SentenceTransformer based on sentence-transformers/clip-ViT-B-32

This is a sentence-transformers model finetuned from sentence-transformers/clip-ViT-B-32 on the unsplash-lite dataset. It maps sentences & paragraphs to a 512-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer

• Base model: sentence-transformers/clip-ViT-B-32

• Maximum Sequence Length: 77 tokens

• Output Dimensionality: 512 dimensions

• Similarity Function: Cosine Similarity

• Supported Modalities: Text, Image

• Training Dataset:

  • unsplash-lite

Model Sources

• Documentation: Sentence Transformers Documentation

• Repository: Sentence Transformers on GitHub

• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'get_text_features', 'method_output_name': 'pooler_output'}, 'image': {'method': 'get_image_features', 'method_output_name': 'pooler_output'}}, 'module_output_name': 'sentence_embedding', 'architecture': 'CLIPModel'})
)

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("sentence_transformers_model_id")
# Run inference
queries = [
    'assets/image_0.jpg',
]
documents = [
    'assets/image_1.jpg',
    'assets/image_2.jpg',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 512] [2, 512]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.0571, -0.0265]])

Evaluation

Metrics

Information Retrieval

• Dataset: unsplash-dev

• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.63
cosine_accuracy@3	0.83
cosine_accuracy@5	0.89
cosine_accuracy@10	0.91
cosine_precision@1	0.63
cosine_precision@3	0.2767
cosine_precision@5	0.178
cosine_precision@10	0.091
cosine_recall@1	0.63
cosine_recall@3	0.83
cosine_recall@5	0.89
cosine_recall@10	0.91
cosine_ndcg@10	0.7791
cosine_mrr@10	0.7358
cosine_map@100	0.7392

Training Details

Training Dataset

unsplash-lite

• Dataset: unsplash-lite at 3afcfc7

• Size: 24,896 training samples

• Columns: image and caption

• Approximate statistics based on the first 100 samples:

  	image	caption
  type	image	string
  modality	image	text
  details	min: 640x340 px mean: 640x679 px max: 640x1138 px	min: 2 tokens mean: 25.48 tokens max: 73 tokens

• Samples:

  image	caption
  	lighthouse, beach, moody, outdoors, water, wave, beacon, ocean, sea, stormy, crashing, porthcawl, sea waves, tower, nature, swell, building, architecture, surf
  	experimental, person, human, bubble
  	nature, outdoors, night, aurora

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Evaluation Dataset

unsplash-lite

• Dataset: unsplash-lite at 3afcfc7

• Size: 100 evaluation samples

• Columns: image and caption

• Approximate statistics based on the first 100 samples:

  	image	caption
  type	image	string
  modality	image	text
  details	min: 640x301 px mean: 640x687 px max: 640x1138 px	min: 2 tokens mean: 28.81 tokens max: 70 tokens

• Samples:

  image	caption
  	windbreaker, style, person, female, clothing, fur coat, apparel, raincoat, human
  	nature, backgrounds, outdoors, wallpapers, fog
  	waves, texture, wind wave, cloudy, water housing, blue, barrel, experimental, california, swimming, southern california, bubble, canon, wave, surf, sky, reflection, surfing

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 32

• num_train_epochs: 1

• learning_rate: 2e-05

• warmup_steps: 0.1

• bf16: True

• per_device_eval_batch_size: 32

• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• per_device_train_batch_size: 32

• num_train_epochs: 1

• max_steps: -1

• learning_rate: 2e-05

• lr_scheduler_type: linear

• lr_scheduler_kwargs: None

• warmup_steps: 0.1

• optim: adamw_torch_fused

• optim_args: None

• weight_decay: 0.0

• adam_beta1: 0.9

• adam_beta2: 0.999

• adam_epsilon: 1e-08

• optim_target_modules: None

• gradient_accumulation_steps: 1

• average_tokens_across_devices: True

• max_grad_norm: 1.0

• label_smoothing_factor: 0.0

• bf16: True

• fp16: False

• bf16_full_eval: False

• fp16_full_eval: False

• tf32: None

• gradient_checkpointing: False

• gradient_checkpointing_kwargs: None

• torch_compile: False

• torch_compile_backend: None

• torch_compile_mode: None

• use_liger_kernel: False

• liger_kernel_config: None

• use_cache: False

• neftune_noise_alpha: None

• torch_empty_cache_steps: None

• auto_find_batch_size: False

• log_on_each_node: True

• logging_nan_inf_filter: True

• include_num_input_tokens_seen: no

• log_level: passive

• log_level_replica: warning

• disable_tqdm: False

• project: huggingface

• trackio_space_id: None

• trackio_bucket_id: None

• trackio_static_space_id: None

• per_device_eval_batch_size: 32

• prediction_loss_only: True

• eval_on_start: False

• eval_do_concat_batches: True

• eval_use_gather_object: False

• eval_accumulation_steps: None

• include_for_metrics: []

• batch_eval_metrics: False

• save_only_model: False

• save_on_each_node: False

• enable_jit_checkpoint: False

• push_to_hub: False

• hub_private_repo: None

• hub_model_id: None

• hub_strategy: every_save

• hub_always_push: False

• hub_revision: None

• load_best_model_at_end: False

• ignore_data_skip: False

• restore_callback_states_from_checkpoint: False

• full_determinism: False

• seed: 42

• data_seed: None

• use_cpu: 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

• dataloader_drop_last: False

• dataloader_num_workers: 0

• dataloader_pin_memory: True

• dataloader_persistent_workers: False

• dataloader_prefetch_factor: None

• remove_unused_columns: True

• label_names: None

• train_sampling_strategy: random

• length_column_name: length

• ddp_find_unused_parameters: None

• ddp_bucket_cap_mb: None

• ddp_broadcast_buffers: False

• ddp_static_graph: None

• ddp_backend: None

• ddp_timeout: 1800

• fsdp: []

• fsdp_config: {‘min_num_params’: 0, ‘xla’: False, ‘xla_fsdp_v2’: False, ‘xla_fsdp_grad_ckpt’: False}

• deepspeed: None

• debug: []

• skip_memory_metrics: True

• do_predict: False

• resume_from_checkpoint: None

• warmup_ratio: None

• local_rank: -1

• prompts: None

• batch_sampler: no_duplicates

• multi_dataset_batch_sampler: proportional

• router_mapping: {}

• learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	unsplash-dev_cosine_ndcg@10
-1	-1	-	-	0.7142
0.0501	39	0.9333	-	-
0.1003	78	0.5194	0.3630	0.7751
0.1504	117	0.6026	-	-
0.2005	156	0.5822	0.3062	0.8042
0.2506	195	0.5841	-	-
0.3008	234	0.5302	0.2984	0.7983
0.3509	273	0.5120	-	-
0.4010	312	0.5243	0.3266	0.7791

Training Time

• Training: 12.0 minutes

Framework Versions

• Python: 3.11.6

• Sentence Transformers: 5.6.0.dev0

• Transformers: 5.8.0.dev0

• PyTorch: 2.10.0+cu128

• Accelerate: 1.13.0.dev0

• Datasets: 4.8.4

• Tokenizers: 0.22.2

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",
}

MultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}