sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer loss:MultipleNegativesRankingLoss autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-large-v1.6
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- loss:MultipleNegativesRankingLoss
---


**osllm.ai  Models Highlights Program**

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# SentenceTransformer based on osllmai/bge-large_osllmai_V1.6

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [osllmai/bge-large_osllmai_V1.6](https://huggingface.co/osllmai/bge-large_osllmai_V1.6). 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:** [osllmai/bge-large_osllmai_V1.6](https://huggingface.co/osllmai/bge-large_osllmai_V1.6) <!-- at revision 79e7739b6ab944e86d6171e44d24c997fc1e0116 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 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': 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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Early this year, there was a buzz on Motorola inviting T-Mobile Moto X owners to take part in a soak test for possible future update. Motorola seemed skeptical in disclosing facts at that point of time but since Moto G was recently upgraded to Android 4.4.2; enthusiasts anticipated the same for T-Mobile Moto X. And it turned out to be true.\r\nNews Update\r\nThis T-Mobile version of Moto X is now receiving the upgrade which is a file size of 147.6 MB. The Android 4.4.2 is the latest version of KitKat that includes all the goodies from the earlier installments, plus a few additions. The good news is, Motorola has customized the whole package and made a few tweaks into the update. The Software Version bumped to 161.44.25 and the notable changes are listed as below:\r\n- It added substantial support for services like printing photos, Google Docs, Gmail messages and other such content via Wi-Fi, Bluetooth and hosted services such as HP ePrinters and Google Cloud Print.\r\n- It fixed all the bugs identified during the preliminary runs, including the ones that caused a few users to experience short battery life after upgrading to KitKat.\r\n- Another bug that caused delays in synchronizing email services like Microsoft Exchange was resolved, thus adding to the convenience of the user.\r\nThis is a noteworthy upgrade, considering the fact that bugs and errors were fixed. Mobile addicts across the world will rejoice, for they can experience the smartness of Android KitKat flawlessly in their devices. This is significant development in terms of update.\r\nThis variant is an unlocked GSM device so chances are, you can use it on networks of other service providers. In all probability, the update should not be affected and the installation should hardly take much time. The T-Mobile Moto X Android update is now available for manual download. It is accessible in the following sequential way:\r\n- Click on Settings\r\n- Click on About Phone\r\n- Click on System Updates\r\n- Click on Download\r\nRecommendations\r\nFor ensuring a successful installation, it is highly recommended to install this update with at least 50% battery and a strong connectivity; preferably Wi-Fi. Follow the notification message and select download-> once the download is over, select Install-> Once the installation is over, and the phone will automatically restart. This marks the completion of the installation process. The phone is now updated to 161.44.25 – This build is same as the soak test.\r\nThis upgrade is free in the carrier network and Motorola and Google has collaborated for a back up service for those in trouble. In case of distress, a user can contact them through the Moto X web interface and avail the service. There is still no news on other carrier variants of this update but we can safely hope that it will roll out very soon. Though the upgrade doesn’t appeal in terms of version number but it is definitely significant for users to live with the latest KitKat.',
    'What are some of the notable changes in the T-Mobile Moto X update?',
    'What are the common features of the Quince Hand Lotion as described by the reviewers?',
]
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.*
-->

<!--
## 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: 9,598 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
  |         | positive                                                                              | anchor                                                                            |
  |:--------|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                            |
  | details | <ul><li>min: 232 tokens</li><li>mean: 493.76 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 26.8 tokens</li><li>max: 67 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | anchor                                                                                               |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------|
