memory-retrieval-jina-v3-lora

SentenceTransformer based on jinaai/jina-embeddings-v3

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 <!-- at revision f1944de8402dcd5f2b03f822a4bc22a7f2de2eb9 -->
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

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.

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]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 369,891 training samples
• Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
• Approximate statistics based on the first 1000 samples:

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

• Samples:

  sentence_0	sentence_1	sentence_2
  <code>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.</code>	<code>Alex has been working on mastering the art of traditional ink drawing (Sumi-e) as a meditative hobby, which requires minimal light and focus.</code>	<code>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.</code>
  <code>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?</code>	<code>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.</code>	<code>Alex previously boosted his geometry understanding on the SAT by reviewing sample test questions daily during short 30-minute sessions after school.</code>
  <code>Jamal pushes safe bets, yet deadline looms like a storm—verify this claim?</code>	<code>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.</code>	<code>Jamal is currently bogged down trying to reconcile conflicting Q3 sales data from three different regional offices, which he finds deeply frustrating.</code>

• Loss: <code>TripletLoss</code> with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.COSINE",
      "triplet_margin": 0.5
  }
  

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

<details><summary>Click to expand</summary>

• 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: {}

</details>

Training Logs

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

@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

@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}
}

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