--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:80 - loss:CoSENTLoss base_model: abdeljalilELmajjodi/model widget: - source_sentence: The school is having a special event in order to show the american culture on how other cultures are dealt with in parties. sentences: - A school is hosting an event. - A child with mom and dad, on summer vacation at the beach. - There are people just getting on a train - source_sentence: Two women who just had lunch hugging and saying goodbye. sentences: - Two adults walk across a street. - A team is trying to tag a runner out. - There are two woman in this picture. - source_sentence: A boy is jumping on skateboard in the middle of a red bridge. sentences: - The boy does a skateboarding trick. - The two sisters saw each other across the crowded diner and shared a hug, both clutching their doggie bags. - The women do not care what clothes they wear. - source_sentence: Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background. sentences: - A woman in white. - The woman is wearing white. - Some people board a train. - source_sentence: A woman is walking across the street eating a banana, while a man is following with his briefcase. sentences: - A man in a restaurant is waiting for his meal to arrive. - A married couple is sleeping. - An actress and her favorite assistant talk a walk in the city. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on abdeljalilELmajjodi/model results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: pair score evaluator dev type: pair-score-evaluator-dev metrics: - type: pearson_cosine value: 0.3132380731765732 name: Pearson Cosine - type: spearman_cosine value: 0.21958363099039482 name: Spearman Cosine --- # SentenceTransformer based on abdeljalilELmajjodi/model This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [abdeljalilELmajjodi/model](https://huggingface.co/abdeljalilELmajjodi/model) on the all-nli dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [abdeljalilELmajjodi/model](https://huggingface.co/abdeljalilELmajjodi/model) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Supported Modality:** Text - **Training Dataset:** - all-nli ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'XLMRobertaModel'}) (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'mean', '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("sentence_transformers_model_id") # Run inference sentences = [ 'A woman is walking across the street eating a banana, while a man is following with his briefcase.', 'An actress and her favorite assistant talk a walk in the city.', 'A man in a restaurant is waiting for his meal to arrive.', ] 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, 0.9891, 0.9925], # [0.9891, 1.0000, 0.9870], # [0.9925, 0.9870, 1.0000]]) ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `pair-score-evaluator-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.3132 | | **spearman_cosine** | **0.2196** | ## Training Details ### Training Dataset #### all-nli * Dataset: all-nli * Size: 80 training samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 80 samples: | | sentence1 | sentence2 | score | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:---------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:-----------------| | Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background. | The woman and man are outdoors. | 1.0 | | An older man sits with his orange juice at a small table in a coffee shop while employees in bright colored shirts smile in the background. | An older man drinks his juice as he waits for his daughter to get off work. | 0.5 | | Children smiling and waving at camera | There are children present | 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" } ``` ### Evaluation Dataset #### all-nli * Dataset: all-nli * Size: 20 evaluation samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 20 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------|:-----------------| | High fashion ladies wait outside a tram beside a crowd of people in the city. | The women do not care what clothes they wear. | 0.0 | | The school is having a special event in order to show the american culture on how other cultures are dealt with in parties. | A school is hosting an event. | 1.0 | | An older man is drinking orange juice at a restaurant. | A man is drinking juice. | 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 - `num_train_epochs`: 1 - `warmup_steps`: 0.05 - `bf16`: True - `fp16_full_eval`: True - `load_best_model_at_end`: True - `push_to_hub`: True - `gradient_checkpointing`: True #### All Hyperparameters
Click to expand - `do_predict`: False - `prediction_loss_only`: True - `per_device_train_batch_size`: 8 - `per_device_eval_batch_size`: 8 - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_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`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: None - `warmup_ratio`: None - `warmup_steps`: 0.05 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `enable_jit_checkpoint`: False - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `use_cpu`: False - `seed`: 42 - `data_seed`: None - `bf16`: True - `fp16`: False - `bf16_full_eval`: False - `fp16_full_eval`: True - `tf32`: None - `local_rank`: -1 - `ddp_backend`: None - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `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 - `project`: huggingface - `trackio_space_id`: trackio - `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 - `push_to_hub`: True - `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`: True - `gradient_checkpointing_kwargs`: None - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `auto_find_batch_size`: False - `full_determinism`: False - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `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 - `use_cache`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | Validation Loss | pair-score-evaluator-dev_spearman_cosine | |:-------:|:------:|:-------------:|:---------------:|:----------------------------------------:| | 0.1 | 1 | 3.0315 | - | - | | 0.5 | 5 | 3.0018 | - | - | | **1.0** | **10** | **3.7124** | **2.6509** | **0.2737** | | 0.1 | 1 | 2.8043 | - | - | | 0.5 | 5 | 2.9111 | - | - | | **1.0** | **10** | **3.1121** | **2.6923** | **0.2196** | * The bold row denotes the saved checkpoint. ### Training Time - **Training**: 5.6 minutes ### Framework Versions - Python: 3.12.13 - Sentence Transformers: 5.4.1 - Transformers: 5.0.0 - PyTorch: 2.10.0+cu128 - Accelerate: 1.13.0 - Datasets: 4.8.5 - Tokenizers: 0.22.2 ## 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 @article{10531646, author={Huang, Xiang and Peng, Hao and Zou, Dongcheng and Liu, Zhiwei and Li, Jianxin and Liu, Kay and Wu, Jia and Su, Jianlin and Yu, Philip S.}, journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, title={CoSENT: Consistent Sentence Embedding via Similarity Ranking}, year={2024}, doi={10.1109/TASLP.2024.3402087} } ```