  | <code>Caption: Tasmanian berry grower Nic Hansen showing Macau chef Antimo Merone around his property as part of export engagement activities.<br>THE RISE and rise of the Australian strawberry, raspberry and blackberry industries has seen the sectors redouble their international trade focus, with the release of a dedicated export plan to grow their global presence over the next 10 years.<br>Driven by significant grower input, the Berry Export Summary 2028 maps the sectors’ current position, where they want to be, high-opportunity markets and next steps.<br>Hort Innovation trade manager Jenny Van de Meeberg said the value and volume of raspberry and blackberry exports rose by 100 per cent between 2016 and 2017. She said the Australian strawberry industry experienced similar success with an almost 30 per cent rise in export volume and a 26 per cent rise in value to $32.6M over the same period.<br>“Australian berry sectors are in a firm position at the moment,” she said. “Production, adoption of protected ...</code>                               | <code>What is the Berry Export Summary 2028 and what is its purpose?</code>                          |
  | <code>RWSN Collaborations<br>Southern Africa Self-supply Study Review of Self-supply and its support services in African countries<br>A lady in Zimbabwe proudly shows off her onions - watered from her self-supply well<br>© 2015 André Olschewski • Skat<br>Project starts: 2015<br>Project finished: 2016<br>Collaborators & Partners:.<br>Project Description<br>UNICEF and Skat have collaborated on a).<br>Perspectives<br>Reach and benefits:<br>- Self-supply is practised by millions of rural households in Sub-Sahara Africa as well as in Europe, USA and other areas of the world.<br>- Benefits reported from having access to Self-supply water sources include convenience, less time spent for fetching water and access to more and better quality water. In some areas, Self-supply sources offer important added values such as water for productive use, income generation, family safety and improved food security.<br>- Sustainability of services from Self-supply is high as there is strong ownership by people investing in own sources.<br>- As Self-suppl...</code> | <code>What are some of the benefits reported from having access to Self-supply water sources?</code> |
  | <code>All Android applications categories<br>Description<br>Coolands for Twitter is a revolutionary twitter client. It has many unique features, gives you the best mobile twitter experience you never imagined before.<br>The first unique feature is Real-Time.<br>You can’t find any refresh button in this app, because you absolutely don’t need to. Every time you open it, you’ll get the latest tweets and while you’re reading, you’ll get incoming tweets in Real-Time. So if your friend mentioned you, you can reply instantly.<br>The second unique feature is Avatar Indicator.<br>Avatar Indicator is small avatars showed on the title bar to indicate that you’ve got new message/tweet/mention. Since it’s real-time, you’ll keep getting incoming tweets while you’re reading your older timeline, Avatar-Indicator will let you know who’s tweet you’ve just got, and decide whether to check it out right away.<br>The third unique feature is Direct Link<br>I think it is obviously the most intuitive and convenient way to open a link. When...</code>                   | <code>What are the unique features of the Coolands for Twitter app?</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

#### Unnamed Dataset

* Size: 500 evaluation samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 500 samples:
  |         | positive                                                                              | anchor                                                                             |
  |:--------|:--------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                             |
  | details | <ul><li>min: 247 tokens</li><li>mean: 492.12 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 27.28 tokens</li><li>max: 54 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | anchor                                                                                  |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|
  | <code>Perhaps Not such a Good Idea
  <br>I have found trying to run a blog is very time-consuming, and there are other calls on my time. I think it has been demonstrated that if enough people are unable to self-moderate, the nuggets of interest are swamped by the rubbish. Sadly I agree with Mark Frank's assessment. I had hoped more thread topics would be proposed nothing has been suggested by anyone for a while.
  <br>My personal view is that, considering DaveScot's generally perceived blog persona, I have to admit that he hasn't been (on this site) quite the unmitigated disaster predicted. John Davison, on the other hand has conformed perfectly to predictions, which is a shame, but his choice.
  <br>I am happy to let things run for a while, but would like to hear from anyone who has a suggestion for a thread topic. Post here or in the suggestions thread
  <br>23 comments:
  <br>How about an "ID: show me the research" thread?
  <br>OK Rich, put some meat on the bones and I'll paste it.
