--- base_model: UKPLab/triple-encoders-dailydialog datasets: [] language: [] library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:985575 - loss:CosineSimilarityTripleEncoderLoss - loss:ContrastiveLoss widget: - source_sentence: A small white and tan dog licking up peanut butter. sentences: - Someone is making dinner in the kitchen. - Someone put peanut butter on the dog's nose because that's always good for a laugh. - Two dogs are eating food from a bowl in a kitchen - source_sentence: A person in a heavy coat shoveling snow. sentences: - Someone is holding a rocket launcher. - An old person is shoveling snow. - The private bar's pro bono work was supported by the judges. - source_sentence: '[B1] [O] [BEFORE] ' sentences: - '[B2] [E] [BEFORE] ' - '[B2] [O] [BEFORE] e' - '[AFTER] u' - source_sentence: '[B1] [E] [BEFORE] e' sentences: - '[B2] [O] [BEFORE] :' - '[B2] [O] [BEFORE] t' - '[AFTER] C' - source_sentence: '[B1] [O] [BEFORE] s' sentences: - '[B2] [O] [BEFORE] o' - '[B2] [E] [BEFORE] ' - '[AFTER] u' --- # SentenceTransformer based on UKPLab/triple-encoders-dailydialog This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [UKPLab/triple-encoders-dailydialog](https://huggingface.co/UKPLab/triple-encoders-dailydialog). 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:** [UKPLab/triple-encoders-dailydialog](https://huggingface.co/UKPLab/triple-encoders-dailydialog) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity ### 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({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): 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}) ) ``` ## 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("abhiraj1/eval_triple_encoder") # Run inference sentences = [ '[B1] [O] [BEFORE] s', '[B2] [E] [BEFORE] ', '[AFTER] u', ] 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] ``` ## Training Details ### Training Datasets #### Unnamed Dataset * Size: 43,506 training samples * Columns: sentence_0, sentence_1, sentence_2, and label * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | sentence_2 | label | |:--------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | string | float | | details | | | | | * Samples: | sentence_0 | sentence_1 | sentence_2 | label | |:---------------------------------|:---------------------------------|:-----------------------|:--------------------------------| | [B1] [O] [BEFORE] | [B2] [E] [BEFORE] | [AFTER] u | 0.0 | | [B1] [E] [BEFORE] e | [B2] [O] [BEFORE] : | [AFTER] C | 0.0 | | [B1] [O] [BEFORE] s | [B2] [E] [BEFORE] | [AFTER] u | 0.6000000000000001 | * Loss: triple_encoders.losses.CosineSimilarityTripleEncoderLoss.CosineSimilarityTripleEncoderLoss #### Unnamed Dataset * Size: 942,069 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | label | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:---------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------| | And the reason Lincoln and his goons had shown up? Well, not everybody was full of respect. | Lincoln didn't show up. | 0 | | a rally car driving down a roadway with people on the side taking pictures | People on the side of road taking picture of a rally car driving down | 1 | | The dog is wearing a purple cape. | THE ANIMAL IS IN A PAGEANT | 2 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 1 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `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 - `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 - `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`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `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 - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.0919 | 500 | 0.0838 | | 0.1838 | 1000 | 0.0474 | | 0.2757 | 1500 | 0.0414 | | 0.3676 | 2000 | 0.0417 | | 0.4596 | 2500 | 0.042 | | 0.5515 | 3000 | 0.0423 | | 0.6434 | 3500 | 0.0408 | | 0.7353 | 4000 | 0.0427 | | 0.8272 | 4500 | 0.0414 | | 0.9191 | 5000 | 0.0415 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.0+cu121 - Accelerate: 0.32.1 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## 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", } ``` #### ContrastiveLoss ```bibtex @inproceedings{hadsell2006dimensionality, author={Hadsell, R. and Chopra, S. and LeCun, Y.}, booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, title={Dimensionality Reduction by Learning an Invariant Mapping}, year={2006}, volume={2}, number={}, pages={1735-1742}, doi={10.1109/CVPR.2006.100} } ```