This is a sentence-transformers model finetuned from jinaai/jina-embeddings-v3. 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: jinaai/jina-embeddings-v3
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 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(
  (transformer): Transformer(
    (auto_model): PeftModelForFeatureExtraction(
      (base_model): LoraModel(
        (model): XLMRobertaLoRA(
          (roberta): XLMRobertaModel(
            (embeddings): XLMRobertaEmbeddings(
              (word_embeddings): ParametrizedEmbedding(
                250002, 1024, padding_idx=1
                (parametrizations): ModuleDict(
                  (weight): ParametrizationList(
                    (0): LoRAParametrization()
                  )
                )
              )
              (token_type_embeddings): ParametrizedEmbedding(
                1, 1024
                (parametrizations): ModuleDict(
                  (weight): ParametrizationList(
                    (0): LoRAParametrization()
                  )
                )
              )
            )
            (emb_drop): Dropout(p=0.1, inplace=False)
            (emb_ln): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
            (encoder): XLMRobertaEncoder(
              (layers): ModuleList(
                (0-23): 24 x Block(
                  (mixer): MHA(
                    (rotary_emb): RotaryEmbedding()
                    (Wqkv): ParametrizedLinearResidual(
                      in_features=1024, out_features=3072, bias=True
                      (parametrizations): ModuleDict(
                        (weight): ParametrizationList(
                          (0): LoRAParametrization()
                        )
                      )
                    )
                    (inner_attn): SelfAttention(
                      (drop): Dropout(p=0.1, inplace=False)
                    )
                    (inner_cross_attn): CrossAttention(
                      (drop): Dropout(p=0.1, inplace=False)
                    )
                    (out_proj): lora.Linear(
                      (base_layer): ParametrizedLinear(
                        in_features=1024, out_features=1024, bias=True
                        (parametrizations): ModuleDict(
                          (weight): ParametrizationList(
                            (0): LoRAParametrization()
                          )
                        )
                      )
                      (lora_dropout): ModuleDict(
                        (default): Dropout(p=0.1, inplace=False)
                      )
                      (lora_A): ModuleDict(
                        (default): Linear(in_features=1024, out_features=32, bias=False)
                      )
                      (lora_B): ModuleDict(
                        (default): Linear(in_features=32, out_features=1024, bias=False)
                      )
                      (lora_embedding_A): ParameterDict()
                      (lora_embedding_B): ParameterDict()
                      (lora_magnitude_vector): ModuleDict()
                    )
                  )
                  (dropout1): Dropout(p=0.1, inplace=False)
                  (drop_path1): StochasticDepth(p=0.0, mode=row)
                  (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                  (mlp): Mlp(
                    (fc1): lora.Linear(
                      (base_layer): ParametrizedLinear(
                        in_features=1024, out_features=4096, bias=True
                        (parametrizations): ModuleDict(
                          (weight): ParametrizationList(
                            (0): LoRAParametrization()
                          )
                        )
                      )
                      (lora_dropout): ModuleDict(
                        (default): Dropout(p=0.1, inplace=False)
                      )
                      (lora_A): ModuleDict(
                        (default): Linear(in_features=1024, out_features=32, bias=False)
                      )
                      (lora_B): ModuleDict(
                        (default): Linear(in_features=32, out_features=4096, bias=False)
                      )
                      (lora_embedding_A): ParameterDict()
                      (lora_embedding_B): ParameterDict()
                      (lora_magnitude_vector): ModuleDict()
                    )
                    (fc2): lora.Linear(
                      (base_layer): ParametrizedLinear(
                        in_features=4096, out_features=1024, bias=True
                        (parametrizations): ModuleDict(
                          (weight): ParametrizationList(
                            (0): LoRAParametrization()
                          )
                        )
                      )
                      (lora_dropout): ModuleDict(
                        (default): Dropout(p=0.1, inplace=False)
                      )
                      (lora_A): ModuleDict(
                        (default): Linear(in_features=4096, out_features=32, bias=False)
                      )
                      (lora_B): ModuleDict(
                        (default): Linear(in_features=32, out_features=1024, bias=False)
                      )
                      (lora_embedding_A): ParameterDict()
                      (lora_embedding_B): ParameterDict()
                      (lora_magnitude_vector): ModuleDict()
                    )
                  )
                  (dropout2): Dropout(p=0.1, inplace=False)
                  (drop_path2): StochasticDepth(p=0.0, mode=row)
                  (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                )
              )
            )
            (pooler): XLMRobertaPooler(
              (dense): ParametrizedLinear(
                in_features=1024, out_features=1024, bias=True
                (parametrizations): ModuleDict(
                  (weight): ParametrizationList(
                    (0): LoRAParametrization()
                  )
                )
              )
              (activation): Tanh()
            )
          )
        )
      )
    )
  )
  (pooler): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (normalizer): 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("Mercity/memory-retrieval-jina-v3-lora")
# Run inference
sentences = [
    'Preparing instructions for potential Brazilian yoga classes excites me—could you curate a professional list of Portuguese phrases for guiding poses and breathing exercises?',
    'The previous attempt at self-study failed because Liam found the standard textbook pronunciation guide recordings to be grating and overly formal, leading him to stop practicing after two weeks.',
    'Chloe has a documented, severe anxiety disorder requiring her to maintain a structured, predictable routine; sudden, high-stress financial calculations or immediate high-stakes decisions trigger significant health setbacks.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.8699, -0.1061],
#         [ 0.8699,  1.0000, -0.1572],
#         [-0.1061, -0.1572,  1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 369,891 training samples
Columns: sentence_0, sentence_1, and sentence_2
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 sentence_2
type string string string
details
min: 12 tokens
mean: 36.09 tokens
max: 81 tokens
min: 20 tokens
mean: 40.88 tokens
max: 78 tokens
min: 15 tokens
mean: 36.83 tokens
max: 68 tokens

