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Neelkumar
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my-embedding-gemma-5424

Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:5424
loss:MultipleNegativesRankingLoss
text-embeddings-inference
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my-embedding-gemma-5424 / README.md
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:5424
- loss:MultipleNegativesRankingLoss
base_model: google/embeddinggemma-300m
widget:
- source_sentence: What does the Competition Bureau do?
sentences:
- What are the requirements for obtaining a Canadian passport?
- The Competition Bureau is an independent law enforcement agency that protects
and promotes competition for the benefit of Canadian consumers and businesses.
- Failure to file an annual or interim management’s discussion and analysis (MD&A)
or an annual or interim management report of fund performance (MRFP) is a common
failure.
- source_sentence: What does this website provide information about?
sentences:
- What are the eligibility requirements for employment insurance benefits?
- Register yourself and/or your whole family with Health Care Connect and a care
connector will search for a doctor or nurse practitioner who is accepting new
patients in your community.
- This website provides information about pension plans under provincial and federal
pension standards legislation.
- source_sentence: What impact did the Skills Canada competitions have on young people?
sentences:
- 'This includes records relating to: employee supervision, leave and time reporting,
job description preparation, job classification requests, staffing and recruitment,
employer-employee relations, ministry recognition programs, occupational safety
and health activities, and ministry training course development and delivery.'
- What are the eligibility requirements for the Canada Pension Plan?
- It meant a lot for the kids, especially those who had parents who were indifferent
to the trades.
- source_sentence: What game animals can John Arseneault guide hunters for?
sentences:
- What are the eligibility requirements for the New Brunswick childcare benefit?
- Our $70 billion National Housing Strategy is helping build affordable housing
supply, including rental housing, across Canada.
- John Arseneault offers hunting services for Atlantic salmon, trout, and bass.
- source_sentence: How can I find information about past Access to Information requests?
sentences:
- This house style was a popular design from 1890-1900.
- What are the eligibility requirements for the Canada Pension Plan?
- Search the summaries of completed Access to Information (ATI) requests to find
information about ATI requests made to the Government of Canada after January
2020.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on google/embeddinggemma-300m
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m). 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:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 -->
- **Maximum Sequence Length:** 2048 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
