--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - dataset_size:10K - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [baconnier/finance2_dataset_private](https://huggingface.co/datasets/baconnier/finance2_dataset_private) ### 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': 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}) (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 đŸ€— Hub model = SentenceTransformer("baconnier/Finance_embedding_large_en-V0.1") # Run inference sentences = [ 'How many companies are listed on the NYSE?', 'What are the trading hours of the New York Stock Exchange?', 'Why do Maple Leaf coins often trade at a premium over their metal content value?', ] 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 #### Triplet * Dataset: `Original_Embedding_model_Metric` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:-------------------|:-----------| | cosine_accuracy | 0.5006 | | dot_accuracy | 0.4977 | | manhattan_accuracy | 0.5015 | | euclidean_accuracy | 0.5003 | | **max_accuracy** | **0.5015** | #### Triplet * Dataset: `Finance_model_Embedding_Metric` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:-------------------|:-----------| | cosine_accuracy | 0.9872 | | dot_accuracy | 0.0112 | | manhattan_accuracy | 0.9869 | | euclidean_accuracy | 0.9872 | | **max_accuracy** | **0.9872** | ## Training Details ### Training Dataset #### baconnier/finance2_dataset_private * Dataset: [baconnier/finance2_dataset_private](https://huggingface.co/datasets/baconnier/finance2_dataset_private) at [f384fe0](https://huggingface.co/datasets/baconnier/finance2_dataset_private/tree/f384fe051309cd3bec396a0ed587e298e6b30e0f) * Size: 36,223 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 | |:----------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | When was the Libyan Dinar (LYD) introduced, and what was the exchange rate with the previous currency? | According to the context, the Libyan Dinar (LYD) was introduced in 1971, replacing the Libyan pound at a rate of 1 dinar = 1 pound.
The Libyan Dinar (LYD) was introduced in 1971, replacing the Libyan pound at a rate of 1 dinar to 1 pound. | The Libyan Dinar was introduced sometime in the 20th century.
The Libyan Dinar was introduced in the 20th century. | | How many fillĂ©r would you have if you exchanged 10 USD for Hungarian Forints at the given exchange rate? | First, calculate the HUF equivalent of 10 USD using the exchange rate: 1 USD ≈ 339 HUF, so 10 USD ≈ 10 × 339 = 3,390 HUF. The context also states that 1 HUF = 100 fillĂ©r, so to find the number of fillĂ©r, multiply the HUF amount by 100: 3,390 HUF × 100 fillĂ©r/HUF = 339,000 fillĂ©r.
At the given exchange rate, exchanging 10 USD would give you approximately 339,000 fillér. | The context provides the exchange rate between USD and HUF, but it doesn't mention how many HUF equal one fillér. Without knowing the conversion rate between HUF and fillér, it's impossible to calculate the number of fillér you'd get for 10 USD.
There is not enough information provided to determine the number of fillér you would get for 10 USD. | | What is the total value of John's vintage car collection and his wife's jewelry collection combined? | The passage states that John's vintage car collection is valued at $500,000 and his wife's jewelry collection is worth $200,000.
To find the total value, we add these two amounts:
Vintage car collection: $500,000
Jewelry collection: $200,000
$500,000 + $200,000 = $700,000
Therefore, the total value of John's vintage car collection and his wife's jewelry collection combined is $700,000.
The total value of John's vintage car collection and his wife's jewelry collection combined is $700,000. | The passage mentions that John has a vintage car collection and his wife has a jewelry collection. However, the values of these collections are not provided.
The total value of John's vintage car collection and his wife's jewelry collection cannot be determined from the given information. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### baconnier/finance2_dataset_private * Dataset: [baconnier/finance2_dataset_private](https://huggingface.co/datasets/baconnier/finance2_dataset_private) at [f384fe0](https://huggingface.co/datasets/baconnier/finance2_dataset_private/tree/f384fe051309cd3bec396a0ed587e298e6b30e0f) * Size: 7,762 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 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What factors have contributed to Acme Inc.'s stock becoming a wallflower? | Several factors have contributed to Acme Inc.'s stock becoming a wallflower:

1. Declining sales: Acme Inc. has experienced a decline in sales, which has negatively impacted its financial performance.

2. Decreasing profit margins: Along with declining sales, Acme Inc.'s profit margins have also decreased, further affecting its bottom line.

3. Falling stock price: As a result of the declining sales and profit margins, Acme Inc.'s stock price has dropped significantly.

