jina2 Base

Name: jina-embed-base-dense-retriever
Availability: InStock

This is a sentence-transformers model finetuned from jinaai/jina-code-embeddings-0.5b. It maps sentences & paragraphs to a 896-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: jinaai/jina-code-embeddings-0.5b
Maximum Sequence Length: 512 tokens
Output Dimensionality: 896 dimensions
Similarity Function: Cosine Similarity

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': 512, 'do_lower_case': False, 'architecture': 'Qwen2Model'})
  (1): Pooling({'word_embedding_dimension': 896, '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("ondayex/jina-embed-base-dense-retriever")
# Run inference
sentences = [
    'Explain the test_hashing_custom_key_encoder logic',
    'def test_hashing_custom_key_encoder() -> None:\n    """Test hashing with a custom key encoder."""\n\n    def custom_key_encoder(doc: Document) -> str:\n        return f"quack-{doc.metadata[\'key\']}"\n\n    document = Document(\n        page_content="Lorem ipsum dolor sit amet", metadata={"key": "like a duck"}\n    )\n    hashed_document = _get_document_with_hash(document, key_encoder=custom_key_encoder)\n    assert hashed_document.id == "quack-like a duck"\n    assert isinstance(hashed_document.id, str)',
    'def __getattr__(name: str) -> Any:\n    """Look up attributes dynamically."""\n    return _import_attribute(name)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 896]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8949, 0.1085],
#         [0.8949, 1.0000, 0.1456],
#         [0.1085, 0.1456, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 900 training samples
Columns: anchor and positive
Approximate statistics based on the first 900 samples:
anchor positive
type string string
details
min: 4 tokens
mean: 8.5 tokens
max: 27 tokens
min: 12 tokens
mean: 153.14 tokens
max: 512 tokens

Samples:

anchor	positive
`How does test_multiple_msg work in Python?`	def test_multiple_msg() -> None:[object Object] human_msg = HumanMessage(content="human", additional_kwargs={"key": "value"})[object Object] ai_msg = AIMessage(content="ai")[object Object] sys_msg = SystemMessage(content="sys")[object Object][object Object] msgs = [[object Object] human_msg,[object Object] ai_msg,[object Object] sys_msg,[object Object] ][object Object] assert messages_from_dict(messages_to_dict(msgs)) == msgs[object Object][object Object] # Test with tool calls[object Object] msgs = [[object Object] AIMessage([object Object] content="",[object Object] tool_calls=[create_tool_call(name="a", args={"b": 1}, id=None)],[object Object] ),[object Object] AIMessage([object Object] content="",[object Object] tool_calls=[create_tool_call(name="c", args={"c": 2}, id=None)],[object Object] ),[object Object] ][object Object] assert messages_from_dict(messages_to_dict(msgs)) == msgs
`How to implement [object Object]?`	`def [object Object](name: str) -> Any:[object Object] """Look up attributes dynamically."""[object Object] return _import_attribute(name)`
`Example usage of test_multiple_msg`	def test_multiple_msg() -> None:[object Object] human_msg = HumanMessage(content="human", additional_kwargs={"key": "value"})[object Object] ai_msg = AIMessage(content="ai")[object Object] sys_msg = SystemMessage(content="sys")[object Object][object Object] msgs = [[object Object] human_msg,[object Object] ai_msg,[object Object] sys_msg,[object Object] ][object Object] assert messages_from_dict(messages_to_dict(msgs)) == msgs[object Object][object Object] # Test with tool calls[object Object] msgs = [[object Object] AIMessage([object Object] content="",[object Object] tool_calls=[create_tool_call(name="a", args={"b": 1}, id=None)],[object Object] ),[object Object] AIMessage([object Object] content="",[object Object] tool_calls=[create_tool_call(name="c", args={"c": 2}, id=None)],[object Object] ),[object Object] ][object Object] assert messages_from_dict(messages_to_dict(msgs)) == msgs

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 4
per_device_eval_batch_size: 4
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
load_best_model_at_end: True
optim: adamw_torch
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
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: 16
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: None
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
bf16: True
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: 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
optim_args: None
adafactor: False
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
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: 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
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.7111	10	0.0808
1.3556	20	0.022
2.0	30	0.0104
2.7111	40	0.0136
3.3556	50	0.0001
4.0	60	0.0442

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.0
Transformers: 4.57.6
PyTorch: 2.9.0+cu126
Accelerate: 1.12.0
Datasets: 4.0.0
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",
}

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: 4 tokens mean: 8.5 tokens max: 27 tokens	min: 12 tokens mean: 153.14 tokens max: 512 tokens