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Qwen3 base Financial

Name: qwen3-base-financial
Availability: InStock

This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B on the json dataset. It maps sentences & paragraphs to a 1024-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-0.6B
Maximum Sequence Length: 32768 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json
Language: en
License: apache-2.0

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': 32768, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
  (1): Pooling({'word_embedding_dimension': 1024, '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:

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("PhilipCisco/qwen3-base-financial")
# Run inference
queries = [
    "Which section of the financial document addresses Financial Statements and Supplementary Data?",
]
documents = [
    'Financial Statements and Supplementary Data are addressed in Item 8 of the financial document.',
    'The 7% Notes due 2029 are scheduled to mature on February 15, 2029.',
    'The gift card liability was $145,014 in 2022 and increased to $164,930 in 2023.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.6634,  0.0292, -0.0534]])

Evaluation

Metrics

Information Retrieval

Dataset: dim_1024
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 1024 3}

Metric	Value
cosine_accuracy@1	0.7179
cosine_accuracy@3	0.8536
cosine_accuracy@5	0.8771
cosine_accuracy@10	0.9207
cosine_precision@1	0.7179
cosine_precision@3	0.2845
cosine_precision@5	0.1754
cosine_precision@10	0.0921
cosine_recall@1	0.7179
cosine_recall@3	0.8536
cosine_recall@5	0.8771
cosine_recall@10	0.9207
cosine_ndcg@10	0.8231
cosine_mrr@10	0.7916
cosine_map@100	0.7948

Training Details

Training Dataset

json

Dataset: json
Size: 5,600 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 7 tokens
mean: 20.73 tokens
max: 50 tokens
min: 10 tokens
mean: 47.95 tokens
max: 431 tokens

Samples:

anchor	positive
`What was the increase in sales and marketing expenses for the year ended December 31, 2023 compared to 2022?`	`Sales and marketing expenses increased by $42.5 million, or 6%, for the year ended December 31, 2023 compared to 2022.`
`What method is used to provide information about legal proceedings in the Annual Report on Form 10-K?`	`Information about legal proceedings in the Annual Report on Form 10-K is incorporated by reference under several notes and sections.`
`How did selling, distribution, and administration expenses change in 2023 compared to previous years?`	`In 2023, the decline in Selling, distribution and administration expense was driven by lower compensation expense associated with workforce reductions, lower costs for professional services and lower freight and warehousing expenses as a result of lower shipments during 2023. Additionally, Selling, distribution and administration expense in 2023 included $116.0 million of intangible asset impairment charges as compared to $281.0 million of intangible asset impairment charges in 2022.`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 1024 5 ], 6 "matryoshka_weights": [ 7 1 8 ], 9 "n_dims_per_step": -1 10}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
gradient_accumulation_steps: 8
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: True
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 8
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 4
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: True
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: True
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
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
hub_revision: None
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	dim_1024_cosine_ndcg@10
-1	-1	-	0.7762
0.0571	10	0.0102	-
0.1143	20	0.01	-
0.1714	30	0.002	-
0.2286	40	0.0162	-
0.2857	50	0.0015	-
0.3429	60	0.0064	-
0.4	70	0.0052	-
0.4571	80	0.0026	-
0.5143	90	0.0098	-
0.5714	100	0.0137	-
0.6286	110	0.0159	-
0.6857	120	0.0062	-
0.7429	130	0.0076	-
0.8	140	0.0046	-
0.8571	150	0.0341	-
0.9143	160	0.0032	-
0.9714	170	0.0051	-
1.0	175	-	0.7873
1.0286	180	0.0106	-
1.0857	190	0.0003	-
1.1429	200	0.0011	-
1.2	210	0.0017	-
1.2571	220	0.0081	-
1.3143	230	0.0005	-
1.3714	240	0.0185	-
1.4286	250	0.0008	-
1.4857	260	0.0034	-
1.5429	270	0.0042	-
1.6	280	0.0088	-
1.6571	290	0.0026	-
1.7143	300	0.0038	-
1.7714	310	0.0032	-
1.8286	320	0.0012	-
1.8857	330	0.0027	-
1.9429	340	0.0073	-
2.0	350	0.0033	0.8056
2.0571	360	0.0013	-
2.1143	370	0.0023	-
2.1714	380	0.0094	-
2.2286	390	0.0132	-
2.2857	400	0.0026	-
2.3429	410	0.0054	-
2.4	420	0.0035	-
2.4571	430	0.0019	-
2.5143	440	0.0003	-
2.5714	450	0.0059	-
2.6286	460	0.0006	-
2.6857	470	0.0004	-
2.7429	480	0.0102	-
2.8	490	0.0011	-
2.8571	500	0.0075	-
2.9143	510	0.013	-
2.9714	520	0.0022	-
3.0	525	-	0.8238
3.0286	530	0.0019	-
3.0857	540	0.0057	-
3.1429	550	0.0042	-
3.2	560	0.0008	-
3.2571	570	0.0001	-
3.3143	580	0.0015	-
3.3714	590	0.0175	-
3.4286	600	0.0006	-
3.4857	610	0.0003	-
3.5429	620	0.0056	-
3.6	630	0.002	-
3.6571	640	0.0091	-
3.7143	650	0.0009	-
3.7714	660	0.0011	-
3.8286	670	0.0001	-
3.8857	680	0.0014	-
3.9429	690	0.0019	-
4.0	700	0.0001	0.8231

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.14
Sentence Transformers: 5.1.0
Transformers: 4.56.1
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 2.19.1
Tokenizers: 0.22.0

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",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	anchor	positive
type	string	string
details	min: 7 tokens mean: 20.73 tokens max: 50 tokens	min: 10 tokens mean: 47.95 tokens max: 431 tokens