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 ---
base_model: bobox/DeBERTa-small-ST-v1-test-step3
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:163205
- loss:CachedGISTEmbedLoss
widget:
- source_sentence: interview question on why you want to leave your current job?
sentences:
- Carrier proteins bind and carry the molecules across the cell membrane. These
proteins bind a molecule on one side of the membrane, change shape as they carry
the molecule across the membrane, and deposit the molecule on the other side of
the membrane. Even though a protein is involved in both these methods of transport,
neither method requires energy. Therefore these are still types of passive transport.
- '[''Desire to learn.'', ''Desire to take on more responsibility.'', ''Desire to
take on less responsibility.'', ''Desire to relocate.'', ''Desire for a career
change.'', ''Desire to gain a new skill or grow a current skill.'', ''Company
reorganization has led to change in job content.'']'
- The small intestine is a narrow tube that starts at the stomach and ends at the
large intestine. In adults, it’s about 7 meters (23 feet) long. Most chemical
digestion and almost all nutrient absorption take place in the small intestine.
- source_sentence: 'They say a number of people from the Mujahideen-e-Khalq (MEK)
group were injured at Camp Liberty in Baghdad.
Baghdad has in the past repeatedly denied attacking the group.
MEK members fought with Iraq against Iran in the 1980s, but have since fallen
out with the current Iraqi government.
In an emailed message, the Paris-based National Council of Resistance of Iran
(NCRI), the MEK''s parent group, said dozens of missiles hit the camp on Thursday
evening.
It said two residents were killed and a third later died in hospital of his wounds.
The US condemned the attack "in the strongest terms" and urged Iraq to better
protect the camp.
An Iranian-backed Shia militia, al-Mukhtar Army, said it had fired rockets at
the camp, Reuters news agency reported.
The camp is located in a former US military base, near Baghdad''s airport.
The Iraqi authorities have made no public comments on the report. However, one
security official was quoted by the Associated Press as saying four rockets hit
the camp, injuring two people.
In September, the MEK accused Iraqi forces of attacking Camp Ashraf north-east
of Baghdad and killing 52 of the group''s members.
In recent years, Baghdad has been trying to dismantle MEK camps and eject the
group.
Iran considers the MEK a terrorist group.
The group was removed from the US state department''s list of terrorist organisations
last year.'
sentences:
- A rocket attack has killed three members of an Iranian opposition group in Iraq,
the group and its parent organisation say.
- Directions See How It's Made. 1 Preheat the oven to 375 degrees. 2 Heat a large
skillet over medium heat. 3 Add the spinach. Spread the spinach mixture evenly
on the bottom of the prepared baking 1 dish. Bake until the egg whites are set,
about 25 minutes. Let the casserole sit for about 5 minutes, and then cut into
pieces and 1 serve. Submit a Correction.
- 'Toll free phone number: 011-44-871-246-0002. Ryan Airlines www.ryanair.com is
a low-fare airlines headquartered in Dublin, Ireland. Transporting over 103 million
passengers last year there are 1600 daily flights with 185 destinations. The Ryan
fleet consists of 300 new Boeing 737-800 aircraft in operation with 283 738 aircraft
on order.'
- source_sentence: In what unit is heat measured in?
sentences:
- The heat that is either absorbed or released is measured in joules. The mass is
measured in grams. The change in temperature is given by , where is the final
temperature and is the initial temperature.
- The nitrogen atom of a primary amine is bonded to two hydrogen atoms and one carbon.
The nitrogen atom of a secondary amine is bonded to one hydrogen and two carbons.
The nitrogen atom of a tertiary amine is bonded to three carbon atoms. Amines
are typically named by a common system rather than by IUPAC guidelines. The common
system for naming amines along with several examples is shown below.
- 'Seattle Symphony Live @ Benaroya Hall — Windborne''s The Music of David Bowie:
A Rock Symphony with the Seattle Symphony Tuesday, 10 January, 2017 7:30PM Join
conductor Brent Havens and a full rock band on a symphonic musical odyssey that
explores the incredible range of David Bowie’s Music.'
- source_sentence: meristematic tissue definition
sentences:
- In this chapter, you saw how pressure and buoyancy of fluids can be used to make
work easier — from raising a car on a lift to floating a ship on the ocean. Devices
that make work easier are called machines in physics.
- A Land Rover is splashing water as it crosses a river.
- "meristem. n. 1. (Botany) a plant tissue responsible for growth, whose cells divide\
\ and differentiate to form the tissues and organs of the plant. Meristems occur\
\ within the stem (see cambium) and leaves and at the tips of stems and roots.\
\ [C19: from Greek meristos divided, from merizein to divide, from meris portion].\
\ (Ë\x88mÉ\x9Br É\x99Ë\x8CstÉ\x9Bm)."
- source_sentence: More than 190 countries and territories around the world had confirmed
coronavirus cases by March 22 , 2020 .
sentences:
- when electricity flows to a light bulb , the light bulb will come on
- As of 22 March , more than 337,000 cases of COVID-19 have been reported in over
190 countries and territories , resulting in more than 14,400 deaths and 96,000
recoveries .
- a greenhouse is used to protect plants by keeping them warm
model-index:
- name: SentenceTransformer based on bobox/DeBERTa-small-ST-v1-test-step3
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.8785914848590666
name: Pearson Cosine
- type: spearman_cosine
value: 0.9048987433800361
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9087606701935215
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.9056138237858093
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9086611488562145
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9052247563192726
name: Spearman Euclidean
- type: pearson_dot
value: 0.8570818659891223
name: Pearson Dot
- type: spearman_dot
value: 0.8616398023022556
name: Spearman Dot
- type: pearson_max
value: 0.9087606701935215
name: Pearson Max
- type: spearman_max
value: 0.9056138237858093
name: Spearman Max
---
# SentenceTransformer based on bobox/DeBERTa-small-ST-v1-test-step3
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [bobox/DeBERTa-small-ST-v1-test-step3](https://huggingface.co/bobox/DeBERTa-small-ST-v1-test-step3) on the bobox/enhanced_nli-50_k dataset. 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:** [bobox/DeBERTa-small-ST-v1-test-step3](https://huggingface.co/bobox/DeBERTa-small-ST-v1-test-step3) <!-- at revision df9aaa75fe0c2791e5ed35ff33de1689d9a5f5ff -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- bobox/enhanced_nli-50_k
<!-- - **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: DebertaV2Model
(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})
)
```
## 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("bobox/DeBERTa-small-ST-v1-test-UnifiedDatasets-Ft-checkpoints-tmp")
# Run inference
sentences = [
'More than 190 countries and territories around the world had confirmed coronavirus cases by March 22 , 2020 .',
'As of 22 March , more than 337,000 cases of COVID-19 have been reported in over 190 countries and territories , resulting in more than 14,400 deaths and 96,000 recoveries .',
'a greenhouse is used to protect plants by keeping them warm',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8786 |
| **spearman_cosine** | **0.9049** |
| pearson_manhattan | 0.9088 |
| spearman_manhattan | 0.9056 |
| pearson_euclidean | 0.9087 |
| spearman_euclidean | 0.9052 |
| pearson_dot | 0.8571 |
| spearman_dot | 0.8616 |
| pearson_max | 0.9088 |
| spearman_max | 0.9056 |
<!--
## 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
#### bobox/enhanced_nli-50_k
* Dataset: bobox/enhanced_nli-50_k
* Size: 163,205 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 36.63 tokens</li><li>max: 370 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 54.93 tokens</li><li>max: 363 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>apple customer number</code> | <code>Apple Customer Service. 800-676-2775. Before dialing Apple Customer Service. We've tried for years to eliminate that 44 minute wait for you. But the fact is that it can still take many phone calls to Apple to resolve your issue. Now you can hire GetHuman to do all the work on the phone for you.</code> |
| <code>Molly 's Game grossed more than $ 28.5 million in the US and Canada and less than $ 24.65 in other countries .</code> | <code>, Molly 's Game has grossed $ 28.8 million in the United States and Canada , and $ 24.6 million in other territories , for a worldwide total of $ 53.4 million .</code> |
| <code>mawk definition</code> | <code>Definitions for mawk. Here are all the possible meanings and translations of the word mawk. Wiktionary(0.00 / 0 votes)Rate this definition: Origin: From mawk, mauk, a contraction of mathek, from maðkr, a diminutive of a base from maþa- (Old English maþa), from Indo-European *math-, moth- used in reference to insects and vermin. Cognate with Danish madike, Swedish mask, archaic English maddock (modern maggot).</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, '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()
), 'temperature': 0.025}
```
### Evaluation Dataset
#### bobox/enhanced_nli-50_k
* Dataset: bobox/enhanced_nli-50_k
* Size: 3,052 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 33.13 tokens</li><li>max: 313 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 58.53 tokens</li><li>max: 422 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Dwight Beare, 27, from Melbourne, Australia, was killed in the crash near to the 16th milestone on the TT course on the Isle of Man.<br>Mr Beare suffered fatal spine and back injuries on the west of the island on 4 June at about 14:10 BST.<br>A verdict of misadventure was recorded by Coroner John Needham at Douglas Court House earlier.<br>Mr Needham said: "I am not in a position to give an exact reason for the loss of control but Mr Beare would have died almost instantaneously".<br>The inquest heard from a witness who said the carpenter and his passenger, Benjamin Binns, were thrown into the air after their vehicle hit the road side.<br>Mr Binns, who was airlifted to hospital with a broken leg, said before the race both he and Mr Beare had been "excited and confident".<br>"I will miss my friend dearly and his memory will live on," he said.<br>No defects were found on the vehicle.<br>Mr Beare moved to Onchan on the Isle of Man to pursue his road racing passion.<br>He made his TT debut in 2014 finishing 12th in the second race of the week, with his father Noel as his passenger.<br>He returned in 2015 when he came 17th.</code> | <code>A TT racer died after being thrown from his sidecar when he hit the side of a road, an inquest has heard.</code> |
| <code>are yeezy adidas or nike?</code> | <code>Adidas Yeezy is a fashion collaboration between the German sportswear brand Adidas and American designer Kanye West. The collaboration has become notable for its high-end sneakers, and the Yeezy Boost sneaker line has been considered one of the most influential sneaker brands in the world.</code> |
| <code>what remains are changed into natural gas by heat and pressure change?</code> | <code>heat and pressure change the remains of prehistoric living things into natural gas. Dinosaurs and Other Prehistoric Creatures Dinosaurs are just one group of prehistoric animals. <br> heat and pressure change the remains of dinosaurs into natural gas</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, '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()
