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ModernBERT-large-biencoder-msmarco

Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
Generated from Trainer
dataset_size:499184
loss:MultipleNegativesRankingLoss
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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:499184
- loss:MultipleNegativesRankingLoss
base_model: answerdotai/ModernBERT-large
widget:
- source_sentence: how long will rotisserie chicken keep in refridgerator
sentences:
- >-
1 Meats with gravy or sauces: 1 to 2 days refrigerator or 6 months
(freezer). 2 Rotisserie chicken: 3 to 4 days (refrigerator) or 2 to 3
months (freezer). 3 Opened package of hot dogs: 1 week (refrigerator) or 1
to 2 months (freezer).4 Opened package of deli meat: 3 to 4 days
(refrigerator) or 1 to 2 months (freezer). Rotisserie chicken: 3 to 4 days
(refrigerator) or 2 to 3 months (freezer). 2 Opened package of hot dogs: 1
week (refrigerator) or 1 to 2 months (freezer). 3 Opened package of deli
meat: 3 to 4 days (refrigerator) or 1 to 2 months (freezer).
- >-
Can Spinach Cause Constipation? Those who have problems with constipation
will want to stay away from certain foods including spinach. Because spinach
has so much fiber in it, it can cause constipation in some people,
especially those who are already prone to it. Other foods which you will
want to avoid if you problems with constipation include apples, peaches, raw
carrots, zucchini, kidney beans, lima beans, and whole-grain cereal.
- >-
Brush the chickens with oil and season the outside and cavities with salt
and pepper. Skewer the chickens onto the rotisserie rod and grill, on the
rotisserie, for 30 to 35 minutes, or until the chicken is golden brown and
just cooked through. Remove from grill and let rest for 10 minutes before
serving.
- source_sentence: empyema causes
sentences:
- "Causes of an Empyema. Most cases of an empyema are related to bacterial pneumonia (infection of the lung). Pneumonia tends to cause a pleural effusion â\x80\x93 para-pneumonic effusion. This can be uncomplicated (containing exudate), complicated (exudate with high concentrations of neurophils) or empyema thoracis (pus in the pleural space)."
- >-
empyema - a collection of pus in a body cavity (especially in the lung
cavity) inflammatory disease - a disease characterized by inflammation.
purulent pleurisy - a collection of pus in the lung cavity. Translations.
- >-
Laminar Flow. The resistance to flow in a liquid can be characterized in
terms of the viscosity of the fluid if the flow is smooth. In the case of a
moving plate in a liquid, it is found that there is a layer or lamina which
moves with the plate, and a layer which is essentially stationary if it is
next to a stationary plate.
- source_sentence: why is coal found in layers
sentences:
- >-
Email the author | Follow on Twitter. on March 06, 2015 at 6:03 PM, updated
March 06, 2015 at 6:35 PM. Comments. CLEVELAND, Ohio -- The first day of
spring 2015 will be on March 20, with winter officially ending at 6:45 p.m.
that day. Summer 2015 will begin on June 21, fall on Sept. 23 and winter on
Dec. 21.
- >-
EXPERT ANSWER. Coal if formed when dead animals and plants got buried inside
the layer of Earth. The layers increase form time to time and more dead
plants and animals get buried in the layers.Therefore, coal is found in
layers.For example, let us consider the layers of sandwich, on the first
bread we apply the toppings and cover it another slice. Then some more
topping is added to second slice and is covered by third slide.XPERT ANSWER.
Coal if formed when dead animals and plants got buried inside the layer of
Earth. The layers increase form time to time and more dead plants and
animals get buried in the layers.
- >-
Why is Coal not classified as a Mineral? July 8, 2011, shiela, Leave a
comment. Why is Coal not classified as a Mineral? Coal is not a mineral
because it does not qualify to be one. A mineral is made of rocks. It is
non-living and made up of atoms of elements. Coals on the other hand are
carbon-based and came from fossilized plants. By just looking into the
origin of coals, these are not qualified to be minerals because they come
from organic material and it has no definite chemical composition. Minerals
are not formed from living things such as plants or animals. They are
building blocks of rocks and are formed thousands of years ago. Coals on the
other hand came from dead plants and animals. The coals are formed when
these living creatures will decay. Again, it takes thousands of years to
form a coal.
- source_sentence: where is the ford edge built
sentences:
- >-
Amongst fruit-bearing cherry trees, there are two main types: Prunus avium
(sweet cherries), which are the kind sold in produce sections for eating,
and Prunus cerasus (sour cherries), which are the kind used in cooking and
baking.mongst fruit-bearing cherry trees, there are two main types: Prunus
avium (sweet cherries), which are the kind sold in produce sections for
eating, and Prunus cerasus (sour cherries), which are the kind used in
cooking and baking.
- >-
Ford is recalling 204,448 Edge and Lincoln MKX crossovers in North America
for fuel-tank brackets that can rust and cause gas to leak, the automaker
said.
- >-
Ford Edge to be built at new $760 million plant in China. DETROIT, MI - Ford
Motor Co. announced Tuesday it has opened its sixth assembly plant in China,
with a $760 million investment for the Changan Ford Hangzhou Plant.
- source_sentence: what is a tensilon universal testing instrument
sentences:
- >-
Universal Material Testing Instrument. The TENSILON RTF is our newest
universal testing machine offering innovative measuring possibilities, based
on A&D's newly-developed and extensive technological knowledge.The RTF
Series is a world-class Class 0.5 testing machine.Having improved the
overall design and structure of the machine, we achieved a very strong load
frame stiffness enabling super-high accuracy in measurement.he RTF Series is
a world-class Class 0.5 testing machine. Having improved the overall design
and structure of the machine, we achieved a very strong load frame stiffness
enabling super-high accuracy in measurement.
- >-
The term ectopic pregnancy frequently refers to a pregnancy that has
occurred in one of the fallopian tubes, instead of the uterus. This is the
case about 95 percent of the time, but ectopic pregnancies can also be
abdominal, ovarian, cornual, or cervical.
- >-
The McDonald Patent Universal String Tension Calculator (MPUSTC) is a handy
calculator to figure string tensions in steel-string instruments. If you
plug in your scale length, string gauges and tuning, it will give you a
readout of the tension on each of the strings. This is useful when you're
trying to fine-tune a set of custom gauges, or when you're working out how
far you can push a drop tuning before it becomes unmanageable.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: mit
---
# SentenceTransformer based on answerdotai/ModernBERT-large
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large). 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:** [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) <!-- at revision 45bb4654a4d5aaff24dd11d4781fa46d39bf8c13 -->
- **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': False}) with Transformer model: ModernBertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'what is a tensilon universal testing instrument',
"Universal Material Testing Instrument. The TENSILON RTF is our newest universal testing machine offering innovative measuring possibilities, based on A&D's newly-developed and extensive technological knowledge.The RTF Series is a world-class Class 0.5 testing machine.Having improved the overall design and structure of the machine, we achieved a very strong load frame stiffness enabling super-high accuracy in measurement.he RTF Series is a world-class Class 0.5 testing machine. Having improved the overall design and structure of the machine, we achieved a very strong load frame stiffness enabling super-high accuracy in measurement.",
