--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:10000 - loss:MultipleNegativesRankingLoss base_model: openai/clip-vit-large-patch14 widget: - source_sentence: A man standing next to a little girl riding a horse. sentences: - The woman is working on her computer at the desk. - A young man holding an umbrella next to a herd of cattle. - 'a person sitting at a desk with a keyboard and monitor ' - source_sentence: 'A car at an intersection while a man is crossing the street. ' sentences: - A plane that is flying in the air. - a small girl sitting on a chair holding a white bear - A young toddler walks across the grass in a park. - source_sentence: A lady riding her bicycle on the side of a street. sentences: - Flowers hang from a small decorative post in a yard. - Flowers in a clear vase sitting on a table. - The toilet is near the door in the bathroom. - source_sentence: 'A group of zebras standing beside each other in the desert. ' sentences: - The bathroom is clean and ready for us to use. - A woman throwing a frisbee as a child looks on. - a bird with a pink eye is sitting on a branch in the woods. - source_sentence: A large desk by a window is neatly arranged. sentences: - An old toilet sits in dirt with a helmet on top. - A lady sitting at an enormous dining table with lots of food. - A long hot dog on a plate on a table. datasets: - jxie/coco_captions pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 co2_eq_emissions: emissions: 11.59757010411656 energy_consumed: 0.04333563796741882 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.137 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: CLIP ViT-L/14 model trained on COCO Captions results: - task: type: information-retrieval name: Information Retrieval dataset: name: coco eval type: coco-eval metrics: - type: cosine_accuracy@1 value: 0.799 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.968 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.991 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.995 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.799 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3226666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19820000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09950000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.799 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.968 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.991 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.995 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9112246370033859 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8827011904761911 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8828050771692076 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: coco test type: coco-test metrics: - type: cosine_accuracy@1 value: 0.776 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.959 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.986 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.995 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.776 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.31966666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19720000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09950000000000003 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.776 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.959 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.986 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.995 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8996790966052481 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8674440476190487 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8678233839689722 name: Cosine Map@100 --- # CLIP ViT-L/14 model trained on COCO Captions This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) on the [coco_captions](https://huggingface.co/datasets/jxie/coco_captions) dataset. It maps sentences & paragraphs to a None-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:** [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) - **Maximum Sequence Length:** None tokens - **Output Dimensionality:** None dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [coco_captions](https://huggingface.co/datasets/jxie/coco_captions) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'get_text_features', 'method_output_name': None}, 'image': {'method': 'get_image_features', 'method_output_name': None}}, 'module_output_name': 'sentence_embedding', 'architecture': 'CLIPModel'}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash 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("tomaarsen/clip-vit-L14-coco") # Run inference sentences = [ 'A large desk by a window is neatly arranged.', 'A long hot dog on a plate on a table.', 'A lady sitting at an enormous dining table with lots of food.', ] 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, -0.0302, 0.1619], # [-0.0302, 1.0000, 0.1578], # [ 0.1619, 0.1578, 1.0000]]) ``` ## Evaluation ### Metrics #### Information Retrieval * Datasets: `coco-eval` and `coco-test` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | coco-eval | coco-test | |:--------------------|:-----------|:-----------| | cosine_accuracy@1 | 0.799 | 0.776 | | cosine_accuracy@3 | 0.968 | 0.959 | | cosine_accuracy@5 | 0.991 | 0.986 | | cosine_accuracy@10 | 0.995 | 0.995 | | cosine_precision@1 | 0.799 | 0.776 | | cosine_precision@3 | 0.3227 | 0.3197 | | cosine_precision@5 | 0.1982 | 0.1972 | | cosine_precision@10 | 0.0995 | 0.0995 | | cosine_recall@1 | 0.799 | 0.776 | | cosine_recall@3 | 0.968 | 0.959 | | cosine_recall@5 | 0.991 | 0.986 | | cosine_recall@10 | 0.995 | 0.995 | | **cosine_ndcg@10** | **0.9112** | **0.8997** | | cosine_mrr@10 | 0.8827 | 0.8674 | | cosine_map@100 | 0.8828 | 0.8678 | ## Training Details ### Training Dataset #### coco_captions * Dataset: [coco_captions](https://huggingface.co/datasets/jxie/coco_captions) at [a2ed90d](https://huggingface.co/datasets/jxie/coco_captions/tree/a2ed90d49b61dd13dd71f399c70f5feb897f8bec) * Size: 10,000 training samples * Columns: image and caption * Approximate statistics based on the first 1000 samples: | | image | caption | |:--------|:----------------------------------|:------------------------------------------------------------------------------------------------| | type | PIL.JpegImagePlugin.JpegImageFile | string | | details | | | * Samples: | image | caption | |:----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------| | | A woman wearing a net on her head cutting a cake. | | | A woman cutting a large white sheet cake. | | | A woman wearing a hair net cutting a large sheet cake. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### Evaluation Dataset #### coco_captions * Dataset: [coco_captions](https://huggingface.co/datasets/jxie/coco_captions) at [a2ed90d](https://huggingface.co/datasets/jxie/coco_captions/tree/a2ed90d49b61dd13dd71f399c70f5feb897f8bec) * Size: 1,000 evaluation samples * Columns: image and caption * Approximate statistics based on the first 1000 samples: | | image | caption | |:--------|:----------------------------------|:------------------------------------------------------------------------------------------------| | type | PIL.JpegImagePlugin.JpegImageFile | string | | details | | | * Samples: | image | caption | |:----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | | A child holding a flowered umbrella and petting a yak. | | | A young man holding an umbrella next to a herd of cattle. | | | a young boy barefoot holding an umbrella touching the horn of a cow | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 1 - `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`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `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 - `use_cpu`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `bf16`: True - `fp16`: False - `half_precision_backend`: None - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `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_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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 - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `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_for_metrics`: [] - `eval_do_concat_batches`: True - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `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 | Validation Loss | coco-eval_cosine_ndcg@10 | coco-test_cosine_ndcg@10 | |:------:|:----:|:-------------:|:---------------:|:------------------------:|:------------------------:| | -1 | -1 | - | - | 0.8902 | - | | 0.0112 | 7 | 0.4782 | - | - | - | | 0.0224 | 14 | 0.3108 | - | - | - | | 0.0336 | 21 | 0.2212 | - | - | - | | 0.0448 | 28 | 0.1612 | - | - | - | | 0.056 | 35 | 0.1853 | - | - | - | | 0.0672 | 42 | 0.0811 | - | - | - | | 0.0784 | 49 | 0.0785 | - | - | - | | 0.0896 | 56 | 0.1022 | - | - | - | | 0.1008 | 63 | 0.0927 | 0.1433 | 0.9189 | - | | 0.112 | 70 | 0.112 | - | - | - | | 0.1232 | 77 | 0.1072 | - | - | - | | 0.1344 | 84 | 0.1272 | - | - | - | | 0.1456 | 91 | 0.1176 | - | - | - | | 0.1568 | 98 | 0.1361 | - | - | - | | 0.168 | 105 | 0.1281 | - | - | - | | 0.1792 | 112 | 0.0961 | - | - | - | | 0.1904 | 119 | 0.1038 | - | - | - | | 0.2016 | 126 | 0.1019 | 0.1506 | 0.8929 | - | | 0.2128 | 133 | 0.0657 | - | - | - | | 0.224 | 140 | 0.1187 | - | - | - | | 0.2352 | 147 | 0.0752 | - | - | - | | 0.2464 | 154 | 0.2314 | - | - | - | | 0.2576 | 161 | 0.0806 | - | - | - | | 0.2688 | 168 | 0.1243 | - | - | - | | 0.28 | 175 | 0.1179 | - | - | - | | 0.2912 | 182 | 0.1174 | - | - | - | | 0.3024 | 189 | 0.0926 | 0.1604 | 0.8907 | - | | 0.3136 | 196 | 0.1327 | - | - | - | | 0.3248 | 203 | 0.0861 | - | - | - | | 0.336 | 210 | 0.0677 | - | - | - | | 0.3472 | 217 | 0.1296 | - | - | - | | 0.3584 | 224 | 0.1322 | - | - | - | | 0.3696 | 231 | 0.1555 | - | - | - | | 0.3808 | 238 | 0.0807 | - | - | - | | 0.392 | 245 | 0.1134 | - | - | - | | 0.4032 | 252 | 0.1826 | 0.1712 | 0.8840 | - | | 0.4144 | 259 | 0.1796 | - | - | - | | 0.4256 | 266 | 0.186 | - | - | - | | 0.4368 | 273 | 0.0971 | - | - | - | | 0.448 | 280 | 0.063 | - | - | - | | 0.4592 | 287 | 0.1344 | - | - | - | | 0.4704 | 294 | 0.072 | - | - | - | | 0.4816 | 301 | 0.1233 | - | - | - | | 0.4928 | 308 | 0.1152 | - | - | - | | 0.504 | 315 | 0.148 | 0.1565 | 0.8960 | - | | 0.5152 | 322 | 0.0836 | - | - | - | | 0.5264 | 329 | 0.1171 | - | - | - | | 0.5376 | 336 | 0.1433 | - | - | - | | 0.5488 | 343 | 0.0494 | - | - | - | | 0.56 | 350 | 0.1533 | - | - | - | | 0.5712 | 357 | 0.0773 | - | - | - | | 0.5824 | 364 | 0.0921 | - | - | - | | 0.5936 | 371 | 0.0546 | - | - | - | | 0.6048 | 378 | 0.1444 | 0.1496 | 0.9001 | - | | 0.616 | 385 | 0.0956 | - | - | - | | 0.6272 | 392 | 0.0445 | - | - | - | | 0.6384 | 399 | 0.0939 | - | - | - | | 0.6496 | 406 | 0.1109 | - | - | - | | 0.6608 | 413 | 0.0466 | - | - | - | | 0.672 | 420 | 0.0627 | - | - | - | | 0.6832 | 427 | 0.0857 | - | - | - | | 0.6944 | 434 | 0.058 | - | - | - | | 0.7056 | 441 | 0.1542 | 0.1443 | 0.9031 | - | | 0.7168 | 448 | 0.0972 | - | - | - | | 0.728 | 455 | 0.0892 | - | - | - | | 0.7392 | 462 | 0.0819 | - | - | - | | 0.7504 | 469 | 0.0838 | - | - | - | | 0.7616 | 476 | 0.0754 | - | - | - | | 0.7728 | 483 | 0.0754 | - | - | - | | 0.784 | 490 | 0.0638 | - | - | - | | 0.7952 | 497 | 0.1006 | - | - | - | | 0.8064 | 504 | 0.0398 | 0.1429 | 0.9122 | - | | 0.8176 | 511 | 0.1562 | - | - | - | | 0.8288 | 518 | 0.1039 | - | - | - | | 0.84 | 525 | 0.0342 | - | - | - | | 0.8512 | 532 | 0.0467 | - | - | - | | 0.8624 | 539 | 0.0703 | - | - | - | | 0.8736 | 546 | 0.0655 | - | - | - | | 0.8848 | 553 | 0.0216 | - | - | - | | 0.896 | 560 | 0.029 | - | - | - | | 0.9072 | 567 | 0.0588 | 0.1530 | 0.9112 | - | | 0.9184 | 574 | 0.1145 | - | - | - | | 0.9296 | 581 | 0.0652 | - | - | - | | 0.9408 | 588 | 0.0556 | - | - | - | | 0.952 | 595 | 0.0458 | - | - | - | | 0.9632 | 602 | 0.0085 | - | - | - | | 0.9744 | 609 | 0.0572 | - | - | - | | 0.9856 | 616 | 0.0942 | - | - | - | | 0.9968 | 623 | 0.109 | - | - | - | | -1 | -1 | - | - | - | 0.8997 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.043 kWh - **Carbon Emitted**: 0.012 kg of CO2 - **Hours Used**: 0.137 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 5.2.0.dev0 - Transformers: 4.57.0.dev0 - PyTorch: 2.8.0+cu128 - Accelerate: 1.6.0 - Datasets: 3.6.0 - 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", } ``` #### 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} } ```