4. Low P/E ratio: The company's P/E ratio has decreased to 8, which is much lower than the industry average of 15. This low P/E ratio indicates that investors are not willing to pay a premium for Acme Inc.'s stock due to its poor financial performance.

These factors have collectively led to Acme Inc.'s stock falling out of favor with investors, making it a wallflower stock.
Acme Inc.'s stock has become a wallflower due to a combination of factors, including declining sales, decreasing profit margins, a falling stock price, and a low P/E ratio compared to the industry average, which have led to investors losing interest in the company's stock. | Acme Inc.'s stock has become a wallflower because its P/E ratio is lower than the industry average.
Acme Inc.'s low P/E ratio has caused its stock to become a wallflower. | | How does the Accumulated Benefit Obligation (ABO) differ from the Projected Benefit Obligation (PBO) in terms of assumptions about future salary increases? | The Accumulated Benefit Obligation (ABO) assumes that the pension plan will terminate immediately and does not take into account any future salary increases. In contrast, the Projected Benefit Obligation (PBO) includes assumptions about future salary increases when calculating the present value of an employee's pension benefits.
The ABO does not consider future salary increases, assuming immediate plan termination, while the PBO incorporates assumptions about future salary increases in its calculations. | The ABO and PBO are the same things and both include assumptions about future salary increases for employees.
There is no difference between ABO and PBO in terms of salary increase assumptions. | | What is the annual interest rate of the annuity, and how is it compounded? | According to the context, the annuity has an annual interest rate of 3%. This interest is compounded monthly, meaning the 3% annual rate is divided by 12 (the number of months in a year) and applied to the account balance each month. This results in a slightly higher effective annual rate due to the compound growth.
The annuity has an annual interest rate of 3%, which is compounded monthly, resulting in compound growth of the account balance. | The annuity has an interest rate that is compounded.
The annuity's interest rate is compounded. | * 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`: 1 - `warmup_ratio`: 0.1 - `bf16`: 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`: 1 - `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`: True - `fp16`: False - `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`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | loss | Finance_model_Embedding_Metric_max_accuracy | Original_Embedding_model_Metric_max_accuracy | |:------:|:----:|:-------------:|:------:|:-------------------------------------------:|:--------------------------------------------:| | 0 | 0 | - | - | - | 0.5015 | | 0.0044 | 10 | 1.0947 | - | - | - | | 0.0088 | 20 | 0.9611 | - | - | - | | 0.0133 | 30 | 0.6565 | - | - | - | | 0.0177 | 40 | 0.4234 | - | - | - | | 0.0221 | 50 | 0.1672 | - | - | - | | 0.0265 | 60 | 0.1305 | - | - | - | | 0.0309 | 70 | 0.1381 | - | - | - | | 0.0353 | 80 | 0.0846 | - | - | - | | 0.0398 | 90 | 0.1078 | - | - | - | | 0.0442 | 100 | 0.0867 | - | - | - | | 0.0486 | 110 | 0.0935 | - | - | - | | 0.0530 | 120 | 0.1197 | - | - | - | | 0.0574 | 130 | 0.0841 | - | - | - | | 0.0618 | 140 | 0.0792 | - | - | - | | 0.0663 | 150 | 0.0811 | - | - | - | | 0.0707 | 160 | 0.1032 | - | - | - | | 0.0751 | 170 | 0.1051 | - | - | - | | 0.0795 | 180 | 0.1091 | - | - | - | | 0.0839 | 190 | 0.0778 | - | - | - | | 0.0883 | 200 | 0.1006 | - | - | - | | 0.0928 | 210 | 0.0738 | - | - | - | | 0.0972 | 220 | 0.1105 | - | - | - | | 0.1003 | 227 | - | 0.1181 | - | - | | 0.1016 | 230 | 0.0697 | - | - | - | | 0.1060 | 240 | 0.064 | - | - | - | | 0.1104 | 250 | 0.1204 | - | - | - | | 0.1148 | 260 | 0.0664 | - | - | - | | 0.1193 | 270 | 0.0776 | - | - | - | | 0.1237 | 280 | 0.0574 | - | - | - | | 0.1281 | 290 | 0.054 | - | - | - | | 0.1325 | 300 | 0.0681 | - | - | - | | 0.1369 | 310 | 0.1315 | - | - | - | | 0.1413 | 320 | 0.1005 | - | - | - | | 0.1458 | 330 | 0.0613 | - | - | - | | 0.1502 | 340 | 0.0476 | - | - | - | | 0.1546 | 350 | 0.0735 | - | - | - | | 0.1590 | 360 | 0.106 | - | - | - | | 0.1634 | 370 | 0.1082 | - | - | - | | 0.1678 | 380 | 0.0437 | - | - | - | | 0.1723 | 390 | 0.0782 | - | - | - | | 0.1767 | 400 | 0.0858 | - | - | - | | 0.1811 | 410 | 0.0563 | - | - | - | | 0.1855 | 420 | 0.0798 | - | - | - | | 0.1899 | 430 | 0.0674 | - | - | - | | 0.1943 | 440 | 0.0887 | - | - | - | | 0.1988 | 450 | 0.1032 | - | - | - | | 0.2005 | 454 | - | 0.0720 | - | - | | 0.2032 | 460 | 0.0591 | - | - | - | | 0.2076 | 470 | 0.0581 | - | - | - | | 0.2120 | 480 | 0.1544 | - | - | - | | 0.2164 | 490 | 0.0169 | - | - | - | | 0.2208 | 500 | 0.0593 | - | - | - | | 0.2253 | 510 | 0.0971 | - | - | - | | 0.2297 | 520 | 0.0567 | - | - | - | | 0.2341 | 530 | 0.0501 | - | - | - | | 0.2385 | 540 | 0.0452 | - | - | - | | 0.2429 | 550 | 0.0574 | - | - | - | | 0.2473 | 560 | 0.0616 | - | - | - | | 0.2518 | 570 | 0.1414 | - | - | - | | 0.2562 | 580 | 0.0776 | - | - | - | | 0.2606 | 590 | 0.0828 | - | - | - | | 0.2650 | 600 | 0.1046 | - | - | - | | 0.2694 | 610 | 0.1248 | - | - | - | | 0.2739 | 620 | 0.0547 | - | - | - | | 0.2783 | 630 | 0.0424 | - | - | - | | 0.2827 | 640 | 0.1401 | - | - | - | | 0.2871 | 650 | 0.0746 | - | - | - | | 0.2915 | 660 | 0.0279 | - | - | - | | 0.2959 | 670 | 0.1115 | - | - | - | | 0.3004 | 680 | 0.0846 | - | - | - | | 0.3008 | 681 | - | 0.0655 | - | - | | 0.3048 | 690 | 0.063 | - | - | - | | 0.3092 | 700 | 0.0949 | - | - | - | | 0.3136 | 710 | 0.0482 | - | - | - | | 0.3180 | 720 | 0.063 | - | - | - | | 0.3224 | 730 | 0.0524 | - | - | - | | 0.3269 | 740 | 0.0752 | - | - | - | | 0.3313 | 750 | 0.0964 | - | - | - | | 0.3357 | 760 | 0.0378 | - | - | - | | 0.3401 | 770 | 0.0611 | - | - | - | | 0.3445 | 780 | 0.0764 | - | - | - | | 0.3489 | 790 | 0.0391 | - | - | - | | 0.3534 | 800 | 0.0549 | - | - | - | | 0.3578 | 810 | 0.0717 | - | - | - | | 0.3622 | 820 | 0.0688 | - | - | - | | 0.3666 | 830 | 0.0891 | - | - | - | | 0.3710 | 840 | 0.034 | - | - | - | | 0.3754 | 850 | 0.0773 | - | - | - | | 0.3799 | 860 | 0.0377 | - | - | - | | 0.3843 | 870 | 0.0629 | - | - | - | | 0.3887 | 880 | 0.0544 | - | - | - | | 0.3931 | 890 | 0.0384 | - | - | - | | 0.3975 | 900 | 0.0489 | - | - | - | | 0.4011 | 908 | - | 0.0708 | - | - | | 0.4019 | 910 | 0.0757 | - | - | - | | 0.4064 | 920 | 0.0904 | - | - | - | | 0.4108 | 930 | 0.0569 | - | - | - | | 0.4152 | 940 | 0.0875 | - | - | - | | 0.4196 | 950 | 0.0452 | - | - | - | | 0.4240 | 960 | 0.0791 | - | - | - | | 0.4284 | 970 | 0.0721 | - | - | - | | 0.4329 | 980 | 0.0354 | - | - | - | | 0.4373 | 990 | 0.0171 | - | - | - | | 0.4417 | 1000 | 0.0726 | - | - | - | | 0.4461 | 1010 | 0.0546 | - | - | - | | 0.4505 | 1020 | 0.0352 | - | - | - | | 0.4549 | 1030 | 0.0424 | - | - | - | | 0.4594 | 1040 | 0.063 | - | - | - | | 0.4638 | 1050 | 0.0928 | - | - | - | | 0.4682 | 1060 | 0.0648 | - | - | - | | 0.4726 | 1070 | 0.0591 | - | - | - | | 0.4770 | 1080 | 0.0506 | - | - | - | | 0.4814 | 1090 | 0.0991 | - | - | - | | 0.4859 | 1100 | 0.0268 | - | - | - | | 0.4903 | 1110 | 0.039 | - | - | - | | 0.4947 | 1120 | 0.0913 | - | - | - | | 0.4991 | 1130 | 0.0413 | - | - | - | | 0.5013 | 1135 | - | 0.0542 | - | - | | 0.5035 | 1140 | 0.0706 | - | - | - | | 0.5080 | 1150 | 0.0476 | - | - | - | | 0.5124 | 1160 | 0.0567 | - | - | - | | 0.5168 | 1170 | 0.0425 | - | - | - | | 0.5212 | 1180 | 0.0378 | - | - | - | | 0.5256 | 1190 | 0.0531 | - | - | - | | 0.5300 | 1200 | 0.0839 | - | - | - | | 0.5345 | 1210 | 0.0378 | - | - | - | | 0.5389 | 1220 | 0.0309 | - | - | - | | 0.5433 | 1230 | 0.0213 | - | - | - | | 0.5477 | 1240 | 0.0769 | - | - | - | | 0.5521 | 1250 | 0.0543 | - | - | - | | 0.5565 | 1260 | 0.0587 | - | - | - | | 0.5610 | 1270 | 0.0658 | - | - | - | | 0.5654 | 1280 | 0.0621 | - | - | - | | 0.5698 | 1290 | 0.0558 | - | - | - | | 0.5742 | 1300 | 0.0521 | - | - | - | | 0.5786 | 1310 | 0.0481 | - | - | - | | 0.5830 | 1320 | 0.0373 | - | - | - | | 0.5875 | 1330 | 0.0652 | - | - | - | | 0.5919 | 1340 | 0.0685 | - | - | - | | 0.5963 | 1350 | 0.077 | - | - | - | | 0.6007 | 1360 | 0.0521 | - | - | - | | 0.6016 | 1362 | - | 0.0516 | - | - | | 0.6051 | 1370 | 0.0378 | - | - | - | | 0.6095 | 1380 | 0.0442 | - | - | - | | 0.6140 | 1390 | 0.0435 | - | - | - | | 0.6184 | 1400 | 0.0288 | - | - | - | | 0.6228 | 1410 | 0.0565 | - | - | - | | 0.6272 | 1420 | 0.0449 | - | - | - | | 0.6316 | 1430 | 0.0226 | - | - | - | | 0.6360 | 1440 | 0.0395 | - | - | - | | 0.6405 | 1450 | 0.059 | - | - | - | | 0.6449 | 1460 | 0.1588 | - | - | - | | 0.6493 | 1470 | 0.0562 | - | - | - | | 0.6537 | 1480 | 0.117 | - | - | - | | 0.6581 | 1490 | 0.107 | - | - | - | | 0.6625 | 1500 | 0.0972 | - | - | - | | 0.6670 | 1510 | 0.0684 | - | - | - | | 0.6714 | 1520 | 0.0743 | - | - | - | | 0.6758 | 1530 | 0.0784 | - | - | - | | 0.6802 | 1540 | 0.0892 | - | - | - | | 0.6846 | 1550 | 0.0676 | - | - | - | | 0.6890 | 1560 | 0.0312 | - | - | - | | 0.6935 | 1570 | 0.0834 | - | - | - | | 0.6979 | 1580 | 0.0241 | - | - | - | | 0.7019 | 1589 | - | 0.0495 | - | - | | 0.7023 | 1590 | 0.0391 | - | - | - | | 0.7067 | 1600 | 0.043 | - | - | - | | 0.7111 | 1610 | 0.045 | - | - | - | | 0.7155 | 1620 | 0.0216 | - | - | - | | 0.7200 | 1630 | 0.0715 | - | - | - | | 0.7244 | 1640 | 0.0173 | - | - | - | | 0.7288 | 1650 | 0.0249 | - | - | - | | 0.7332 | 1660 | 0.0187 | - | - | - | | 0.7376 | 1670 | 0.0647 | - | - | - | | 0.7420 | 1680 | 0.0199 | - | - | - | | 0.7465 | 1690 | 0.0333 | - | - | - | | 0.7509 | 1700 | 0.0718 | - | - | - | | 0.7553 | 1710 | 0.0373 | - | - | - | | 0.7597 | 1720 | 0.0744 | - | - | - | | 0.7641 | 1730 | 0.0185 | - | - | - | | 0.7686 | 1740 | 0.0647 | - | - | - | | 0.7730 | 1750 | 0.0289 | - | - | - | | 0.7774 | 1760 | 0.034 | - | - | - | | 0.7818 | 1770 | 0.0184 | - | - | - | | 0.7862 | 1780 | 0.0537 | - | - | - | | 0.7906 | 1790 | 0.0724 | - | - | - | | 0.7951 | 1800 | 0.0511 | - | - | - | | 0.7995 | 1810 | 0.0165 | - | - | - | | 0.8021 | 1816 | - | 0.0488 | - | - | | 0.8039 | 1820 | 0.0364 | - | - | - | | 0.8083 | 1830 | 0.1126 | - | - | - | | 0.8127 | 1840 | 0.0148 | - | - | - | | 0.8171 | 1850 | 0.0722 | - | - | - | | 0.8216 | 1860 | 0.0586 | - | - | - | | 0.8260 | 1870 | 0.0496 | - | - | - | | 0.8304 | 1880 | 0.026 | - | - | - | | 0.8348 | 1890 | 0.0417 | - | - | - | | 0.8392 | 1900 | 0.0586 | - | - | - | | 0.8436 | 1910 | 0.0255 | - | - | - | | 0.8481 | 1920 | 0.0329 | - | - | - | | 0.8525 | 1930 | 0.015 | - | - | - | | 0.8569 | 1940 | 0.0657 | - | - | - | | 0.8613 | 1950 | 0.0465 | - | - | - | | 0.8657 | 1960 | 0.0107 | - | - | - | | 0.8701 | 1970 | 0.0401 | - | - | - | | 0.8746 | 1980 | 0.022 | - | - | - | | 0.8790 | 1990 | 0.061 | - | - | - | | 0.8834 | 2000 | 0.0474 | - | - | - | | 0.8878 | 2010 | 0.0358 | - | - | - | | 0.8922 | 2020 | 0.0599 | - | - | - | | 0.8966 | 2030 | 0.0522 | - | - | - | | 0.9011 | 2040 | 0.0312 | - | - | - | | 0.9024 | 2043 | - | 0.0421 | - | - | | 0.9055 | 2050 | 0.024 | - | - | - | | 0.9099 | 2060 | 0.1085 | - | - | - | | 0.9143 | 2070 | 0.0144 | - | - | - | | 0.9187 | 2080 | 0.038 | - | - | - | | 0.9231 | 2090 | 0.0948 | - | - | - | | 0.9276 | 2100 | 0.0317 | - | - | - | | 0.9320 | 2110 | 0.0674 | - | - | - | | 0.9364 | 2120 | 0.081 | - | - | - | | 0.9408 | 2130 | 0.036 | - | - | - | | 0.9452 | 2140 | 0.0649 | - | - | - | | 0.9496 | 2150 | 0.0235 | - | - | - | | 0.9541 | 2160 | 0.0291 | - | - | - | | 0.9585 | 2170 | 0.0293 | - | - | - | | 0.9629 | 2180 | 0.0703 | - | - | - | | 0.9673 | 2190 | 0.0148 | - | - | - | | 0.9717 | 2200 | 0.0397 | - | - | - | | 0.9761 | 2210 | 0.0552 | - | - | - | | 0.9806 | 2220 | 0.0097 | - | - | - | | 0.9850 | 2230 | 0.0723 | - | - | - | | 0.9894 | 2240 | 0.0379 | - | - | - | | 0.9938 | 2250 | 0.0289 | - | - | - | | 0.9982 | 2260 | 0.0267 | - | - | - | | 1.0 | 2264 | - | - | 0.9872 | - |
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.0 - Transformers: 4.41.1 - PyTorch: 2.3.0+cu121 - Accelerate: 0.30.1 - 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", } ``` #### 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} } ```