(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})
(2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
(3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
(4): 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("Neelkumar/my-embedding-gemma-5424")
# Run inference
queries = [
"How can I find information about past Access to Information requests?",
]
documents = [
'Search the summaries of completed Access to Information (ATI) requests to find information about ATI requests made to the Government of Canada after January 2020.',
'What are the eligibility requirements for the Canada Pension Plan?',
'This house style was a popular design from 1890-1900.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.9569, 0.1398, -0.0558]])
```
<!--
### 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.*
-->
<!--
## 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
#### Unnamed Dataset
* Size: 5,424 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: 6 tokens</li><li>mean: 15.8 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 32.04 tokens</li><li>max: 130 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 15.01 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| <code>Quelles mesures les propriétaires peuvent-ils prendre pour éliminer les punaises de lit?</code> | <code>Les propriétaires peuvent instaurer différentes mesures pour prévenir et éliminer les punaises des lits.</code> | <code>Quelles sont les conditions pour obtenir une assurance automobile?</code> |
| <code>Comment les pages web du gouvernement de la Saskatchewan sont-elles traduites en français?</code> | <code>Un certain nombre de pages sur le site web du gouvernement de la Saskatchewan ont été traduites professionnellement en français.</code> | <code>Quelles sont les exigences pour obtenir un permis de conduire?</code> |
| <code>How long do plant breeders' rights last in Canada?</code> | <code>Plant breeders receive legal protection for up to 25 years for trees and vines, and 20 years for other plant varieties.</code> | <code>What are the requirements for importing a pet into Canada?</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",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 4
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `prompts`: task: sentence similarity | query:
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_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`: 10
- `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`: 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}
- `parallelism_config`: 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`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `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
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: task: sentence similarity | query:
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0147 | 20 | 0.1138 |
| 0.0295 | 40 | 0.0682 |
| 0.0442 | 60 | 0.0099 |
| 0.0590 | 80 | 0.0212 |
| 0.0737 | 100 | 0.0447 |
| 0.0885 | 120 | 0.0047 |