  <br>Of course I have. I have no respec...</code> | <code>What is the author's personal view on DaveScot's blog persona?</code>             |
  | <code>Age reduction Academic atmosphere Beef tendon bottom Straight buckle low-heel cowhide Lefu shoes Mary Jane shoes Spring and summer Women's shoes 0.73
  <br>ins Chaopai shoes Women's Shoes Academic atmosphere Versatile Graffiti Frenulum gym shoes Harajuku leisure time Hip hop jointly skate shoes
  <br>Air force one Men's shoes Low Gang summer skate shoes student Korean version Versatile leisure time gym shoes female Reflection Little white shoes
  <br>autumn Clover ozweego Daddy shoes Jackson Yi Same men and women Reflection motion Running shoes EE6999
  <br>Retro Britain Square head Frenulum Color matching motion Casual shoes 2021 new pattern Versatile Flat bottom Elastic band Little white shoes female
  <br>Thick bottom British style Small leather shoes Women's shoes 2021 new pattern Big square head Spring and Autumn Lefu Autumn shoes black Single shoes
  <br>U.S.A quality goods Jeffrey Campbell temperament crude high-heeled dollskill Buckles Low top shoes female widow
  <br>quality goods Clover ozweego Black Warrior D...</code> | <code>What type of shoes are mentioned as being suitable for both men and women?</code> |
  | <code>I just started a new blog on my ultralight gear. My gear list in all it's glory is located on: each item of gear, I'm writing an in-depth review for the item and how we have used it. Would love to get feedback and the site and our gear and/or comments from people on how we can fine tune.Currently my wifes pack is 7.5 lbs base weight, and mine is 10.5 lbs.Thanks!-Brett
  <br>Edited by brettmarl on 09/09/2006 15:59:48 MDT.
  <br>Brett, Your BLOG looks good.You should put the size of your items where their is one such as pants, shoes, jacket etc. There is a golf like "handicap' for anyone that wears larger then size medium or size 9.5 shoe. Sure.I think you might recheck some of your math. Not sure but some totals look low. Don't trust the posted weightof gear, weigh it yourself if you haven't.Why is your pack list so heavy?
  <br>I agree, nice looking blog. Bill is right on listing the sizes, other than that....looks great!
  <br>Brett - nice list, and nice format!(One small typo: it currently says "Cloudbu...</code>          | <code>What are the base weights of the blogger's and his wife's packs?</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`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 30
- `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`: 10
- `per_device_eval_batch_size`: 10
- `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`: 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`: 30
- `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`: None
- `hub_always_push`: False
- `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
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Step	| Training Loss	 | Validation Loss |
|----| --------------- | --------------- |
| 100 | No log	| 0.077932 |