"The McDonald Patent Universal String Tension Calculator (MPUSTC) is a handy calculator to figure string tensions in steel-string instruments. If you plug in your scale length, string gauges and tuning, it will give you a readout of the tension on each of the strings. This is useful when you're trying to fine-tune a set of custom gauges, or when you're working out how far you can push a drop tuning before it becomes unmanageable.",
]
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: 499,184 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.07 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 80.89 tokens</li><li>max: 254 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 79.05 tokens</li><li>max: 226 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:---------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is a dependent person</code> | <code>1. depending on a person or thing for aid, support, life, etc. 2. (postpositive; foll by on or upon) influenced or conditioned (by); contingent (on) 3. subordinate; subject: a dependent prince. 4. obsolete hanging down.</code> | <code>Dependent personality disorder (DPD) is one of the most frequently diagnosed personality disorders. It occurs equally in men and women, usually becoming apparent in young adulthood or later as important adult relationships form. People with DPD become emotionally dependent on other people and spend great effort trying to please others. People with DPD tend to display needy, passive, and clinging behavior, and have a fear of separation. Other common characteristics of this personality disorder include:</code> |
| <code>what is the hat trick in hockey</code> | <code>Definition of hat trick. 1 1 : the retiring of three batsmen with three consecutive balls by a bowler in cricket. 2 2 : the scoring of three goals in one game (as of hockey or soccer) by a single player. 3 3 : a series of three victories, successes, or related accomplishments scored a hat trick when her three best steers corralled top honors — People.</code> | <code>Hat trick was first recorded in print in the 1870s, but has since been widened to apply to any sport in which the person competing carries off some feat three times in quick succession, such as scoring three goals in one game of soccer.</code> |
| <code>what is an egalitarian</code> | <code>An egalitarian is defined as a person who believes all people were created equal and should be treated equal. An example of an egalitarian is a person who fights for civil rights, like Martin Luther King Jr.</code> | <code>About Egalitarian Companies. In the tradition hierarchical corporate structure, each employee operates under a specific job description. Each employee also reports to a superior who monitors his progress and issues instructions. Egalitarian-style companies eliminate most of this structure. Employees in an egalitarian company have general job descriptions, rather than specific ones. Instead of reporting to a superior, all employees in an egalitarian company work collaboratively on tasks and behave as equals.</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
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 10
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### 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`: 32
- `per_device_eval_batch_size`: 32
- `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
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: 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}
- `tp_size`: 0
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:------:|:-------------:|
| 0.0321 | 500 | 1.1178 |
| 0.0641 | 1000 | 0.293 |
| 0.0962 | 1500 | 0.2542 |
| 0.1282 | 2000 | 0.2357 |
| 0.1603 | 2500 | 0.2187 |
| 0.1923 | 3000 | 0.2107 |
| 0.2244 | 3500 | 0.1959 |
| 0.2564 | 4000 | 0.2049 |
| 0.2885 | 4500 | 0.1945 |
| 0.3205 | 5000 | 0.1848 |
| 0.3526 | 5500 | 0.1846 |
| 0.3846 | 6000 | 0.1736 |
| 0.4167 | 6500 | 0.1795 |
| 0.4487 | 7000 | 0.1767 |
| 0.4808 | 7500 | 0.1727 |
| 0.5128 | 8000 | 0.1688 |
| 0.5449 | 8500 | 0.1708 |
| 0.5769 | 9000 | 0.1663 |
| 0.6090 | 9500 | 0.1654 |
| 0.6410 | 10000 | 0.1637 |
| 0.6731 | 10500 | 0.1651 |
| 0.7051 | 11000 | 0.1625 |
| 0.7372 | 11500 | 0.1584 |
| 0.7692 | 12000 | 0.1607 |
| 0.8013 | 12500 | 0.156 |
| 0.8333 | 13000 | 0.1548 |
| 0.8654 | 13500 | 0.1484 |
| 0.8974 | 14000 | 0.1527 |
| 0.9295 | 14500 | 0.1555 |
| 0.9615 | 15000 | 0.1528 |
| 0.9936 | 15500 | 0.1533 |
| 1.0256 | 16000 | 0.0827 |
| 1.0577 | 16500 | 0.0597 |
| 1.0897 | 17000 | 0.0599 |
| 1.1218 | 17500 | 0.0592 |
| 1.1538 | 18000 | 0.0592 |
| 1.1859 | 18500 | 0.0584 |
| 1.2179 | 19000 | 0.0615 |
| 1.25 | 19500 | 0.0589 |
| 1.2821 | 20000 | 0.0612 |
| 1.3141 | 20500 | 0.0618 |
| 1.3462 | 21000 | 0.0606 |
| 1.3782 | 21500 | 0.0587 |
| 1.4103 | 22000 | 0.0611 |
| 1.4423 | 22500 | 0.0616 |
| 1.4744 | 23000 | 0.0623 |
| 1.5064 | 23500 | 0.0615 |
| 1.5385 | 24000 | 0.0602 |
| 1.5705 | 24500 | 0.0658 |
| 1.6026 | 25000 | 0.068 |
| 1.6346 | 25500 | 0.0649 |
| 1.6667 | 26000 | 0.0645 |
| 1.6987 | 26500 | 0.0652 |
| 1.7308 | 27000 | 0.0632 |
| 1.7628 | 27500 | 0.0631 |
| 1.7949 | 28000 | 0.0655 |
| 1.8269 | 28500 | 0.0633 |
| 1.8590 | 29000 | 0.0607 |
| 1.8910 | 29500 | 0.0633 |
| 1.9231 | 30000 | 0.0612 |
| 1.9551 | 30500 | 0.0631 |
| 1.9872 | 31000 | 0.0616 |
| 2.0192 | 31500 | 0.0382 |
| 2.0513 | 32000 | 0.0178 |
| 2.0833 | 32500 | 0.0177 |
| 2.1154 | 33000 | 0.0178 |
| 2.1474 | 33500 | 0.0171 |
| 2.1795 | 34000 | 0.0188 |
| 2.2115 | 34500 | 0.0186 |
| 2.2436 | 35000 | 0.0177 |
| 2.2756 | 35500 | 0.0183 |
| 2.3077 | 36000 | 0.0195 |
| 2.3397 | 36500 | 0.0202 |
| 2.3718 | 37000 | 0.0199 |
| 2.4038 | 37500 | 0.0197 |
| 2.4359 | 38000 | 0.019 |
| 2.4679 | 38500 | 0.021 |
| 2.5 | 39000 | 0.0195 |
| 2.5321 | 39500 | 0.0211 |
| 2.5641 | 40000 | 0.0205 |
| 2.5962 | 40500 | 0.0207 |
| 2.6282 | 41000 | 0.0222 |
| 2.6603 | 41500 | 0.0204 |
| 2.6923 | 42000 | 0.0205 |
| 2.7244 | 42500 | 0.0211 |
| 2.7564 | 43000 | 0.0232 |
| 2.7885 | 43500 | 0.0202 |
| 2.8205 | 44000 | 0.0207 |
| 2.8526 | 44500 | 0.0225 |
| 2.8846 | 45000 | 0.0224 |
| 2.9167 | 45500 | 0.0203 |
| 2.9487 | 46000 | 0.0215 |
| 2.9808 | 46500 | 0.0218 |
| 3.0128 | 47000 | 0.0159 |
| 3.0449 | 47500 | 0.0064 |
| 3.0769 | 48000 | 0.0069 |
| 3.1090 | 48500 | 0.0074 |
| 3.1410 | 49000 | 0.0075 |
| 3.1731 | 49500 | 0.0066 |
| 3.2051 | 50000 | 0.0076 |
| 3.2372 | 50500 | 0.0073 |
| 3.2692 | 51000 | 0.0077 |
| 3.3013 | 51500 | 0.0075 |
| 3.3333 | 52000 | 0.0079 |
| 3.3654 | 52500 | 0.008 |
| 3.3974 | 53000 | 0.0087 |
| 3.4295 | 53500 | 0.0077 |
| 3.4615 | 54000 | 0.0084 |
| 3.4936 | 54500 | 0.0086 |
| 3.5256 | 55000 | 0.009 |
| 3.5577 | 55500 | 0.0082 |
| 3.5897 | 56000 | 0.0084 |
| 3.6218 | 56500 | 0.0084 |
| 3.6538 | 57000 | 0.008 |
| 3.6859 | 57500 | 0.0079 |
| 3.7179 | 58000 | 0.0085 |
| 3.75 | 58500 | 0.0096 |
| 3.7821 | 59000 | 0.0087 |
| 3.8141 | 59500 | 0.0086 |
| 3.8462 | 60000 | 0.0089 |
| 3.8782 | 60500 | 0.0081 |
| 3.9103 | 61000 | 0.0087 |
| 3.9423 | 61500 | 0.0085 |
| 3.9744 | 62000 | 0.0082 |
| 4.0064 | 62500 | 0.0076 |
| 4.0385 | 63000 | 0.0037 |
| 4.0705 | 63500 | 0.0035 |
| 4.1026 | 64000 | 0.0037 |
| 4.1346 | 64500 | 0.004 |
| 4.1667 | 65000 | 0.0037 |
| 4.1987 | 65500 | 0.0036 |
| 4.2308 | 66000 | 0.0042 |
| 4.2628 | 66500 | 0.0044 |
| 4.2949 | 67000 | 0.0041 |
| 4.3269 | 67500 | 0.004 |
| 4.3590 | 68000 | 0.0037 |
| 4.3910 | 68500 | 0.0043 |
| 4.4231 | 69000 | 0.0035 |
| 4.4551 | 69500 | 0.0045 |
| 4.4872 | 70000 | 0.0042 |
| 4.5192 | 70500 | 0.0043 |
| 4.5513 | 71000 | 0.0042 |
| 4.5833 | 71500 | 0.0049 |
| 4.6154 | 72000 | 0.0041 |
| 4.6474 | 72500 | 0.0041 |
| 4.6795 | 73000 | 0.0044 |
| 4.7115 | 73500 | 0.0038 |
| 4.7436 | 74000 | 0.0039 |
| 4.7756 | 74500 | 0.0049 |
| 4.8077 | 75000 | 0.0041 |
| 4.8397 | 75500 | 0.0044 |
| 4.8718 | 76000 | 0.0043 |
| 4.9038 | 76500 | 0.0053 |
| 4.9359 | 77000 | 0.0043 |
| 4.9679 | 77500 | 0.0049 |
| 5.0 | 78000 | 0.0042 |
| 5.0321 | 78500 | 0.0022 |
| 5.0641 | 79000 | 0.0023 |
| 5.0962 | 79500 | 0.0021 |
| 5.1282 | 80000 | 0.003 |
| 5.1603 | 80500 | 0.0024 |
| 5.1923 | 81000 | 0.0022 |
| 5.2244 | 81500 | 0.0023 |
| 5.2564 | 82000 | 0.0022 |
| 5.2885 | 82500 | 0.0027 |
| 5.3205 | 83000 | 0.0023 |
| 5.3526 | 83500 | 0.0029 |
| 5.3846 | 84000 | 0.0027 |
| 5.4167 | 84500 | 0.0025 |
| 5.4487 | 85000 | 0.0029 |
| 5.4808 | 85500 | 0.0029 |
| 5.5128 | 86000 | 0.0024 |
| 5.5449 | 86500 | 0.0026 |
| 5.5769 | 87000 | 0.0026 |
| 5.6090 | 87500 | 0.0028 |
| 5.6410 | 88000 | 0.0025 |
| 5.6731 | 88500 | 0.0026 |
| 5.7051 | 89000 | 0.0023 |
| 5.7372 | 89500 | 0.0029 |
| 5.7692 | 90000 | 0.0027 |
| 5.8013 | 90500 | 0.0019 |
| 5.8333 | 91000 | 0.0023 |
| 5.8654 | 91500 | 0.0022 |
| 5.8974 | 92000 | 0.003 |
| 5.9295 | 92500 | 0.0023 |
| 5.9615 | 93000 | 0.0026 |
| 5.9936 | 93500 | 0.0027 |
| 6.0256 | 94000 | 0.0015 |
| 6.0577 | 94500 | 0.0012 |
| 6.0897 | 95000 | 0.0016 |
| 6.1218 | 95500 | 0.0018 |
| 6.1538 | 96000 | 0.0017 |
| 6.1859 | 96500 | 0.0014 |
| 6.2179 | 97000 | 0.0013 |
| 6.25 | 97500 | 0.0022 |
| 6.2821 | 98000 | 0.0015 |
| 6.3141 | 98500 | 0.002 |
| 6.3462 | 99000 | 0.0021 |
| 6.3782 | 99500 | 0.0016 |
| 6.4103 | 100000 | 0.0024 |
| 6.4423 | 100500 | 0.002 |
| 6.4744 | 101000 | 0.0014 |
| 6.5064 | 101500 | 0.0019 |
| 6.5385 | 102000 | 0.0017 |
| 6.5705 | 102500 | 0.0019 |
| 6.6026 | 103000 | 0.0016 |
| 6.6346 | 103500 | 0.0013 |
| 6.6667 | 104000 | 0.0012 |
| 6.6987 | 104500 | 0.0015 |
| 6.7308 | 105000 | 0.0015 |
| 6.7628 | 105500 | 0.0018 |
| 6.7949 | 106000 | 0.0018 |
| 6.8269 | 106500 | 0.0016 |
| 6.8590 | 107000 | 0.0018 |
| 6.8910 | 107500 | 0.0026 |
| 6.9231 | 108000 | 0.0013 |
| 6.9551 | 108500 | 0.0019 |
| 6.9872 | 109000 | 0.0015 |
| 7.0192 | 109500 | 0.0014 |
| 7.0513 | 110000 | 0.0009 |
| 7.0833 | 110500 | 0.0012 |
| 7.1154 | 111000 | 0.0016 |
| 7.1474 | 111500 | 0.0014 |
| 7.1795 | 112000 | 0.0013 |
| 7.2115 | 112500 | 0.0009 |
| 7.2436 | 113000 | 0.0015 |
| 7.2756 | 113500 | 0.0011 |
| 7.3077 | 114000 | 0.0011 |
| 7.3397 | 114500 | 0.0011 |
| 7.3718 | 115000 | 0.0013 |
| 7.4038 | 115500 | 0.001 |
| 7.4359 | 116000 | 0.0012 |
| 7.4679 | 116500 | 0.0012 |
| 7.5 | 117000 | 0.0013 |
| 7.5321 | 117500 | 0.0014 |
| 7.5641 | 118000 | 0.0013 |
| 7.5962 | 118500 | 0.0013 |
| 7.6282 | 119000 | 0.0014 |
| 7.6603 | 119500 | 0.001 |
| 7.6923 | 120000 | 0.0012 |
| 7.7244 | 120500 | 0.0018 |
| 7.7564 | 121000 | 0.001 |
| 7.7885 | 121500 | 0.0014 |
| 7.8205 | 122000 | 0.0011 |
| 7.8526 | 122500 | 0.0012 |
| 7.8846 | 123000 | 0.0012 |
| 7.9167 | 123500 | 0.0008 |
| 7.9487 | 124000 | 0.0013 |
| 7.9808 | 124500 | 0.0014 |
| 8.0128 | 125000 | 0.001 |
| 8.0449 | 125500 | 0.0007 |
| 8.0769 | 126000 | 0.001 |
| 8.1090 | 126500 | 0.0009 |
| 8.1410 | 127000 | 0.0007 |
| 8.1731 | 127500 | 0.0007 |
| 8.2051 | 128000 | 0.001 |
| 8.2372 | 128500 | 0.0011 |
| 8.2692 | 129000 | 0.0008 |
| 8.3013 | 129500 | 0.0007 |
| 8.3333 | 130000 | 0.0013 |
| 8.3654 | 130500 | 0.0012 |
| 8.3974 | 131000 | 0.001 |
| 8.4295 | 131500 | 0.001 |
| 8.4615 | 132000 | 0.0007 |
| 8.4936 | 132500 | 0.001 |
| 8.5256 | 133000 | 0.001 |
| 8.5577 | 133500 | 0.001 |
| 8.5897 | 134000 | 0.0011 |
| 8.6218 | 134500 | 0.0013 |
| 8.6538 | 135000 | 0.0007 |
| 8.6859 | 135500 | 0.001 |
| 8.7179 | 136000 | 0.0008 |
| 8.75 | 136500 | 0.001 |
| 8.7821 | 137000 | 0.0008 |
| 8.8141 | 137500 | 0.0006 |
| 8.8462 | 138000 | 0.0006 |
| 8.8782 | 138500 | 0.0009 |
| 8.9103 | 139000 | 0.0007 |
| 8.9423 | 139500 | 0.0009 |
| 8.9744 | 140000 | 0.0006 |
| 9.0064 | 140500 | 0.0018 |
| 9.0385 | 141000 | 0.0008 |
| 9.0705 | 141500 | 0.0008 |
| 9.1026 | 142000 | 0.0009 |
| 9.1346 | 142500 | 0.0006 |
| 9.1667 | 143000 | 0.0009 |
| 9.1987 | 143500 | 0.0007 |
| 9.2308 | 144000 | 0.0007 |
| 9.2628 | 144500 | 0.0006 |
| 9.2949 | 145000 | 0.0008 |
| 9.3269 | 145500 | 0.0009 |
| 9.3590 | 146000 | 0.0005 |
| 9.3910 | 146500 | 0.001 |
| 9.4231 | 147000 | 0.001 |
| 9.4551 | 147500 | 0.0011 |
| 9.4872 | 148000 | 0.0011 |
| 9.5192 | 148500 | 0.0012 |
| 9.5513 | 149000 | 0.0011 |
| 9.5833 | 149500 | 0.0007 |
| 9.6154 | 150000 | 0.0008 |
| 9.6474 | 150500 | 0.0005 |
| 9.6795 | 151000 | 0.0007 |
| 9.7115 | 151500 | 0.0008 |
| 9.7436 | 152000 | 0.0007 |
| 9.7756 | 152500 | 0.0009 |
| 9.8077 | 153000 | 0.0007 |
| 9.8397 | 153500 | 0.0012 |
| 9.8718 | 154000 | 0.0005 |
| 9.9038 | 154500 | 0.0008 |
| 9.9359 | 155000 | 0.0007 |
| 9.9679 | 155500 | 0.0007 |
| 10.0 | 156000 | 0.0011 |
</details>
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.50.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## 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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