| 0.1032 | 140 | 0.0057 |
| 0.1180 | 160 | 0.0025 |
| 0.1327 | 180 | 0.0036 |
| 0.1475 | 200 | 0.0062 |
| 0.1622 | 220 | 0.0285 |
| 0.1770 | 240 | 0.0069 |
| 0.1917 | 260 | 0.0008 |
| 0.2065 | 280 | 0.0104 |
| 0.2212 | 300 | 0.0019 |
| 0.2360 | 320 | 0.0576 |
| 0.2507 | 340 | 0.0088 |
| 0.2655 | 360 | 0.0046 |
| 0.2802 | 380 | 0.0014 |
| 0.2950 | 400 | 0.001 |
| 0.3097 | 420 | 0.0184 |
| 0.3245 | 440 | 0.0016 |
| 0.3392 | 460 | 0.0019 |
| 0.3540 | 480 | 0.0192 |
| 0.3687 | 500 | 0.0392 |
| 0.3835 | 520 | 0.0051 |
| 0.3982 | 540 | 0.0023 |
| 0.4130 | 560 | 0.0119 |
| 0.4277 | 580 | 0.0022 |
| 0.4425 | 600 | 0.0046 |
| 0.4572 | 620 | 0.0041 |
| 0.4720 | 640 | 0.0066 |
| 0.4867 | 660 | 0.0115 |
| 0.5015 | 680 | 0.0112 |
| 0.5162 | 700 | 0.0327 |
| 0.5310 | 720 | 0.0009 |
| 0.5457 | 740 | 0.0031 |
| 0.5605 | 760 | 0.0007 |
| 0.5752 | 780 | 0.0367 |
| 0.5900 | 800 | 0.0344 |
| 0.6047 | 820 | 0.0027 |
| 0.6195 | 840 | 0.0105 |
| 0.6342 | 860 | 0.0597 |
| 0.6490 | 880 | 0.0594 |
| 0.6637 | 900 | 0.0022 |
| 0.6785 | 920 | 0.0177 |
| 0.6932 | 940 | 0.0041 |
| 0.7080 | 960 | 0.0123 |
| 0.7227 | 980 | 0.0988 |
| 0.7375 | 1000 | 0.0248 |
| 0.7522 | 1020 | 0.0021 |
| 0.7670 | 1040 | 0.0376 |
| 0.7817 | 1060 | 0.0216 |
| 0.7965 | 1080 | 0.0779 |
| 0.8112 | 1100 | 0.0317 |
| 0.8260 | 1120 | 0.0233 |
| 0.8407 | 1140 | 0.0201 |
| 0.8555 | 1160 | 0.1391 |
| 0.8702 | 1180 | 0.0846 |
| 0.8850 | 1200 | 0.0064 |
| 0.8997 | 1220 | 0.1509 |
| 0.9145 | 1240 | 0.0196 |
| 0.9292 | 1260 | 0.0198 |
| 0.9440 | 1280 | 0.0174 |
| 0.9587 | 1300 | 0.117 |
| 0.9735 | 1320 | 0.0741 |
| 0.9882 | 1340 | 0.3282 |
| 1.0029 | 1360 | 0.0314 |
| 1.0177 | 1380 | 0.1522 |
| 1.0324 | 1400 | 0.0378 |
| 1.0472 | 1420 | 0.025 |
| 1.0619 | 1440 | 0.0442 |
| 1.0767 | 1460 | 0.0314 |
| 1.0914 | 1480 | 0.0745 |
| 1.1062 | 1500 | 0.0272 |
| 1.1209 | 1520 | 0.1248 |
| 1.1357 | 1540 | 0.299 |
| 1.1504 | 1560 | 0.0123 |
| 1.1652 | 1580 | 0.0245 |
| 1.1799 | 1600 | 0.0153 |
| 1.1947 | 1620 | 0.0171 |
| 1.2094 | 1640 | 0.0146 |
| 1.2242 | 1660 | 0.0313 |
| 1.2389 | 1680 | 0.0317 |
| 1.2537 | 1700 | 0.084 |
| 1.2684 | 1720 | 0.0569 |
| 1.2832 | 1740 | 0.1958 |
| 1.2979 | 1760 | 0.09 |
| 1.3127 | 1780 | 0.0526 |
| 1.3274 | 1800 | 0.0956 |
| 1.3422 | 1820 | 0.1601 |
| 1.3569 | 1840 | 0.156 |
| 1.3717 | 1860 | 0.0296 |
| 1.3864 | 1880 | 0.0391 |
| 1.4012 | 1900 | 0.0816 |
| 1.4159 | 1920 | 0.1262 |
| 1.4307 | 1940 | 0.1375 |
| 1.4454 | 1960 | 0.3373 |
| 1.4602 | 1980 | 0.094 |
| 1.4749 | 2000 | 0.0875 |
| 1.4897 | 2020 | 0.1161 |
| 1.5044 | 2040 | 0.1739 |
| 1.5192 | 2060 | 0.0526 |
| 1.5339 | 2080 | 0.1364 |
| 1.5487 | 2100 | 0.0508 |
| 1.5634 | 2120 | 0.0614 |
| 1.5782 | 2140 | 0.0589 |
| 1.5929 | 2160 | 0.0593 |
| 1.6077 | 2180 | 0.0078 |
| 1.6224 | 2200 | 0.2009 |
| 1.6372 | 2220 | 0.1356 |
| 1.6519 | 2240 | 0.1268 |
| 1.6667 | 2260 | 0.0257 |
| 1.6814 | 2280 | 0.0679 |
| 1.6962 | 2300 | 0.0229 |
| 1.7109 | 2320 | 0.1467 |
| 1.7257 | 2340 | 0.1239 |
| 1.7404 | 2360 | 0.0138 |
| 1.7552 | 2380 | 0.0997 |
| 1.7699 | 2400 | 0.0197 |
| 1.7847 | 2420 | 0.0358 |
| 1.7994 | 2440 | 0.0368 |