| 200 | No log	| 0.060976 |
| 300 | No log	| 0.069869 |
| 400 | No log	| 0.049215 |
| 500 | 0.129900 | 0.050496 |
| 600 | 0.129900 | 0.054146 |
| 700 | 0.129900 | 0.058882 |
| 800 | 0.129900 | 0.059746 |
| 900 | 0.129900 | 0.063642 |
| 1000 | 0.102800 | 0.075839 |
| 1100 | 0.102800 | 0.062941 |
| 1200 | 0.102800 | 0.066200 |
| 1300 | 0.102800 | 0.090931 |
| 1400 | 0.102800 | 0.109871 |
| 1500 | 0.091000 | 0.107309 |
| 1600 | 0.091000 | 0.113213 |
| 1700 | 0.091000 | 0.098840 |
| 1800 | 0.091000 | 0.117776 |
| 1900 | 0.091000 | 0.099684 |
| 2000 | 0.114700 | 0.099308 |
| 2100 | 0.114700 | 0.095418 |
| 2200 | 0.114700 | 0.121110 |
| 2300 | 0.114700 | 0.103965 |
| 2400 | 0.114700 | 0.151238 |
| 2500 | 0.083200 | 0.169087 |
| 2600 | 0.083200 | 0.164672 |
| 2700 | 0.083200 | 0.196103 |
| 2800 | 0.083200 | 0.200677 |
| 2900 | 0.083200 | 0.145300 |
| 3000 | 0.115000 | 0.171855 |
| 3100 | 0.115000 | 0.192693 |
| 3200 | 0.115000 | 0.206315 |
| 3300 | 0.115000 | 0.154698 |
| 3400 | 0.115000 | 0.133345 |
| 3500 | 0.095400 | 0.163068 |
| 3600 | 0.095400 | 0.211607 |
| 3700 | 0.095400 | 0.181887 |
| 3800 | 0.095400 | 0.179725 |
| 3900 | 0.095400 | 0.164818 |
| 4000 | 0.082300 | 0.207933 |
| 4100 | 0.082300 | 0.197499 |
| 4200 | 0.082300 | 0.248613 |
| 4300 | 0.082300 | 0.177535 |
| 4400 | 0.082300 | 0.170462 |
| 4500 | 0.054900 | 0.229082 |
| 4600 | 0.054900 | 0.206981 |
| 4700 | 0.054900 | 0.205012 |
| 4800 | 0.054900 | 0.183472 |
| 4900 | 0.054900 | 0.174745 |
| 5000 | 0.051800 | 0.211212 |
| 5100 | 0.051800 | 0.202875 |
| 5200 | 0.051800 | 0.209989 |
| 5300 | 0.051800 | 0.217802 |
| 5400 | 0.051800 | 0.207275 |
| 5500 | 0.036300 | 0.166451 |
| 5600 | 0.036300 | 0.185850 |
| 5700 | 0.036300 | 0.241831 |
| 5800 | 0.036300 | 0.220541 |
| 5900 | 0.036300 | 0.174495 |
| 6000 | 0.030500 | 0.204093 |
| 6100 | 0.030500 | 0.230805 |
| 6200 | 0.030500 | 0.234512 |
| 6300 | 0.030500 | 0.180759 |
| 6400 | 0.030500 | 0.210566 |
| 6500 | 0.022100 | 0.207234 |
| 6600 | 0.022100 | 0.231294 |
| 6700 | 0.022100 | 0.217805 |
| 6800 | 0.022100 | 0.196253 |
| 6900 | 0.022100 | 0.232458 |
| 7000 | 0.019100 | 0.206829 |
| 7100 | 0.019100 | 0.278143 |
| 7200 | 0.019100 | 0.233788 |
| 7300 | 0.019100 | 0.207704 |
| 7400 | 0.019100 | 0.193656 |
| 7500 | 0.018500 | 0.221518 |
| 7600 | 0.018500 | 0.185376 |
| 7700 | 0.018500 | 0.212624 |
| 7800 | 0.018500 | 0.243344 |
| 7900 | 0.018500 | 0.249413 |
| 8000 | 0.015200 | 0.189084 |
| 8100 | 0.015200 | 0.215418 |
| 8200 | 0.015200 | 0.175985 |
| 8300 | 0.015200 | 0.192763 |
| 8400 | 0.015200 | 0.276208 |
| 8500 | 0.013100 | 0.219308 |
| 8600 | 0.013100 | 0.252156 |
| 8700 | 0.013100 | 0.257007 |
| 8800 | 0.013100 | 0.285778 |
| 8900 | 0.013100 | 0.244496 |
| 9000 | 0.010900 | 0.251443 |
| 9100 | 0.010900 | 0.254057 |
| 9200 | 0.010900 | 0.249420 |
| 9300 | 0.010900 | 0.259040 |
| 9400 | 0.010900 | 0.216327 |
| 9500 | 0.011700 | 0.227780 |
| 9600 | 0.011700 | 0.250833 |
| 9700 | 0.011700 | 0.278442 |
| 9800 | 0.011700 | 0.265544 |
| 9900 | 0.011700 | 0.272136 |
