--- base_model: BAAI/bge-base-en-v1.5 datasets: [] language: [] library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:48 - loss:MultipleNegativesRankingLoss widget: - source_sentence: Users are entitled to a refund for excess payments after necessary deductions, provided that payments were not processed to a wrong account due to user error. sentences: - What is the timeline for the delivery of the documentary film as outlined in this contract? - Under what circumstances can a user receive a refund for multiple payments made for a single order? - What are the Payment Terms for the Batteries? - source_sentence: Users can contact Customer Care before confirmation to request a refund for offline services or reschedule for online services, subject to the platform's discretion. sentences: - How does Paratalks handle refund requests made before a service professional confirms a booking? - How should proprietary and confidential information disclosed under the Agreement be treated by the Parties? - When does this Agreement terminate? - source_sentence: If there is any unreasonable delay in the refund process, the User can report it to Customer Care at contact@paratalks.in or +91-9116768791. sentences: - What should a User do if there is an unreasonable delay in the refund process? - What are the confidentiality provisions in this contract? - What are the specified payment terms for the photography services under this contract? - source_sentence: The refund (if permitted by the Platform) shall be processed after deductions, which may include transaction charges levied by the bank and/or the payment gateway, as well as any other charges incurred by the Platform for facilitating the payment or refund. sentences: - What are the conditions under which a user is not entitled to a refund according to Paratalks' refund policy? - What is the jurisdiction and governing law applicable to this contract? - How are refunds processed if permitted by the Platform? - source_sentence: This Agreement shall be governed by and construed in accordance with the laws of Indiana. Any dispute arising out of or in connection with this Agreement shall be resolved through good faith negotiations between the Parties and will be subject to the jurisdiction of the courts of Dania. sentences: - Under what condition will the User not be entitled to a refund if the payment is processed to a wrong Account? - What events constitute Force Majeure under this Agreement? - Under which laws is the Battery Supply Agreement governed and how are disputes resolved? model-index: - name: SentenceTransformer based on BAAI/bge-base-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.8333333333333334 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8333333333333334 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8333333333333334 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8333333333333334 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27777777777777773 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16666666666666666 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09999999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8333333333333334 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8333333333333334 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8333333333333334 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.892701197851337 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8611111111111112 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8611111111111112 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.8333333333333334 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8333333333333334 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8333333333333334 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8333333333333334 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27777777777777773 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16666666666666666 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09999999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8333333333333334 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8333333333333334 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8333333333333334 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.892701197851337 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8611111111111112 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8611111111111112 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.8333333333333334 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8333333333333334 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8333333333333334 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8333333333333334 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27777777777777773 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16666666666666666 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09999999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8333333333333334 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8333333333333334 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8333333333333334 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.892701197851337 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8611111111111112 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8611111111111112 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.8333333333333334 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8333333333333334 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8333333333333334 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8333333333333334 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27777777777777773 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16666666666666666 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09999999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8333333333333334 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8333333333333334 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8333333333333334 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8859108127976215 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8541666666666666 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8541666666666666 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.8333333333333334 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8333333333333334 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8333333333333334 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8333333333333334 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27777777777777773 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16666666666666666 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09999999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8333333333333334 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8333333333333334 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8333333333333334 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8835049992773302 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8518518518518517 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8518518518518517 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 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': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, '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}) (2): Normalize() ) ``` ## 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("vineet10/fm1") # Run inference sentences = [ 'This Agreement shall be governed by and construed in accordance with the laws of Indiana. Any dispute arising out of or in connection with this Agreement shall be resolved through good faith negotiations between the Parties and will be subject to the jurisdiction of the courts of Dania.', 'Under which laws is the Battery Supply Agreement governed and how are disputes resolved?', 'What events constitute Force Majeure under this Agreement?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.8333 | | cosine_accuracy@3 | 0.8333 | | cosine_accuracy@5 | 0.8333 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.8333 | | cosine_precision@3 | 0.2778 | | cosine_precision@5 | 0.1667 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.8333 | | cosine_recall@3 | 0.8333 | | cosine_recall@5 | 0.8333 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 0.8927 | | cosine_mrr@10 | 0.8611 | | **cosine_map@100** | **0.8611** | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.8333 | | cosine_accuracy@3 | 0.8333 | | cosine_accuracy@5 | 0.8333 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.8333 | | cosine_precision@3 | 0.2778 | | cosine_precision@5 | 0.1667 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.8333 | | cosine_recall@3 | 0.8333 | | cosine_recall@5 | 0.8333 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 0.8927 | | cosine_mrr@10 | 0.8611 | | **cosine_map@100** | **0.8611** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.8333 | | cosine_accuracy@3 | 0.8333 | | cosine_accuracy@5 | 0.8333 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.8333 | | cosine_precision@3 | 0.2778 | | cosine_precision@5 | 0.1667 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.8333 | | cosine_recall@3 | 0.8333 | | cosine_recall@5 | 0.8333 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 0.8927 | | cosine_mrr@10 | 0.8611 | | **cosine_map@100** | **0.8611** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.8333 | | cosine_accuracy@3 | 0.8333 | | cosine_accuracy@5 | 0.8333 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.8333 | | cosine_precision@3 | 0.2778 | | cosine_precision@5 | 0.1667 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.8333 | | cosine_recall@3 | 0.8333 | | cosine_recall@5 | 0.8333 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 0.8859 | | cosine_mrr@10 | 0.8542 | | **cosine_map@100** | **0.8542** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.8333 | | cosine_accuracy@3 | 0.8333 | | cosine_accuracy@5 | 0.8333 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.8333 | | cosine_precision@3 | 0.2778 | | cosine_precision@5 | 0.1667 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.8333 | | cosine_recall@3 | 0.8333 | | cosine_recall@5 | 0.8333 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 0.8835 | | cosine_mrr@10 | 0.8519 | | **cosine_map@100** | **0.8519** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 48 training samples * Columns: context and question * Approximate statistics based on the first 1000 samples: | | context | question | |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | context | question | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------| | The Client will pay a flat fee of Rs. 52,000/-, with 50% (Rs. 26,000/-) due upon signing the agreement and the remaining 50% due one week after completion of pre-production. Payment delays will result in proportional delays in data delivery and editing. | What are the specified payment terms for the photography services under this contract? | | Users can report delays to Customer Care and expect an automatic refund within 3-4 business days if services are canceled or rescheduled by the platform. | What actions can a user take if the platform is unable to fulfill a successfully placed order? | | Signed by James Hira, Managing Director of Electric Vehicle Battery Supplier Pvt. Ltd, and Managing Director of Best Car Manufacturer Pvt. Ltd | Who signed the Battery Supply Agreement on behalf of the Supplier and the Manufacturer? | * 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`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### 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`: 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`: 5 - `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 - `eval_on_start`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |:-----:|:----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0 | 0 | 0.8542 | 0.8611 | 0.8611 | 0.8519 | 0.8611 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.3.1+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", } ``` #### 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} } ```