| 1.8142 | 2460 | 0.0755 |
| 1.8289 | 2480 | 0.1305 |
| 1.8437 | 2500 | 0.0164 |
| 1.8584 | 2520 | 0.1273 |
| 1.8732 | 2540 | 0.0255 |
| 1.8879 | 2560 | 0.0547 |
| 1.9027 | 2580 | 0.0494 |
| 1.9174 | 2600 | 0.1257 |
| 1.9322 | 2620 | 0.0434 |
| 1.9469 | 2640 | 0.0358 |
| 1.9617 | 2660 | 0.1272 |
| 1.9764 | 2680 | 0.022 |
| 1.9912 | 2700 | 0.054 |
| 2.0059 | 2720 | 0.0281 |
| 2.0206 | 2740 | 0.0229 |
| 2.0354 | 2760 | 0.0117 |
| 2.0501 | 2780 | 0.0242 |
| 2.0649 | 2800 | 0.0819 |
| 2.0796 | 2820 | 0.0625 |
| 2.0944 | 2840 | 0.0622 |
| 2.1091 | 2860 | 0.0316 |
| 2.1239 | 2880 | 0.2254 |
| 2.1386 | 2900 | 0.0857 |
| 2.1534 | 2920 | 0.026 |
| 2.1681 | 2940 | 0.0023 |
| 2.1829 | 2960 | 0.0053 |
| 2.1976 | 2980 | 0.004 |
| 2.2124 | 3000 | 0.0087 |
| 2.2271 | 3020 | 0.0068 |
| 2.2419 | 3040 | 0.0207 |
| 2.2566 | 3060 | 0.0522 |
| 2.2714 | 3080 | 0.005 |
| 2.2861 | 3100 | 0.038 |
| 2.3009 | 3120 | 0.0059 |
| 2.3156 | 3140 | 0.035 |
| 2.3304 | 3160 | 0.0603 |
| 2.3451 | 3180 | 0.0209 |
| 2.3599 | 3200 | 0.0103 |
| 2.3746 | 3220 | 0.0109 |
| 2.3894 | 3240 | 0.0755 |
| 2.4041 | 3260 | 0.0021 |
| 2.4189 | 3280 | 0.1019 |
| 2.4336 | 3300 | 0.1014 |
| 2.4484 | 3320 | 0.0198 |
| 2.4631 | 3340 | 0.0205 |
| 2.4779 | 3360 | 0.0431 |
| 2.4926 | 3380 | 0.1268 |
| 2.5074 | 3400 | 0.0097 |
| 2.5221 | 3420 | 0.0035 |
| 2.5369 | 3440 | 0.0292 |
| 2.5516 | 3460 | 0.0175 |
| 2.5664 | 3480 | 0.0687 |
| 2.5811 | 3500 | 0.021 |
| 2.5959 | 3520 | 0.0438 |
| 2.6106 | 3540 | 0.0024 |
| 2.6254 | 3560 | 0.0029 |
| 2.6401 | 3580 | 0.0267 |
| 2.6549 | 3600 | 0.0288 |
| 2.6696 | 3620 | 0.0058 |
| 2.6844 | 3640 | 0.0634 |
| 2.6991 | 3660 | 0.0404 |
| 2.7139 | 3680 | 0.0253 |
| 2.7286 | 3700 | 0.0127 |
| 2.7434 | 3720 | 0.0786 |
| 2.7581 | 3740 | 0.0739 |
| 2.7729 | 3760 | 0.0348 |
| 2.7876 | 3780 | 0.0697 |
| 2.8024 | 3800 | 0.0143 |
| 2.8171 | 3820 | 0.015 |
| 2.8319 | 3840 | 0.0139 |
| 2.8466 | 3860 | 0.023 |
| 2.8614 | 3880 | 0.0625 |
| 2.8761 | 3900 | 0.01 |
| 2.8909 | 3920 | 0.0656 |
| 2.9056 | 3940 | 0.0435 |
| 2.9204 | 3960 | 0.0367 |
| 2.9351 | 3980 | 0.0482 |
| 2.9499 | 4000 | 0.0557 |
| 2.9646 | 4020 | 0.1046 |
| 2.9794 | 4040 | 0.0578 |
| 2.9941 | 4060 | 0.0793 |
| 3.0088 | 4080 | 0.0053 |
| 3.0236 | 4100 | 0.0035 |
| 3.0383 | 4120 | 0.0095 |
| 3.0531 | 4140 | 0.001 |
| 3.0678 | 4160 | 0.0368 |
| 3.0826 | 4180 | 0.0251 |
| 3.0973 | 4200 | 0.0084 |
| 3.1121 | 4220 | 0.0224 |
| 3.1268 | 4240 | 0.0407 |
| 3.1416 | 4260 | 0.0611 |
| 3.1563 | 4280 | 0.0226 |
| 3.1711 | 4300 | 0.0092 |
| 3.1858 | 4320 | 0.0052 |
| 3.2006 | 4340 | 0.0578 |
| 3.2153 | 4360 | 0.0259 |
| 3.2301 | 4380 | 0.0002 |
| 3.2448 | 4400 | 0.0787 |
| 3.2596 | 4420 | 0.0194 |
| 3.2743 | 4440 | 0.0002 |
| 3.2891 | 4460 | 0.0006 |
| 3.3038 | 4480 | 0.0188 |
| 3.3186 | 4500 | 0.0722 |
| 3.3333 | 4520 | 0.0621 |
| 3.3481 | 4540 | 0.0017 |
| 3.3628 | 4560 | 0.1242 |
| 3.3776 | 4580 | 0.0057 |
| 3.3923 | 4600 | 0.0064 |
| 3.4071 | 4620 | 0.0016 |
| 3.4218 | 4640 | 0.0007 |
| 3.4366 | 4660 | 0.1187 |
| 3.4513 | 4680 | 0.0529 |
| 3.4661 | 4700 | 0.0294 |
| 3.4808 | 4720 | 0.1213 |
| 3.4956 | 4740 | 0.0221 |
| 3.5103 | 4760 | 0.0234 |
| 3.5251 | 4780 | 0.0034 |
| 3.5398 | 4800 | 0.0107 |
| 3.5546 | 4820 | 0.012 |
| 3.5693 | 4840 | 0.0351 |
| 3.5841 | 4860 | 0.0099 |
| 3.5988 | 4880 | 0.002 |
| 3.6136 | 4900 | 0.0024 |
| 3.6283 | 4920 | 0.0321 |
| 3.6431 | 4940 | 0.0008 |
| 3.6578 | 4960 | 0.038 |
| 3.6726 | 4980 | 0.0944 |
| 3.6873 | 5000 | 0.0227 |
| 3.7021 | 5020 | 0.0088 |
| 3.7168 | 5040 | 0.0573 |
| 3.7316 | 5060 | 0.2029 |
| 3.7463 | 5080 | 0.0522 |
| 3.7611 | 5100 | 0.012 |
| 3.7758 | 5120 | 0.0044 |
| 3.7906 | 5140 | 0.0178 |
| 3.8053 | 5160 | 0.0032 |
| 3.8201 | 5180 | 0.0375 |
| 3.8348 | 5200 | 0.0322 |
| 3.8496 | 5220 | 0.0066 |
| 3.8643 | 5240 | 0.0108 |
| 3.8791 | 5260 | 0.0143 |
| 3.8938 | 5280 | 0.0271 |
| 3.9086 | 5300 | 0.003 |
| 3.9233 | 5320 | 0.0183 |
| 3.9381 | 5340 | 0.0307 |
| 3.9528 | 5360 | 0.0026 |
| 3.9676 | 5380 | 0.0031 |
| 3.9823 | 5400 | 0.0011 |
| 3.9971 | 5420 | 0.0749 |
| 4.0118 | 5440 | 0.0192 |
| 4.0265 | 5460 | 0.037 |
| 4.0413 | 5480 | 0.0017 |
| 4.0560 | 5500 | 0.0013 |
| 4.0708 | 5520 | 0.0246 |
| 4.0855 | 5540 | 0.0007 |
| 4.1003 | 5560 | 0.045 |
| 4.1150 | 5580 | 0.038 |
| 4.1298 | 5600 | 0.0179 |
| 4.1445 | 5620 | 0.021 |
| 4.1593 | 5640 | 0.0012 |
| 4.1740 | 5660 | 0.0001 |
| 4.1888 | 5680 | 0.0004 |
| 4.2035 | 5700 | 0.0001 |
| 4.2183 | 5720 | 0.0021 |
| 4.2330 | 5740 | 0.0279 |
| 4.2478 | 5760 | 0.0044 |
| 4.2625 | 5780 | 0.0063 |
| 4.2773 | 5800 | 0.0046 |
| 4.2920 | 5820 | 0.0692 |
| 4.3068 | 5840 | 0.0007 |
| 4.3215 | 5860 | 0.0053 |
| 4.3363 | 5880 | 0.0288 |
| 4.3510 | 5900 | 0.0197 |
| 4.3658 | 5920 | 0.0007 |
| 4.3805 | 5940 | 0.002 |
| 4.3953 | 5960 | 0.0059 |
| 4.4100 | 5980 | 0.0258 |
| 4.4248 | 6000 | 0.001 |
| 4.4395 | 6020 | 0.0017 |
| 4.4543 | 6040 | 0.0024 |
| 4.4690 | 6060 | 0.0748 |
| 4.4838 | 6080 | 0.002 |
| 4.4985 | 6100 | 0.0498 |
| 4.5133 | 6120 | 0.0016 |
| 4.5280 | 6140 | 0.0018 |
| 4.5428 | 6160 | 0.0022 |
| 4.5575 | 6180 | 0.0012 |
| 4.5723 | 6200 | 0.009 |
| 4.5870 | 6220 | 0.0659 |
| 4.6018 | 6240 | 0.0121 |
| 4.6165 | 6260 | 0.0294 |
| 4.6313 | 6280 | 0.0002 |
| 4.6460 | 6300 | 0.0184 |
| 4.6608 | 6320 | 0.0158 |
| 4.6755 | 6340 | 0.0104 |
| 4.6903 | 6360 | 0.0498 |
| 4.7050 | 6380 | 0.0061 |
| 4.7198 | 6400 | 0.0305 |
| 4.7345 | 6420 | 0.0427 |
| 4.7493 | 6440 | 0.0004 |
| 4.7640 | 6460 | 0.0009 |
| 4.7788 | 6480 | 0.0001 |
| 4.7935 | 6500 | 0.0261 |
| 4.8083 | 6520 | 0.0019 |
| 4.8230 | 6540 | 0.0024 |
| 4.8378 | 6560 | 0.0228 |
| 4.8525 | 6580 | 0.0002 |
| 4.8673 | 6600 | 0.002 |
| 4.8820 | 6620 | 0.0005 |
| 4.8968 | 6640 | 0.0082 |
| 4.9115 | 6660 | 0.0119 |
| 4.9263 | 6680 | 0.0175 |
| 4.9410 | 6700 | 0.0011 |
| 4.9558 | 6720 | 0.0021 |
| 4.9705 | 6740 | 0.0106 |
| 4.9853 | 6760 | 0.018 |
| 5.0 | 6780 | 0.019 |
| 5.0147 | 6800 | 0.0629 |
| 5.0295 | 6820 | 0.0076 |
| 5.0442 | 6840 | 0.0004 |
| 5.0590 | 6860 | 0.0014 |
| 5.0737 | 6880 | 0.0012 |
| 5.0885 | 6900 | 0.0021 |
| 5.1032 | 6920 | 0.0032 |
| 5.1180 | 6940 | 0.0275 |
| 5.1327 | 6960 | 0.019 |
| 5.1475 | 6980 | 0.0006 |
| 5.1622 | 7000 | 0.0006 |
| 5.1770 | 7020 | 0.0049 |
| 5.1917 | 7040 | 0.0359 |
| 5.2065 | 7060 | 0.0028 |
| 5.2212 | 7080 | 0.0012 |
| 5.2360 | 7100 | 0.0138 |
| 5.2507 | 7120 | 0.0042 |
| 5.2655 | 7140 | 0.0003 |
| 5.2802 | 7160 | 0.0056 |
| 5.2950 | 7180 | 0.0329 |
| 5.3097 | 7200 | 0.0016 |
| 5.3245 | 7220 | 0.0092 |
| 5.3392 | 7240 | 0.0002 |
| 5.3540 | 7260 | 0.0211 |
| 5.3687 | 7280 | 0.019 |
| 5.3835 | 7300 | 0.0012 |
| 5.3982 | 7320 | 0.0002 |
| 5.4130 | 7340 | 0.0002 |
| 5.4277 | 7360 | 0.0143 |
| 5.4425 | 7380 | 0.0004 |
| 5.4572 | 7400 | 0.0004 |
| 5.4720 | 7420 | 0.0068 |
| 5.4867 | 7440 | 0.0201 |
| 5.5015 | 7460 | 0.0003 |
| 5.5162 | 7480 | 0.0042 |
| 5.5310 | 7500 | 0.0007 |
| 5.5457 | 7520 | 0.0664 |
| 5.5605 | 7540 | 0.0014 |
| 5.5752 | 7560 | 0.0175 |
| 5.5900 | 7580 | 0.0362 |
| 5.6047 | 7600 | 0.0225 |
| 5.6195 | 7620 | 0.0003 |
| 5.6342 | 7640 | 0.0025 |
| 5.6490 | 7660 | 0.0128 |
| 5.6637 | 7680 | 0.0013 |
| 5.6785 | 7700 | 0.0042 |
| 5.6932 | 7720 | 0.0012 |
| 5.7080 | 7740 | 0.0017 |
| 5.7227 | 7760 | 0.0039 |
| 5.7375 | 7780 | 0.0013 |
| 5.7522 | 7800 | 0.0008 |
| 5.7670 | 7820 | 0.006 |
| 5.7817 | 7840 | 0.0177 |
| 5.7965 | 7860 | 0.0189 |
| 5.8112 | 7880 | 0.0015 |
| 5.8260 | 7900 | 0.0003 |
| 5.8407 | 7920 | 0.001 |
| 5.8555 | 7940 | 0.0269 |
| 5.8702 | 7960 | 0.0006 |
| 5.8850 | 7980 | 0.0176 |
| 5.8997 | 8000 | 0.0048 |
| 5.9145 | 8020 | 0.0031 |
| 5.9292 | 8040 | 0.0056 |
| 5.9440 | 8060 | 0.0015 |
| 5.9587 | 8080 | 0.0102 |
| 5.9735 | 8100 | 0.0047 |
| 5.9882 | 8120 | 0.0339 |
| 6.0029 | 8140 | 0.0027 |
| 6.0177 | 8160 | 0.0008 |
| 6.0324 | 8180 | 0.0014 |
| 6.0472 | 8200 | 0.0001 |
| 6.0619 | 8220 | 0.0183 |
| 6.0767 | 8240 | 0.0142 |
| 6.0914 | 8260 | 0.0004 |
| 6.1062 | 8280 | 0.0392 |
| 6.1209 | 8300 | 0.0016 |
| 6.1357 | 8320 | 0.0025 |
| 6.1504 | 8340 | 0.0017 |
| 6.1652 | 8360 | 0.018 |
| 6.1799 | 8380 | 0.0031 |
| 6.1947 | 8400 | 0.0021 |
| 6.2094 | 8420 | 0.0244 |
| 6.2242 | 8440 | 0.0263 |
| 6.2389 | 8460 | 0.0183 |
| 6.2537 | 8480 | 0.0367 |
| 6.2684 | 8500 | 0.0009 |
| 6.2832 | 8520 | 0.0 |
| 6.2979 | 8540 | 0.0001 |
| 6.3127 | 8560 | 0.0011 |
| 6.3274 | 8580 | 0.0007 |
| 6.3422 | 8600 | 0.0004 |
| 6.3569 | 8620 | 0.0044 |
| 6.3717 | 8640 | 0.0174 |
| 6.3864 | 8660 | 0.0002 |
| 6.4012 | 8680 | 0.0176 |
| 6.4159 | 8700 | 0.0341 |
| 6.4307 | 8720 | 0.0015 |
| 6.4454 | 8740 | 0.0002 |
| 6.4602 | 8760 | 0.0043 |
| 6.4749 | 8780 | 0.0036 |
| 6.4897 | 8800 | 0.0001 |
| 6.5044 | 8820 | 0.0004 |
| 6.5192 | 8840 | 0.0474 |
| 6.5339 | 8860 | 0.0001 |
| 6.5487 | 8880 | 0.0003 |
| 6.5634 | 8900 | 0.0021 |
| 6.5782 | 8920 | 0.0014 |
| 6.5929 | 8940 | 0.0004 |
| 6.6077 | 8960 | 0.0176 |
| 6.6224 | 8980 | 0.0001 |
| 6.6372 | 9000 | 0.0009 |
| 6.6519 | 9020 | 0.0015 |
| 6.6667 | 9040 | 0.0003 |
| 6.6814 | 9060 | 0.0001 |
| 6.6962 | 9080 | 0.0016 |
| 6.7109 | 9100 | 0.0182 |
| 6.7257 | 9120 | 0.0002 |
| 6.7404 | 9140 | 0.0009 |
| 6.7552 | 9160 | 0.0018 |
| 6.7699 | 9180 | 0.0182 |
| 6.7847 | 9200 | 0.0 |
| 6.7994 | 9220 | 0.0206 |
| 6.8142 | 9240 | 0.0001 |
| 6.8289 | 9260 | 0.0002 |
| 6.8437 | 9280 | 0.0037 |
| 6.8584 | 9300 | 0.0238 |