| 10000 | 010000| 0.208672 |
| 10100  0.010000| 0.218282 |
| 10200 | 0.010000| 0.173614 |
| 10300 | 0.010000| 0.239000 |
| 10400 | 0.010000| 0.211169 |
| 10500 | 0.007900| 0.223127 |
| 10600 | 0.007900| 0.193511 |
| 10700 | 0.007900| 0.224293 |
| 10800 | 0.007900| 0.276138 |
| 10900 | 0.007900| 0.212863 |
| 11000 | 0.007600| 0.168702 |
| 11100 | 0.007600| 0.244286 |
| 11200 | 0.007600| 0.242311 |
| 11300 | 0.007600| 0.292155 |
| 11400 | 0.007600| 0.224811 |
| 11500 | 0.007400| 0.212932 |
| 11600 | 0.007400| 0.232738 |
| 11700 | 0.007400| 0.237509 |
| 11800 | 0.007400| 0.269904 |
| 11900 | 0.007400| 0.247419 |
| 12000 | 0.005600| 0.227091 |
| 12100 | 0.005600| 0.240582 |
| 12200 | 0.005600| 0.288775 |
| 12300 | 0.005600| 0.317762 |
| 12400 | 0.005600| 0.289674 |
| 12500 | 0.006800| 0.269095 |
| 12600 | 0.006800| 0.280439 |
| 12700 | 0.006800| 0.270056 |
| 12800 | 0.006800| 0.286388 |
| 12900 | 0.006800| 0.310388 |
| 13000 | 0.005300| 0.295646 |
| 13100 | 0.005300| 0.247113 |
| 13200 | 0.005300| 0.253593 |
| 13300 | 0.005300| 0.237059 |
| 13400 | 0.005300| 0.235936 |
| 13500 | 0.004900| 0.246602 |
| 13600 | 0.004900| 0.270857 |
| 13700 | 0.004900| 0.237764 |
| 13800 | 0.004900| 0.225527 |
| 13900 | 0.004900| 0.257233 |
| 14000 | 0.003400| 0.242815 |
| 14100 | 0.003400| 0.285892 |
| 14200 | 0.003400| 0.239855 |
| 14300 | 0.003400| 0.265451 |
| 14400 | 0.003400| 0.243612 |
| 14500 | 0.002500| 0.225554 |
| 14600 | 0.002500| 0.236780 |
| 14700 | 0.002500| 0.245257 |
| 14800 | 0.002500| 0.238916 |
| 14900 | 0.002500| 0.258283 |
| 15000 | 0.002800| 0.215534 |
| 15100 | 0.002800| 0.187369 |
| 15200 | 0.002800| 0.234369 |
| 15300 | 0.002800| 0.213728 |
| 15400 | 0.002800| 0.281290 |
| 15500 | 0.003800| 0.241194 |
| 15600 | 0.003800| 0.273098 |
| 15700 | 0.003800| 0.266355 |
| 15800 | 0.003800| 0.276067 |
| 15900 | 0.003800| 0.256065 |
| 16000 | 0.002400| 0.317020 |
| 16100 | 0.002400| 0.230963 |
| 16200 | 0.002400| 0.280397 |
| 16300 | 0.002400| 0.267816 |
| 16400 | 0.002400| 0.278919 |
| 16500 | 0.002100| 0.242398 |
| 16600 | 0.002100| 0.260493 |
| 16700 | 0.002100| 0.306106 |
| 16800 | 0.002100| 0.262822 |
| 16900 | 0.002100| 0.249681 |
| 17000 | 0.003300| 0.271645 |
| 17100 | 0.003300| 0.269828 |
| 17200 | 0.003300| 0.267099 |
| 17300 | 0.003300| 0.249909 |
| 17400 | 0.003300| 0.291281 |
| 17500 | 0.003000| 0.268641 |
| 17600 | 0.003000| 0.256109 |
| 17700 | 0.003000| 0.275269 |
| 17800 | 0.003000| 0.259269 |
| 17900 | 0.003000| 0.269805 |
| 18000 | 0.002300| 0.221830 |
| 18100 | 0.002300| 0.245713 |
| 18200 | 0.002300| 0.234901 |
| 18300 | 0.002300| 0.279927 |
| 18400 | 0.002300| 0.295232 |
| 18500 | 0.001800| 0.233305 |
| 18600 | 0.001800| 0.261527 |
| 18700 | 0.001800| 0.268007 |
| 18800 | 0.001800| 0.219146 |
| 18900 | 0.001800| 0.263330 |
| 19000 | 0.001900| 0.227705 |
| 19100 | 0.001900| 0.261595 |
| 19200 | 0.001900| 0.238764 |
| 19300 | 0.001900| 0.229096 |
| 19400 | 0.001900| 0.232315 |
| 19500 | 0.001300| 0.222621 |
| 19600 | 0.001300| 0.240302 |
| 19700 | 0.001300| 0.241781 |
| 19800 | 0.001300| 0.269149 |
