--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:53 - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/clip-ViT-L-14 widget: - source_sentence: The Hugging Face Transformers Library | Example Code + Chatbot UI with Gradio sentences: - Shit Happens, Stay Solution Oriented - 3 Ways to Make a Custom AI Assistant | RAG, Tools, & Fine-tuning - How to Manage Data Science Projects - source_sentence: 5 Questions Every Data Scientist Should Hardcode into Their Brain sentences: - 5 AI Projects You Can Build This Weekend (with Python) - An Introduction to Decision Trees | Gini Impurity & Python Code - How to Deploy ML Solutions with FastAPI, Docker, & AWS - source_sentence: My $100,000+ Data Science Resume (what got me hired) sentences: - The Mapper Algorithm | Overview & Python Example Code - How to Build Data Pipelines for ML Projects (w/ Python Code) - How to Make a Data Science Portfolio With GitHub Pages (2024) datasets: - shawhin/yt-title-thumbnail-pairs pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy model-index: - name: SentenceTransformer based on sentence-transformers/clip-ViT-L-14 results: - task: type: triplet name: Triplet dataset: name: yt title thumbnail train type: yt-title-thumbnail-train metrics: - type: cosine_accuracy value: 1.0 name: Cosine Accuracy - task: type: triplet name: Triplet dataset: name: yt title thumbnail valid type: yt-title-thumbnail-valid metrics: - type: cosine_accuracy value: 1.0 name: Cosine Accuracy --- # SentenceTransformer based on sentence-transformers/clip-ViT-L-14 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/clip-ViT-L-14](https://huggingface.co/sentence-transformers/clip-ViT-L-14) on the [yt-title-thumbnail-pairs](https://huggingface.co/datasets/shawhin/yt-title-thumbnail-pairs) 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:** [sentence-transformers/clip-ViT-L-14](https://huggingface.co/sentence-transformers/clip-ViT-L-14) - **Maximum Sequence Length:** None tokens - **Output Dimensionality:** None dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [yt-title-thumbnail-pairs](https://huggingface.co/datasets/shawhin/yt-title-thumbnail-pairs) ### 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): 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("babelmanish/clip-title-thumbnail-embeddings") # Run inference sentences = [ 'My $100,000+ Data Science Resume (what got me hired)', 'The Mapper Algorithm | Overview & Python Example Code', 'How to Build Data Pipelines for ML Projects (w/ Python Code)', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Triplet * Datasets: `yt-title-thumbnail-train` and `yt-title-thumbnail-valid` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | yt-title-thumbnail-train | yt-title-thumbnail-valid | |:--------------------|:-------------------------|:-------------------------| | **cosine_accuracy** | **1.0** | **1.0** | ## Training Details ### Training Dataset #### yt-title-thumbnail-pairs * Dataset: [yt-title-thumbnail-pairs](https://huggingface.co/datasets/shawhin/yt-title-thumbnail-pairs) at [c1b9a13](https://huggingface.co/datasets/shawhin/yt-title-thumbnail-pairs/tree/c1b9a131c52a15636472e440835e2b8634799f0e) * Size: 53 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 53 samples: | | anchor | positive | negative | |:--------|:----------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | PIL.JpegImagePlugin.JpegImageFile | string | string | | details | | | | * Samples: | anchor | positive | negative | |:--------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:----------------------------------------------------------------------| | | Multimodal RAG: A Beginner-friendly Guide (with Python Code) | What Nature Can Teach Us About Business... | | | Detecting Power Laws in Real-world Data | w/ Python Code | I Have 90 Days to Make $10k/mo—Here's my plan | | | I Quit My Job… Here’s How Much I Made 1 Year Later | Persistent Homology | Introduction & Python Example Code | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### yt-title-thumbnail-pairs * Dataset: [yt-title-thumbnail-pairs](https://huggingface.co/datasets/shawhin/yt-title-thumbnail-pairs) at [c1b9a13](https://huggingface.co/datasets/shawhin/yt-title-thumbnail-pairs/tree/c1b9a131c52a15636472e440835e2b8634799f0e) * Size: 11 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 11 samples: | | anchor | positive | negative | |:--------|:----------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | PIL.JpegImagePlugin.JpegImageFile | string | string | | details | | | | * Samples: | anchor | positive | negative | |:--------------------------------------------------------------------------------------------|:------------------------------------------------------------------|:--------------------------------------------------------------------------------| | | I Was Wrong About AI Consulting (what I learned) | How to Make a Data Science Portfolio With GitHub Pages (2024) | | | My $100,000+ Data Science Resume (what got me hired) | The Mapper Algorithm | Overview & Python Example Code | | | 4 Skills You Need to Be a Full-Stack Data Scientist | Fine-Tuning Text Embeddings For Domain-specific Search (w/ Python) | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 0.0001 - `num_train_epochs`: 2 #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `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`: 0.0001 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 2 - `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 - `use_ipex`: False - `bf16`: False - `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`: 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} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `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 - `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 - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | yt-title-thumbnail-train_cosine_accuracy | yt-title-thumbnail-valid_cosine_accuracy | |:-----:|:----:|:-------------:|:---------------:|:----------------------------------------:|:----------------------------------------:| | -1 | -1 | - | - | 0.9623 | 1.0 | | 0.25 | 1 | 2.0056 | - | - | - | | 0.5 | 2 | 1.9543 | - | - | - | | 0.75 | 3 | 1.6954 | - | - | - | | 1.0 | 4 | 0.7505 | 1.4916 | - | - | | 1.25 | 5 | 1.5534 | - | - | - | | 1.5 | 6 | 1.2892 | - | - | - | | 1.75 | 7 | 1.3283 | - | - | - | | 2.0 | 8 | 0.3315 | 1.4990 | - | - | | -1 | -1 | - | - | 1.0 | 1.0 | ### Framework Versions - Python: 3.10.4 - Sentence Transformers: 3.4.1 - Transformers: 4.48.2 - PyTorch: 2.6.0 - Accelerate: 0.26.0 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## 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} } ```