--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1100 - loss:CoSENTLoss base_model: WhereIsAI/UAE-Large-V1 datasets: [] metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max widget: - source_sentence: booking_reference sentences: - Person - Person - Organization - source_sentence: supply sentences: - Time - Quantity - Person - source_sentence: spouse sentences: - ID - Person - Person - source_sentence: blood_type sentences: - Person - Geographical - Organization - source_sentence: account_id sentences: - ID - Organization - Quantity pipeline_tag: sentence-similarity model-index: - name: SentenceTransformer based on WhereIsAI/UAE-Large-V1 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.8924660010011639 name: Pearson Cosine - type: spearman_cosine value: 0.8235197032172585 name: Spearman Cosine - type: pearson_manhattan value: 0.8606201562664572 name: Pearson Manhattan - type: spearman_manhattan value: 0.8165407226815192 name: Spearman Manhattan - type: pearson_euclidean value: 0.8607526008409677 name: Pearson Euclidean - type: spearman_euclidean value: 0.8151449265743713 name: Spearman Euclidean - type: pearson_dot value: 0.8740992356806746 name: Pearson Dot - type: spearman_dot value: 0.8339881740208678 name: Spearman Dot - type: pearson_max value: 0.8924660010011639 name: Pearson Max - type: spearman_max value: 0.8339881740208678 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev test type: sts-dev_test metrics: - type: pearson_cosine value: 0.7742742031598305 name: Pearson Cosine - type: spearman_cosine value: 0.7349811537106432 name: Spearman Cosine - type: pearson_manhattan value: 0.8011822405747617 name: Pearson Manhattan - type: spearman_manhattan value: 0.7482240573811053 name: Spearman Manhattan - type: pearson_euclidean value: 0.7973589089683236 name: Pearson Euclidean - type: spearman_euclidean value: 0.7482240573811053 name: Spearman Euclidean - type: pearson_dot value: 0.7745895614088659 name: Pearson Dot - type: spearman_dot value: 0.7482240573811053 name: Spearman Dot - type: pearson_max value: 0.8011822405747617 name: Pearson Max - type: spearman_max value: 0.7482240573811053 name: Spearman Max --- # SentenceTransformer based on WhereIsAI/UAE-Large-V1 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1). 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:** [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1) - **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': True, 'pooling_mode_mean_tokens': False, '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("Naveen20o1/UAE_Large_V1_nav2") # Run inference sentences = [ 'account_id', 'ID', 'Quantity', ] 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 #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8925 | | **spearman_cosine** | **0.8235** | | pearson_manhattan | 0.8606 | | spearman_manhattan | 0.8165 | | pearson_euclidean | 0.8608 | | spearman_euclidean | 0.8151 | | pearson_dot | 0.8741 | | spearman_dot | 0.834 | | pearson_max | 0.8925 | | spearman_max | 0.834 | #### Semantic Similarity * Dataset: `sts-dev_test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:----------| | pearson_cosine | 0.7743 | | **spearman_cosine** | **0.735** | | pearson_manhattan | 0.8012 | | spearman_manhattan | 0.7482 | | pearson_euclidean | 0.7974 | | spearman_euclidean | 0.7482 | | pearson_dot | 0.7746 | | spearman_dot | 0.7482 | | pearson_max | 0.8012 | | spearman_max | 0.7482 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 1,100 training samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:-------------------------|:--------------------------|:-----------------| | enrollment | Quantity | 1.0 | | instrument | Artifact | 1.0 | | stock_level | Geographical | 0.0 | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 100 evaluation samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:-----------------------|:--------------------------|:-----------------| | review | Quantity | 0.0 | | machinery | Artifact | 1.0 | | locality | Geographical | 1.0 | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### 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`: 11 - `warmup_ratio`: 0.1 - `fp16`: True #### 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 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_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`: 11 - `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 - `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`: 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} - `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`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-dev_test_spearman_cosine | |:-------:|:----:|:-------------:|:------:|:-----------------------:|:----------------------------:| | 0.7246 | 50 | 2.9649 | - | - | - | | 1.4493 | 100 | 1.0967 | 1.4481 | 0.8368 | - | | 2.1739 | 150 | 0.5062 | - | - | - | | 2.8986 | 200 | 0.3909 | 1.3760 | 0.8242 | - | | 3.6232 | 250 | 0.2006 | - | - | - | | 4.3478 | 300 | 0.0324 | 2.3098 | 0.8124 | - | | 5.0725 | 350 | 0.0564 | - | - | - | | 5.7971 | 400 | 0.0729 | 1.5758 | 0.8193 | - | | 6.5217 | 450 | 0.0051 | - | - | - | | 7.2464 | 500 | 0.0091 | 2.2818 | 0.8165 | - | | 7.9710 | 550 | 0.0084 | - | - | - | | 8.6957 | 600 | 0.0319 | 1.9056 | 0.8144 | - | | 9.4203 | 650 | 0.0023 | - | - | - | | 10.1449 | 700 | 0.0136 | 2.1295 | 0.8235 | - | | 10.8696 | 750 | 0.0156 | - | - | - | | 11.0 | 759 | - | - | - | 0.7350 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.0+cu121 - Accelerate: 0.31.0 - 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", } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ```