--- base_model: sentence-transformers/msmarco-distilbert-base-v2 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 - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:88018 - loss:TripletLoss widget: - source_sentence: How should SpinRecords.com notify NETTAXI of a potential indemnifiable claim? sentences: - '4. The Company shall have no obligations to Verenium with respect to the use of such information, or disclosure to others not party to this Agreement, of such information which: (d) is rightfully and in good faith developed by Company independently of any disclosures made under this Agreement, as evidenced by Company’s competent written records; or ' - 13. Changes To This Privacy Policy We may update this Privacy Policy to reflect changes to our information practices. If we make any material changes we will notify you by email (sent to the e-mail address specified in your account) or by means of a notice on the Services prior to the change becoming effective. We encourage you to periodically review this page for the latest information on our privacy practices. - 7.2 Indemnification by NETTAXI. NETTAXI shall defend, indemnify and ---------------------------- hold SpinRecords.com harmless from any and all damages, liabilities, costs and expenses (including, but not limited to reasonable attorneys' fees) incurred by SpinRecords.com as a result of (1) any breach of this Agreement; (ii) any claim that the NETTAXI Brand Features or any part thereof, infringes or misappropriates any Intellectual Property Right of a third party; or (iii) any claim arising out of Spinrecords.com's display of the NETTAXI Brand Features SpinRecords.com shall provide NETTAXI with written notice of the claim and permit NETTAXI to control the defense, settlement, adjustment or compromise of any such claim. SpinRecords.com may employ counsel at its own expense to assist it with respect to any such claim; provided, however, that if such counsel is necessary because of a conflict of interest of either NETTAXI or its counsel or because NETTAXI does not assume control, NETTAXI will bear the expense of such counsel. - source_sentence: What types of advertisements does Crazy Labs accept within their apps? sentences: - Each party each agrees that it will not knowingly do anything inconsistent with the other party's ownership of such party's intellectual property, including without limitation, questioning the validity of that party's Trademarks or registering or attempting to register the other party's Trademarks in its own name or that of any other firm, person or corporation. - '11. Advertisements We accept advertisements, in various formats (such as banners, interstitials, rewarded videos, etc.) from third parties ad networks which may be displayed in our Crazy Labs Apps. These third parties ad networks may collect and use, inter alia (i) information about your visits to Crazy Labs Apps in connection with such marketing, sales and advertising activities; and (ii) geographic tracking and carrier network preferences. (iii) information, such as age, gender and logged from device to ensure that appropriate advertising is presented within the App and calculate or control the number of views of an ad, and/or deliver advertisements relating to User''s interests, and measure the effectiveness of advertisements campaigns. The delivery of advertisements to you may be based on IP address, device identifiers and other Personal Information gathered during your use of the Crazy Labs Apps. Note that third parties ad networks which are referred to in relation to the Crazy Labs Apps may include third parties service providers, such as Facebook and other ad networks, in addition to those which are listed in the following link: https://www.tabtale.com/3rdparties/. Note that if you click on any of these advertisements, the advertisers may use cookies and other web-tracking technologies (such as tracking pixel agent or visitor identification technology, etc.) on your device to collect data regarding advertisement performance, your interaction with such advertisements and our Crazy Labs Apps and your interests (which may include, non-personal and/or personal information (such as, device and network information, unique identifiers, gender, age and geo-location) about you) in order to serve you advertisements, including targeted advertisements, and for the legitimate business interests of such Third Parties ad networks. We recommend that you review the terms of use and privacy policy of any third party advertisers with whom you are interacting before doing so. Their privacy policy, not ours, will apply to any of those interactions.' - We want our advertising to be as relevant and interesting as the other information you find on our Services. With this in mind, we use all of the information we have about you to show you relevant ads. We do not share information that personally identifies you (personally identifiable information is information like name or email address that can by itself be used to contact you or identifies who you are) with advertising, measurement or analytics partners unless you give us permission. We may provide these partners with information about the reach and effectiveness of their advertising without providing information that personally identifies you, or if we have aggregated the information so that it does not personally identify you. 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Each party is precluded from conducting themselves in any way that might suggest or insinuate any association different from that of an independent entity, nor shall either party possess the authority to obligate or commit the other party in any manner. sentences: - 'The parties hereby grant to each other non-exclusive, fully-paid, royalty-free licenses to utilize the other party''s trademarks, as follows: (a) Biocept Trademarks. To facilitate the promotion and performance of Tests, during the Term Biocept hereby grants Life Technologies a non-exclusive, royalty-free, non-transferable license to use the Biocept Trademarks solely foruse in connection with the promotion and performance of the Tests in the Territory.' - This Agreement may not be assigned or otherwise transferred, nor may any right or obligations hereunder be assigned or transferred, by either Party without the prior written consent of the other Party; provided, however, that Licensor may, without such consent, assign this Agreement and its rights and obligations hereunder, in whole or in part, to an Affiliate or in connection with the transfer or sale of all or substantially all of its assets related to the Licensed Product or the business relating thereto, or in the event of its merger or consolidation or change in control or similar transaction. - 9.2 Relationship of Parties. The parties are independent contractors ------------------------- under this Agreement and no other relationship is intended, including a partnership, franchise, joint venture, agency, employer/employee, fiduciary, master/servant relationship, or other special relationship. Neither party shall act in a manner which expresses or implies a relationship other than that of independent contractor, nor bind the other party. - source_sentence: In which section can I find the specifics of the 'Initial Term'? sentences: - 1.22 "Initial Term" has the meaning set forth in Section 8.1. - If the Reseller sells less than 50% of any year's Annual Milestone, Todos, in its sole discretion, may either (a) cancel the Reseller's exclusivity, and market, distribute, and sell the Products in the Territory directly or indirectly through other distributors and resellers, while leaving the Reseller with a non-exclusive right to distribute and sell the Products for the remainder of the term, or (b) terminate the Agreement upon one hundred eighty (180) days prior written notice, provided that the Reseller does not cure its failure to achieve 50% of the applicable year's Annual Milestone within the 180-day notice period. - 6 Term and Termination. - source_sentence: In what circumstances can FCE assume responsibility for a Program Patent? sentences: - We may also collect anonymous, statistical data from users of the Services, such as a user's browser version, operating system version, country, page loading time, type of device, number of visits, time using the Services, network, demographic estimates, flow-through the website and/or Services or referral source, which may then be aggregated. We may use non-personal data that we collect from you to improve the Services or to support advertising services. For registered users, this anonymous, statistical data may include that relating to their activities, such as high scores, game rankings, league rankings, game challenges, avatars etc. - Notwithstanding the foregoing, in the event ExxonMobil decides not to prosecute, defend, enforce, maintain or decides to abandon any Program Patent, then ExxonMobil will provide notice thereof to FCE, and FCE will then have the right, but not the obligation, to prosecute or maintain the Program Patent and sole responsibility for the continuing costs, taxes, legal fees, maintenance fees and other fees associated with that Program Patent. - 4. Limitation of Liability of the Sponsor. The Sponsor shall not be liable for any error of judgment or mistake of law or for any act or omission in the oversight, administration or management of the Trust or the performance of its duties hereunder, except for willful misfeasance, bad faith or gross negligence in the performance of its duties, or by reason of the reckless disregard of its obligations and duties hereunder. As used in this Section 4, the term "Sponsor" shall include Domini and/or any of its affiliates and the directors, officers and employees of Domini and/or any of its affiliates. model-index: - name: SentenceTransformer based on sentence-transformers/msmarco-distilbert-base-v2 results: - task: type: information-retrieval name: Information Retrieval dataset: name: msmarco distilbert base v2 type: msmarco-distilbert-base-v2 metrics: - type: cosine_accuracy@1 value: 0.3953048087845513 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5376751230594472 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.594471790988262 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.673608481635744 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.3953048087845513 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1792250410198157 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1188943581976524 