Samples:

sentence_0	sentence_1	sentence_2
`To achieve sufficient relaxation by 11 PM after a demanding shift, suggest budget-conscious, non-stimulating pursuits that differ from audiobooks and suit my solo living situation.`	`Alex has been working on mastering the art of traditional ink drawing (Sumi-e) as a meditative hobby, which requires minimal light and focus.`	`Maria has set a personal milestone to donate 10% of her memoir's first-year royalties to a burnout recovery nonprofit, tying her publication success directly to the book's perceived authenticity and impact.`
`I'm so pumped about this new grammar series—it's going to make such a difference for my subscribers who keep mixing up noun genders! Can you brainstorm ways to animate those common pitfalls like the -o ending myth?`	`The beta group overwhelmingly preferred short, character-driven skits over abstract quizzes, specifically mentioning that the last tutorial that relied heavily on on-screen text overlays resulted in lower engagement.`	`Alex previously boosted his geometry understanding on the SAT by reviewing sample test questions daily during short 30-minute sessions after school.`
`Jamal pushes safe bets, yet deadline looms like a storm—verify this claim?`	`Maria received an internal promotion review last week, and exceeding expectations on this presentation is the single biggest factor determining her eligibility for the Senior Manager role opening in January.`	`Jamal is currently bogged down trying to reconcile conflicting Q3 sales data from three different regional offices, which he finds deeply frustrating.`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.COSINE", 3 "triplet_margin": 0.5 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 1
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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: 1
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
bf16: False
fp16: True
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}
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
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: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.0433	500	0.2143
0.0865	1000	0.1182

Framework Versions

Python: 3.12.3
Sentence Transformers: 5.1.2
Transformers: 4.57.1
PyTorch: 2.8.0+cu128
Accelerate: 1.11.0
Datasets: 4.4.1
Tokenizers: 0.22.1

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",
}

TripletLoss

bibtex

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 12 tokens mean: 36.09 tokens max: 81 tokens	min: 20 tokens mean: 40.88 tokens max: 78 tokens	min: 15 tokens mean: 36.83 tokens max: 68 tokens