad90a75d4983776a23d44af -->
- **Maximum Sequence Length:** 40960 tokens
- **Output Dimensionality:** 4096 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### 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})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("DChak2000/qwen3-trace-align")
# Run inference
queries = [
    "Under section 1(a)(iv), the tax on $164,612 of taxable income is $44,789.",
]
documents = [
    'Succeeded: 44789 is round(35928.5+(164612-140000)*0.36)',
    'Succeeded: 2019 is 2018+1',
    'Succeeded: var(_21490)',
]
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([[0.0548, 0.3047, 0.3684]])
```

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 3,156 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                         | label                                                          |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | float                                                          |
  | details | <ul><li>min: 5 tokens</li><li>mean: 23.92 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 23.48 tokens</li><li>max: 241 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.83</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                                              | sentence_1                                                      | label            |
  |:----------------------------------------------------------------------------------------|:----------------------------------------------------------------|:-----------------|
  | <code>The marriage predicate could not be satisfied.</code>                             | <code>Failed: marriage_(_19298)</code>                          | <code>1.0</code> |
  | <code>The last day of the year 2018 is 2018-12-31.</code>                               | <code>Succeeded: last_day_year(2018,"2018-12-31")</code>        | <code>1.0</code> |
  | <code>The conversion of the date 2019‑11‑03 to a timestamp yielded 1572825600.0.</code> | <code>Succeeded: day_to_stamp("2019-11-03",1572825600.0)</code> | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `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`: None
- `warmup_ratio`: None
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `enable_jit_checkpoint`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `bf16`: False
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: -1
- `ddp_backend`: None
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `auto_find_batch_size`: False
- `full_determinism`: False
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `use_cache`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 1.2658 | 500  | 0.0683        |
| 2.5316 | 1000 | 0.0309        |


### Framework Versions
- Python: 3.10.19
- Sentence Transformers: 5.2.2
- Transformers: 5.0.0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.12.0
- Datasets: 4.5.0
- Tokenizers: 0.22.2

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@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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