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.06736084816357439 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.3953048087845513 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5376751230594472 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.594471790988262 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.673608481635744 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5276829229789854 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4818510605049796 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.48897515764559735 name: Cosine Map@100 - type: dot_accuracy@1 value: 0.3964407421431276 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5335100340780008 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.5933358576296858 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.6743657705414615 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3964407421431276 name: Dot Precision@1 - type: dot_precision@3 value: 0.17783667802600023 name: Dot Precision@3 - type: dot_precision@5 value: 0.11866717152593716 name: Dot Precision@5 - type: dot_precision@10 value: 0.06743657705414616 name: Dot Precision@10 - type: dot_recall@1 value: 0.3964407421431276 name: Dot Recall@1 - type: dot_recall@3 value: 0.5335100340780008 name: Dot Recall@3 - type: dot_recall@5 value: 0.5933358576296858 name: Dot Recall@5 - type: dot_recall@10 value: 0.6743657705414615 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5274757216450244 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4814724160521211 name: Dot Mrr@10 - type: dot_map@100 value: 0.4884569183065979 name: Dot Map@100 --- # SentenceTransformer based on sentence-transformers/msmarco-distilbert-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/msmarco-distilbert-base-v2](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-v2). 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:** [sentence-transformers/msmarco-distilbert-base-v2](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-v2) - **Maximum Sequence Length:** 350 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': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, '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("kperkins411/msmarco-distilbert-base-v2_triplet_legal") # Run inference sentences = [ 'In what circumstances can FCE assume responsibility for a Program Patent?', 'Notwithstanding the foregoing, in the event ExxonMobil decides not to prosecute, defend, enforce, maintain or decides to abandon any Program Patent, then ExxonMobil will provide notice thereof to FCE, and FCE will then have the right, but not the obligation, to prosecute or maintain the Program Patent and sole responsibility for the continuing costs, taxes, legal fees, maintenance fees and other fees associated with that Program Patent.', '4. Limitation of Liability of the Sponsor. The Sponsor shall not be liable for any error of judgment or mistake of law or for any act or omission in the oversight, administration or management of the Trust or the performance of its duties hereunder, except for willful misfeasance, bad faith or gross negligence in the performance of its duties, or by reason of the reckless disregard of its obligations and duties hereunder. As used in this Section 4, the term "Sponsor" shall include Domini and/or any of its affiliates and the directors, officers and employees of Domini and/or any of its affiliates.', ] 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: `msmarco-distilbert-base-v2` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.3953 | | cosine_accuracy@3 | 0.5377 | | cosine_accuracy@5 | 0.5945 | | cosine_accuracy@10 | 0.6736 | | cosine_precision@1 | 0.3953 | | cosine_precision@3 | 0.1792 | | cosine_precision@5 | 0.1189 | | cosine_precision@10 | 0.0674 | | cosine_recall@1 | 0.3953 | | cosine_recall@3 | 0.5377 | | cosine_recall@5 | 0.5945 | | cosine_recall@10 | 0.6736 | | cosine_ndcg@10 | 0.5277 | | cosine_mrr@10 | 0.4819 | | **cosine_map@100** | **0.489** | | dot_accuracy@1 | 0.3964 | | dot_accuracy@3 | 0.5335 | | dot_accuracy@5 | 0.5933 | | dot_accuracy@10 | 0.6744 | | dot_precision@1 | 0.3964 | | dot_precision@3 | 0.1778 | | dot_precision@5 | 0.1187 | | dot_precision@10 | 0.0674 | | dot_recall@1 | 0.3964 | | dot_recall@3 | 0.5335 | | dot_recall@5 | 0.5933 | | dot_recall@10 | 0.6744 | | dot_ndcg@10 | 0.5275 | | dot_mrr@10 | 0.4815 | | dot_map@100 | 0.4885 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 88,018 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What happens if a Party fails to retain records for the required period? | Each Party will retain such records for at least three (3) years following expiration or termination of this Agreement or such longer period as may be required by applicable law or regulation. | Either party hereto may terminate this Agreement after the Initial Period upon at least six (6) months' prior written notice to the other party thereof. | | What happens if a Party fails to retain records for the required period? | Each Party will retain such records for at least three (3) years following expiration or termination of this Agreement or such longer period as may be required by applicable law or regulation. | The Agreement may be terminated by both Parties with a notification period