| 6.8732 | 9320 | 0.0002 |
| 6.8879 | 9340 | 0.0 |
| 6.9027 | 9360 | 0.0002 |
| 6.9174 | 9380 | 0.019 |
| 6.9322 | 9400 | 0.0059 |
| 6.9469 | 9420 | 0.0002 |
| 6.9617 | 9440 | 0.0001 |
| 6.9764 | 9460 | 0.0004 |
| 6.9912 | 9480 | 0.0023 |
| 7.0059 | 9500 | 0.0006 |
| 7.0206 | 9520 | 0.0019 |
| 7.0354 | 9540 | 0.0176 |
| 7.0501 | 9560 | 0.0026 |
| 7.0649 | 9580 | 0.0014 |
| 7.0796 | 9600 | 0.0003 |
| 7.0944 | 9620 | 0.0001 |
| 7.1091 | 9640 | 0.0002 |
| 7.1239 | 9660 | 0.0362 |
| 7.1386 | 9680 | 0.001 |
| 7.1534 | 9700 | 0.0001 |
| 7.1681 | 9720 | 0.0002 |
| 7.1829 | 9740 | 0.0029 |
| 7.1976 | 9760 | 0.0002 |
| 7.2124 | 9780 | 0.0003 |
| 7.2271 | 9800 | 0.0027 |
| 7.2419 | 9820 | 0.0001 |
| 7.2566 | 9840 | 0.0001 |
| 7.2714 | 9860 | 0.0002 |
| 7.2861 | 9880 | 0.0124 |
| 7.3009 | 9900 | 0.0361 |
| 7.3156 | 9920 | 0.0039 |
| 7.3304 | 9940 | 0.0 |
| 7.3451 | 9960 | 0.0 |
| 7.3599 | 9980 | 0.0008 |
| 7.3746 | 10000 | 0.0002 |
| 7.3894 | 10020 | 0.0003 |
| 7.4041 | 10040 | 0.0001 |
| 7.4189 | 10060 | 0.0174 |
| 7.4336 | 10080 | 0.0015 |
| 7.4484 | 10100 | 0.0152 |
| 7.4631 | 10120 | 0.0351 |
| 7.4779 | 10140 | 0.0007 |
| 7.4926 | 10160 | 0.0005 |
| 7.5074 | 10180 | 0.0005 |
| 7.5221 | 10200 | 0.0001 |
| 7.5369 | 10220 | 0.0002 |
| 7.5516 | 10240 | 0.0001 |
| 7.5664 | 10260 | 0.001 |
| 7.5811 | 10280 | 0.0057 |
| 7.5959 | 10300 | 0.0012 |
| 7.6106 | 10320 | 0.0001 |
| 7.6254 | 10340 | 0.0005 |
| 7.6401 | 10360 | 0.0016 |
| 7.6549 | 10380 | 0.0072 |
| 7.6696 | 10400 | 0.0007 |
| 7.6844 | 10420 | 0.0001 |
| 7.6991 | 10440 | 0.0002 |
| 7.7139 | 10460 | 0.0036 |
| 7.7286 | 10480 | 0.0001 |
| 7.7434 | 10500 | 0.0002 |
| 7.7581 | 10520 | 0.0001 |
| 7.7729 | 10540 | 0.0001 |
| 7.7876 | 10560 | 0.0007 |
| 7.8024 | 10580 | 0.0002 |
| 7.8171 | 10600 | 0.0001 |
| 7.8319 | 10620 | 0.018 |
| 7.8466 | 10640 | 0.0882 |
| 7.8614 | 10660 | 0.0006 |
| 7.8761 | 10680 | 0.0001 |
| 7.8909 | 10700 | 0.0001 |
| 7.9056 | 10720 | 0.0001 |
| 7.9204 | 10740 | 0.0176 |
| 7.9351 | 10760 | 0.0002 |
| 7.9499 | 10780 | 0.0231 |
| 7.9646 | 10800 | 0.0002 |
| 7.9794 | 10820 | 0.0002 |
| 7.9941 | 10840 | 0.0 |
| 8.0088 | 10860 | 0.0001 |
| 8.0236 | 10880 | 0.0001 |
| 8.0383 | 10900 | 0.0003 |
| 8.0531 | 10920 | 0.0172 |
| 8.0678 | 10940 | 0.0002 |
| 8.0826 | 10960 | 0.018 |
| 8.0973 | 10980 | 0.0174 |
| 8.1121 | 11000 | 0.0001 |
| 8.1268 | 11020 | 0.0174 |
| 8.1416 | 11040 | 0.0 |
| 8.1563 | 11060 | 0.0039 |
| 8.1711 | 11080 | 0.0001 |
| 8.1858 | 11100 | 0.0 |
| 8.2006 | 11120 | 0.002 |
| 8.2153 | 11140 | 0.0176 |
| 8.2301 | 11160 | 0.0022 |
| 8.2448 | 11180 | 0.0001 |
| 8.2596 | 11200 | 0.0 |
| 8.2743 | 11220 | 0.0027 |
| 8.2891 | 11240 | 0.0198 |
| 8.3038 | 11260 | 0.0 |
| 8.3186 | 11280 | 0.0003 |
| 8.3333 | 11300 | 0.0223 |
| 8.3481 | 11320 | 0.0092 |
| 8.3628 | 11340 | 0.0001 |
| 8.3776 | 11360 | 0.0009 |
| 8.3923 | 11380 | 0.0014 |
| 8.4071 | 11400 | 0.0006 |
| 8.4218 | 11420 | 0.0006 |
| 8.4366 | 11440 | 0.0006 |
| 8.4513 | 11460 | 0.0005 |
| 8.4661 | 11480 | 0.0192 |
| 8.4808 | 11500 | 0.0347 |
| 8.4956 | 11520 | 0.0009 |
| 8.5103 | 11540 | 0.0002 |
