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Finance_embedding_large_en-V0.1

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dataset_size:10K<n<100K
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:10K<n<100K
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-large-en
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
widget:
- source_sentence: What is the primary purpose of a swap fund?
sentences:
- What is the primary function of a Federal Savings and Loan Association?
- Is the Harmonized System a binding system for origin, valuation, or duty rates?
- How many shares of ABC Inc. did Company A purchase, and at what price per share?
- source_sentence: Calculate the information ratio for Portfolio B.
sentences:
- What is the risk-reward ratio for Stock B?
- Are private companies and individuals considered foreign official institutions?
- What is the role of the Federal Reserve System in relation to U.S. currency?
- source_sentence: What is the official language of Angola?
sentences:
- What are the official languages of Somalia, and which language is most widely
spoken?
- What debts and obligations did Michael Johnson, the executor, have to settle?
- Do horizon returns guarantee future investment performance?
- source_sentence: What is the capital of the United States?
sentences:
- What is the capital and largest city of Mauritius?
- How does Isabelle determine the appropriate bonds to purchase for each year?
- What strategies might ABC Company employ to mitigate its economic exposure?
- source_sentence: How many companies are listed on the NYSE?
sentences:
- 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?
- How do interest rate fluctuations affect the prepayment risk of companion bonds?
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on BAAI/bge-large-en
results:
- task:
type: triplet
name: Triplet
dataset:
name: Original Embedding model Metric
type: Original_Embedding_model_Metric
metrics:
- type: cosine_accuracy
value: 0.5005796728069045
name: Cosine Accuracy
- type: dot_accuracy
value: 0.4977457168620379
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.5014813860620894
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.5003220404482803
name: Euclidean Accuracy
- type: max_accuracy
value: 0.5014813860620894
name: Max Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: Finance model Embedding Metric
type: Finance_model_Embedding_Metric
metrics:
- type: cosine_accuracy
value: 0.9872471982480999
name: Cosine Accuracy
- type: dot_accuracy
value: 0.01120700760015458
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.9868607497101636
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.9872471982480999
name: Euclidean Accuracy
- type: max_accuracy
value: 0.9872471982480999
name: Max Accuracy
---
# SentenceTransformer based on BAAI/bge-large-en
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en). 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:** [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) <!-- at revision abe7d9d814b775ca171121fb03f394dc42974275 -->
- **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)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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 🤗 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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Triplet
* Dataset: `Original_Embedding_model_Metric`
* Evaluated with [<code>TripletEvaluator</code>](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 [<code>TripletEvaluator</code>](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** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## 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: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 25.02 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 152.04 tokens</li><li>max: 460 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 54.96 tokens</li><li>max: 225 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:----------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>When was the Libyan Dinar (LYD) introduced, and what was the exchange rate with the previous currency?</code> | <code>According to the context, the Libyan Dinar (LYD) was introduced in 1971, replacing the Libyan pound at a rate of 1 dinar = 1 pound.<br>The Libyan Dinar (LYD) was introduced in 1971, replacing the Libyan pound at a rate of 1 dinar to 1 pound.</code> | <code>The Libyan Dinar was introduced sometime in the 20th century.<br>The Libyan Dinar was introduced in the 20th century.</code> |
| <code>How many fillér would you have if you exchanged 10 USD for Hungarian Forints at the given exchange rate?</code> | <code>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 fillér, so to find the number of fillér, multiply the HUF amount by 100: 3,390 HUF × 100 fillér/HUF = 339,000 fillér.<br>At the given exchange rate, exchanging 10 USD would give you approximately 339,000 fillér.</code> | <code>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.<br>There is not enough information provided to determine the number of fillér you would get for 10 USD.</code> |
| <code>What is the total value of John's vintage car collection and his wife's jewelry collection combined?</code> | <code>The passage states that John's vintage car collection is valued at $500,000 and his wife's jewelry collection is worth $200,000.<br>To find the total value, we add these two amounts:<br>Vintage car collection: $500,000<br>Jewelry collection: $200,000<br>$500,000 + $200,000 = $700,000<br>Therefore, the total value of John's vintage car collection and his wife's jewelry collection combined is $700,000.<br>The total value of John's vintage car collection and his wife's jewelry collection combined is $700,000.</code> | <code>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.<br>The total value of John's vintage car collection and his wife's jewelry collection cannot be determined from the given information.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](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: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 25.52 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 153.66 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 53.73 tokens</li><li>max: 181 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What factors have contributed to Acme Inc.'s stock becoming a wallflower?</code> | <code>Several factors have contributed to Acme Inc.'s stock becoming a wallflower:<br><br>1. Declining sales: Acme Inc. has experienced a decline in sales, which has negatively impacted its financial performance.<br><br>2. Decreasing profit margins: Along with declining sales, Acme Inc.'s profit margins have also decreased, further affecting its bottom line.<br><br>3. Falling stock price: As a result of the declining sales and profit margins, Acme Inc.'s stock price has dropped significantly.<br><br>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.<br><br>These factors have collectively led to Acme Inc.'s stock falling out of favor with investors, making it a wallflower stock.<br>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.</code> | <code>Acme Inc.'s stock has become a wallflower because its P/E ratio is lower than the industry average.<br>Acme Inc.'s low P/E ratio has caused its stock to become a wallflower.</code> |
| <code>How does the Accumulated Benefit Obligation (ABO) differ from the Projected Benefit Obligation (PBO) in terms of assumptions about future salary increases?</code> | <code>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.<br>The ABO does not consider future salary increases, assuming immediate plan termination, while the PBO incorporates assumptions about future salary increases in its calculations.</code> | <code>The ABO and PBO are the same things and both include assumptions about future salary increases for employees.<br>There is no difference between ABO and PBO in terms of salary increase assumptions.</code> |
| <code>What is the annual interest rate of the annuity, and how is it compounded?</code> | <code>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.<br>The annuity has an annual interest rate of 3%, which is compounded monthly, resulting in compound growth of the account balance.</code> | <code>The annuity has an interest rate that is compounded.<br>The annuity's interest rate is compounded.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](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
<details><summary>Click to expand</summary>
- `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
</details>
### Training Logs
<details><summary>Click to expand</summary>
| 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 | - |
</details>
### 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}
}
```
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