| 19900 | 0.001300| 0.219018 |
| 20000 | 0.001400| 0.248050 |
| 20100 | 0.001400| 0.229688 |
| 20200 | 0.001400| 0.239501 |
| 20300 | 0.001400| 0.268694 |
| 20400 | 0.001400| 0.274840 |
| 20500 | 0.001300| 0.248240 |
| 20600 | 0.001300| 0.257988 |
| 20700 | 0.001300| 0.250519 |
| 20800 | 0.001300| 0.245723 |
| 20900 | 0.001300| 0.261572 |
| 21000 | 0.001200| 0.246133 |
| 21100 | 0.001200| 0.229774 |
| 21200 | 0.001200| 0.208827 |
| 21300 | 0.001200| 0.243179 |
| 21400 | 0.001200| 0.240906 |
| 21500 | 0.000600| 0.239661 |
| 21600 | 0.000600| 0.244203 |
| 21700 | 0.000600| 0.261082 |
| 21800 | 0.000600| 0.241161 |
| 21900 | 0.000600| 0.281254 |
| 22000 | 0.001600| 0.289732 |
| 22100 | 0.001600| 0.250815 |
| 22200 | 0.001600| 0.274771 |
| 22300 | 0.001600| 0.246459 |
| 22400 | 0.001600| 0.224530 |
| 22500 | 0.001200| 0.257518 |
| 22600 | 0.001200| 0.254099 |
| 22700 | 0.001200| 0.264190 |
| 22800 | 0.001200| 0.272964 |
| 22900 | 0.001200| 0.281039 |
| 23000 | 0.000900| 0.294881 |
| 23100 | 0.000900| 0.264351 |
| 23200 | 0.000900| 0.283872 |
| 23300 | 0.000900| 0.284469 |
| 23400 | 0.000900| 0.243716 |
| 23500 | 0.001200| 0.252970 |
| 23600 | 0.001200| 0.235496 |
| 23700 | 0.001200| 0.246921 |
| 23800 | 0.001200| 0.259446 |
| 23900 | 0.001200| 0.256852 |
| 24000 | 0.000600| 0.239791 |
| 24100 | 0.000600| 0.251410 |
| 24200 | 0.000600| 0.253499 |
| 24300 | 0.000600| 0.216110 |
| 24400 | 0.000600| 0.228419 |
| 24500 | 0.000500| 0.231425 |
| 24600 | 0.000500| 0.222847 |
| 24700 | 0.000500| 0.233325 |
| 24800 | 0.000500| 0.230961 |
| 24900 | 0.000500| 0.223775 |
| 25000 | 0.001500| 0.229884 |
| 25100 | 0.001500| 0.224879 |
| 25200 | 0.001500| 0.215886 |
| 25300 | 0.001500| 0.229122 |
| 25400 | 0.001500| 0.243697 |
| 25500 | 0.000900| 0.245515 |
| 25600 | 0.000900| 0.232157 |
| 25700 | 0.000900| 0.237162 |
| 25800 | 0.000900| 0.244510 |
| 25900 | 0.000900| 0.248212 |
| 26000 | 0.000400| 0.239014 |
| 26100 | 0.000400| 0.244146 |
| 26200 | 0.000400| 0.228095 |
| 26300 | 0.000400| 0.230700 |
| 26400 | 0.000400| 0.227404 |
| 26500 | 0.000300| 0.232739 |
| 26600 | 0.000300| 0.246335 |
| 26700 | 0.000300| 0.241653 |
| 26800 | 0.000300| 0.248361 |
| 26900 | 0.000300| 0.252419 |
| 27000 | 0.000500| 0.249153 |
| 27100 | 0.000500| 0.246641 |
| 27200 | 0.000500| 0.237843 |
| 27300 | 0.000500| 0.237281 |
| 27400 | 0.000500| 0.235281 |
| 27500 | 0.000700| 0.220346 |
| 27600 | 0.000700| 0.220535 |
| 27700 | 0.000700| 0.219589 |
| 27800 | 0.000700| 0.222483 |
| 27900 | 0.000700| 0.225659 |
| 28000 | 0.000100| 0.228401 |
| 28100 | 0.000100| 0.227247 |
| 28200 | 0.000100| 0.232755 |
| 28300 | 0.000100| 0.232288 |
| 28400 | 0.000100| 0.232112 |
| 28500	| 0.000700 | 0.231037 |
| 28600	| 0.000700 | 0.231302 |
| 28700	| 0.000700 | 0.231330 |
| 28800	| 0.000700 | 0.230988 |


</details>

### Framework Versions
- Python: 3.10.16
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu126
- Accelerate: 1.4.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0





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