--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:899 - 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: hr sentences: - Geographical - Quantity - Person - source_sentence: product sentences: - Organization - Time - Artifact - source_sentence: council sentences: - Person - Person - Quantity - source_sentence: salesman sentences: - Person - Time - Person - source_sentence: joint_venture_name sentences: - Person - Organization - Person 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: 0.8883347646952768 name: Pearson Cosine - type: spearman_cosine value: 0.8463283813349622 name: Spearman Cosine - type: pearson_manhattan value: 0.8611263810572393 name: Pearson Manhattan - type: spearman_manhattan value: 0.838590521848471 name: Spearman Manhattan - type: pearson_euclidean value: 0.8622761936152195 name: Pearson Euclidean - type: spearman_euclidean value: 0.8405249867200939 name: Spearman Euclidean - type: pearson_dot value: 0.8773449747713008 name: Pearson Dot - type: spearman_dot value: 0.8443939164633394 name: Spearman Dot - type: pearson_max value: 0.8883347646952768 name: Pearson Max - type: spearman_max value: 0.8463283813349622 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev test type: sts-dev_test metrics: - type: pearson_cosine value: 0.9278166656810813 name: Pearson Cosine - type: spearman_cosine value: 0.8783100656536799 name: Spearman Cosine - type: pearson_manhattan value: 0.954242190347034 name: Pearson Manhattan - type: spearman_manhattan value: 0.8783100656536799 name: Spearman Manhattan - type: pearson_euclidean value: 0.9519570678729806 name: Pearson Euclidean - type: spearman_euclidean value: 0.8783100656536799 name: Spearman Euclidean - type: pearson_dot value: 0.9258180799496141 name: Pearson Dot - type: spearman_dot value: 0.8783100656536799 name: Spearman Dot - type: pearson_max value: 0.954242190347034 name: Pearson Max - type: spearman_max value: 0.8783100656536799 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_nav1") # Run inference sentences = [ 'joint_venture_name', 'Organization', 'Person', ] 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 | 0.8883 | | **spearman_cosine** | **0.8463** | | pearson_manhattan | 0.8611 | | spearman_manhattan | 0.8386 | | pearson_euclidean | 0.8623 | | spearman_euclidean | 0.8405 | | pearson_dot | 0.8773 | | spearman_dot | 0.8444 | | pearson_max | 0.8883 | | spearman_max | 0.8463 | #### 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 | 0.9278 | | **spearman_cosine** | **0.8783** | | pearson_manhattan | 0.9542 | | spearman_manhattan | 0.8783 | | pearson_euclidean | 0.952 | | spearman_euclidean | 0.8783 | | pearson_dot | 0.9258 | | spearman_dot | 0.8783 | | pearson_max | 0.9542 | | spearman_max | 0.8783 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 899 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 | |:------------------------------|:---------------------------|:-----------------| | postcode | Communication | 0.0 | | telephone_number | Communication | 1.0 | | vehicle_type | Person | 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: 60 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 | |:------------------------------|:----------------------|:-----------------| | surgical_history | Person | 0.0 | | count | Quantity | 1.0 | | board | Person | 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" } ``` ### 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 | |:-------:|:----:|:-------------:|:------:|:-----------------------:|:----------------------------:| | 0.8772 | 50 | 2.6697 | - | - | - | | 1.7544 | 100 | 0.5212 | 2.4196 | 0.8057 | - | | 2.6316 | 150 | 0.3741 | - | - | - | | 3.5088 | 200 | 0.0033 | 1.7749 | 0.8115 | - | | 4.3860 | 250 | 0.0257 | - | - | - | | 5.2632 | 300 | 0.0159 | 2.2808 | 0.8154 | - | | 6.1404 | 350 | 0.0057 | - | - | - | | 7.0175 | 400 | 0.0044 | 1.5027 | 0.8444 | - | | 7.8947 | 450 | 0.0004 | - | - | - | | 8.7719 | 500 | 0.0008 | 0.9416 | 0.8483 | - | | 9.6491 | 550 | 0.0001 | - | - | - | | 10.5263 | 600 | 0.0002 | 1.1264 | 0.8463 | - | | 11.0 | 627 | - | - | - | 0.8783 | ### 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}, } ```