of *** before the end of the Initial Term of the Agreement. | | What happens if a Party fails to retain records for the required period? | Each Party will retain such records for at least three (3) years following expiration or termination of this Agreement or such longer period as may be required by applicable law or regulation. | For twelve (12) months after delivery of the Master Copy of each Licensed Product to Licensee, Licensor warrants that the media in which the Licensed Products are stored shall be free from defects in materials and workmanship, assuming normal use. Licensee may return any defective media to Licensor for replacement free of charge during such twelve (12) month period. | * Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 1,084 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:--------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Are Capital Contributions categorized as either 'Initial' or 'Additional' in the accounts? | Capital Accounts

An individual capital account (the "Capital Accounts") will be maintained for each Participant and their Initial Capital Contribution will be credited to this account. Any Additional Capital Contributions made by any Participant will be credited to that Participant's individual Capital Account. | Section 4.3 Deposits and Payments 19 | | Are Capital Contributions categorized as either 'Initial' or 'Additional' in the accounts? | Capital Accounts

An individual capital account (the "Capital Accounts") will be maintained for each Participant and their Initial Capital Contribution will be credited to this account. Any Additional Capital Contributions made by any Participant will be credited to that Participant's individual Capital Account. | Section 2.1 The Fund agrees at its own expense to execute any and all documents, to furnish any and all information, and to take any other actions that may be reasonably necessary in connection with the qualification of the Shares for sale in those states that Integrity may designate. | | Are Capital Contributions categorized as either 'Initial' or 'Additional' in the accounts? | Capital Accounts

An individual capital account (the "Capital Accounts") will be maintained for each Participant and their Initial Capital Contribution will be credited to this account. Any Additional Capital Contributions made by any Participant will be credited to that Participant's individual Capital Account. | Section 1.9 Integrity shall prepare and deliver reports to the Treasurer of the Fund and to the Investment Adviser on a regular, at least quarterly, basis, showing the distribution expenses incurred pursuant to this Agreement and the Plan and the purposes therefore, as well as any supplemental reports as the Trustees, from time to time, may reasonably request. | * Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `learning_rate`: 2e-05 - `num_train_epochs`: 6 - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `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`: 6 - `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`: True - `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`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | msmarco-distilbert-base-v2_cosine_map@100 | |:----------:|:--------:|:-------------:|:----------:|:-----------------------------------------:| | 0 | 0 | - | - | 0.4145 | | 0.1453 | 100 | 1.7626 | - | - | | 0.2907 | 200 | 0.9595 | - | - | | 0.4360 | 300 | 0.7263 | - | - | | 0.5814 | 400 | 0.6187 | - | - | | 0.7267 | 500 | 0.5571 | - | - | | 0.8721 | 600 | 0.4885 | - | - | | 1.0131 | 697 | - | 0.3676 | - | | 1.0044 | 700 | 0.4283 | - | - | | 1.1497 | 800 | 0.3956 | - | - | | 1.2951 | 900 | 0.2941 | - | - | | 1.4404 | 1000 | 0.2437 | - | - | | 1.5858 | 1100 | 0.1988 | - | - | | 1.7311 | 1200 | 0.185 | - | - | | 1.8765 | 1300 | 0.1571 | - | - | | 2.0131 | 1394 | - | 0.2679 | - | | 2.0087 | 1400 | 0.1409 | - | - | | 2.1541 | 1500 | 0.1368 | - | - | | 2.2994 | 1600 | 0.111 | - | - | | 2.4448 | 1700 | 0.0994 | - | - | | 2.5901 | 1800 | 0.0837 | - | - | | 2.7355 | 1900 | 0.076 | - | - | | 2.8808 | 2000 | 0.0645 | - | - | | 3.0131 | 2091 | - | 0.2412 | - | | 3.0131 | 2100 | 0.0607 | - | - | | 3.1584 | 2200 | 0.0609 | - | - | | 3.3038 | 2300 | 0.0503 | - | - | | 3.4491 | 2400 | 0.0483 | - | - | | 3.5945 | 2500 | 0.0402 | - | - | | 3.7398 | 2600 | 0.0397 | - | - | | 3.8852 | 2700 | 0.0305 | - | - | | 4.0131 | 2788 | - | 0.2196 | - | | 4.0174 | 2800 | 0.0304 | - | - | | 4.1628 | 2900 | 0.0307 | - | - | | 4.3081 | 3000 | 0.0256 | - | - | | 4.4535 | 3100 | 0.0258 | - | - | | 4.5988 | 3200 | 0.0212 | - | - | | 4.7442 | 3300 | 0.0213 | - | - | | 4.8895 | 3400 | 0.0174 | - | - | | 5.0131 | 3485 | - | 0.2036 | - | | 5.0218 | 3500 | 0.0191 | - | - | | 5.1672 | 3600 | 0.0198 | - | - | | 5.3125 | 3700 | 0.0161 | - | - | | 5.4578 | 3800 | 0.0166 | - | - | | 5.6032 | 3900 | 0.0135 | - | - | | 5.7485 | 4000 | 0.0145 | - | - | | 5.8939 | 4100 | 0.0129 | - | - | | **5.9346** | **4128** | **-** | **0.1966** | **0.489** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.9 - Sentence Transformers: 3.1.0.dev0 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.31.0 - Datasets: 2.19.1 - 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", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```