| 8.5251 | 11560 | 0.0 |
| 8.5398 | 11580 | 0.0 |
| 8.5546 | 11600 | 0.0002 |
| 8.5693 | 11620 | 0.0174 |
| 8.5841 | 11640 | 0.0001 |
| 8.5988 | 11660 | 0.0171 |
| 8.6136 | 11680 | 0.0001 |
| 8.6283 | 11700 | 0.0001 |
| 8.6431 | 11720 | 0.0428 |
| 8.6578 | 11740 | 0.0003 |
| 8.6726 | 11760 | 0.0 |
| 8.6873 | 11780 | 0.0001 |
| 8.7021 | 11800 | 0.0176 |
| 8.7168 | 11820 | 0.0358 |
| 8.7316 | 11840 | 0.0002 |
| 8.7463 | 11860 | 0.0002 |
| 8.7611 | 11880 | 0.0001 |
| 8.7758 | 11900 | 0.0002 |
| 8.7906 | 11920 | 0.0015 |
| 8.8053 | 11940 | 0.0001 |
| 8.8201 | 11960 | 0.0001 |
| 8.8348 | 11980 | 0.0112 |
| 8.8496 | 12000 | 0.0033 |
| 8.8643 | 12020 | 0.0001 |
| 8.8791 | 12040 | 0.001 |
| 8.8938 | 12060 | 0.0174 |
| 8.9086 | 12080 | 0.0001 |
| 8.9233 | 12100 | 0.0002 |
| 8.9381 | 12120 | 0.0001 |
| 8.9528 | 12140 | 0.0001 |
| 8.9676 | 12160 | 0.0222 |
| 8.9823 | 12180 | 0.0003 |
| 8.9971 | 12200 | 0.0001 |
| 9.0118 | 12220 | 0.0 |
| 9.0265 | 12240 | 0.0001 |
| 9.0413 | 12260 | 0.0182 |
| 9.0560 | 12280 | 0.0174 |
| 9.0708 | 12300 | 0.0 |
| 9.0855 | 12320 | 0.0 |
| 9.1003 | 12340 | 0.0023 |
| 9.1150 | 12360 | 0.0001 |
| 9.1298 | 12380 | 0.0248 |
| 9.1445 | 12400 | 0.0 |
| 9.1593 | 12420 | 0.0 |
| 9.1740 | 12440 | 0.0 |
| 9.1888 | 12460 | 0.0001 |
| 9.2035 | 12480 | 0.0087 |
| 9.2183 | 12500 | 0.0 |
| 9.2330 | 12520 | 0.0003 |
| 9.2478 | 12540 | 0.0174 |
| 9.2625 | 12560 | 0.0 |
| 9.2773 | 12580 | 0.0006 |
| 9.2920 | 12600 | 0.0001 |
| 9.3068 | 12620 | 0.0053 |
| 9.3215 | 12640 | 0.0 |
| 9.3363 | 12660 | 0.0174 |
| 9.3510 | 12680 | 0.0001 |
| 9.3658 | 12700 | 0.0002 |
| 9.3805 | 12720 | 0.0001 |
| 9.3953 | 12740 | 0.0001 |
| 9.4100 | 12760 | 0.0001 |
| 9.4248 | 12780 | 0.0002 |
| 9.4395 | 12800 | 0.0002 |
| 9.4543 | 12820 | 0.0023 |
| 9.4690 | 12840 | 0.0 |
| 9.4838 | 12860 | 0.0018 |
| 9.4985 | 12880 | 0.0028 |
| 9.5133 | 12900 | 0.0174 |
| 9.5280 | 12920 | 0.0001 |
| 9.5428 | 12940 | 0.0001 |
| 9.5575 | 12960 | 0.0174 |
| 9.5723 | 12980 | 0.0003 |
| 9.5870 | 13000 | 0.0 |
| 9.6018 | 13020 | 0.0174 |
| 9.6165 | 13040 | 0.0001 |
| 9.6313 | 13060 | 0.0 |
| 9.6460 | 13080 | 0.0001 |
| 9.6608 | 13100 | 0.0174 |
| 9.6755 | 13120 | 0.0173 |
| 9.6903 | 13140 | 0.0 |
| 9.7050 | 13160 | 0.0005 |
| 9.7198 | 13180 | 0.0001 |
| 9.7345 | 13200 | 0.0002 |
| 9.7493 | 13220 | 0.0 |
| 9.7640 | 13240 | 0.0001 |
| 9.7788 | 13260 | 0.0 |
| 9.7935 | 13280 | 0.0026 |
| 9.8083 | 13300 | 0.0003 |
| 9.8230 | 13320 | 0.0001 |
| 9.8378 | 13340 | 0.0174 |
| 9.8525 | 13360 | 0.0099 |
| 9.8673 | 13380 | 0.0002 |
| 9.8820 | 13400 | 0.0 |
| 9.8968 | 13420 | 0.0032 |
| 9.9115 | 13440 | 0.0177 |
| 9.9263 | 13460 | 0.0175 |
| 9.9410 | 13480 | 0.0176 |
| 9.9558 | 13500 | 0.0001 |
| 9.9705 | 13520 | 0.0 |
| 9.9853 | 13540 | 0.0011 |
| 10.0 | 13560 | 0.0174 |
</details>
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 5.1.1
- Transformers: 4.57.0.dev0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.8.1
- Datasets: 3.6.0
- Tokenizers: 0.22.1
## 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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