), 'temperature': 0.025}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 320
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `weight_decay`: 0.0001
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine_with_restarts
- `lr_scheduler_kwargs`: {'num_cycles': 3}
- `warmup_ratio`: 0.25
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTa-small-ST-v1-test-UnifiedDatasets-Ft-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `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`: 320
- `per_device_eval_batch_size`: 128
- `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`: 0.0001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: cosine_with_restarts
- `lr_scheduler_kwargs`: {'num_cycles': 3}
- `warmup_ratio`: 0.25
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `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}
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: bobox/DeBERTa-small-ST-v1-test-UnifiedDatasets-Ft-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | loss | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:------------------------:|
| 0.0020 | 1 | 0.107 | - | - |
| 0.0039 | 2 | 0.1529 | - | - |
| 0.0059 | 3 | 0.1874 | - | - |
| 0.0078 | 4 | 0.1682 | - | - |
| 0.0098 | 5 | 0.1438 | 0.1470 | 0.9078 |
| 0.0117 | 6 | 0.2961 | - | - |
| 0.0137 | 7 | 0.3019 | - | - |
| 0.0157 | 8 | 0.1184 | - | - |
| 0.0176 | 9 | 0.3176 | - | - |
| 0.0196 | 10 | 0.2234 | 0.1468 | 0.9078 |
| 0.0215 | 11 | 0.1881 | - | - |
| 0.0235 | 12 | 0.1593 | - | - |
| 0.0254 | 13 | 0.1833 | - | - |
| 0.0274 | 14 | 0.1352 | - | - |
| 0.0294 | 15 | 0.3143 | 0.1462 | 0.9077 |
| 0.0313 | 16 | 0.1583 | - | - |
| 0.0333 | 17 | 0.2015 | - | - |
| 0.0352 | 18 | 0.1476 | - | - |
| 0.0372 | 19 | 0.1676 | - | - |
| 0.0391 | 20 | 0.1525 | 0.1454 | 0.9079 |
| 0.0411 | 21 | 0.1717 | - | - |
| 0.0431 | 22 | 0.198 | - | - |
| 0.0450 | 23 | 0.3062 | - | - |
| 0.0470 | 24 | 0.1241 | - | - |
| 0.0489 | 25 | 0.1087 | 0.1446 | 0.9082 |
| 0.0509 | 26 | 0.1767 | - | - |
| 0.0528 | 27 | 0.1951 | - | - |
| 0.0548 | 28 | 0.1621 | - | - |
| 0.0568 | 29 | 0.221 | - | - |
| 0.0587 | 30 | 0.2241 | 0.1435 | 0.9083 |
| 0.0607 | 31 | 0.2093 | - | - |
| 0.0626 | 32 | 0.1615 | - | - |
| 0.0646 | 33 | 0.1615 | - | - |
| 0.0665 | 34 | 0.1772 | - | - |
| 0.0685 | 35 | 0.2324 | 0.1423 | 0.9084 |
| 0.0705 | 36 | 0.2611 | - | - |
| 0.0724 | 37 | 0.214 | - | - |
| 0.0744 | 38 | 0.1985 | - | - |
| 0.0763 | 39 | 0.1855 | - | - |
| 0.0783 | 40 | 0.1234 | 0.1410 | 0.9085 |
| 0.0802 | 41 | 0.1492 | - | - |
| 0.0822 | 42 | 0.2022 | - | - |
| 0.0841 | 43 | 0.2146 | - | - |
| 0.0861 | 44 | 0.1688 | - | - |
| 0.0881 | 45 | 0.175 | 0.1396 | 0.9087 |
| 0.0900 | 46 | 0.2123 | - | - |
| 0.0920 | 47 | 0.1118 | - | - |
| 0.0939 | 48 | 0.3009 | - | - |
| 0.0959 | 49 | 0.1071 | - | - |
| 0.0978 | 50 | 0.2608 | 0.1382 | 0.9085 |
| 0.0998 | 51 | 0.1368 | - | - |
| 0.1018 | 52 | 0.2307 | - | - |
| 0.1037 | 53 | 0.1366 | - | - |
| 0.1057 | 54 | 0.1857 | - | - |
| 0.1076 | 55 | 0.2155 | 0.1367 | 0.9085 |
| 0.1096 | 56 | 0.2022 | - | - |
| 0.1115 | 57 | 0.2076 | - | - |
| 0.1135 | 58 | 0.4133 | - | - |
| 0.1155 | 59 | 0.1823 | - | - |
| 0.1174 | 60 | 0.1136 | 0.1353 | 0.9088 |
| 0.1194 | 61 | 0.1687 | - | - |
| 0.1213 | 62 | 0.1591 | - | - |
| 0.1233 | 63 | 0.1653 | - | - |
| 0.1252 | 64 | 0.1799 | - | - |
| 0.1272 | 65 | 0.1578 | 0.1337 | 0.9090 |
| 0.1292 | 66 | 0.1844 | - | - |
| 0.1311 | 67 | 0.1489 | - | - |
| 0.1331 | 68 | 0.1845 | - | - |
| 0.1350 | 69 | 0.1364 | - | - |
| 0.1370 | 70 | 0.1584 | 0.1321 | 0.9086 |
| 0.1389 | 71 | 0.2279 | - | - |
| 0.1409 | 72 | 0.2028 | - | - |
| 0.1429 | 73 | 0.2291 | - | - |
| 0.1448 | 74 | 0.2419 | - | - |
| 0.1468 | 75 | 0.1329 | 0.1306 | 0.9083 |
| 0.1487 | 76 | 0.204 | - | - |
| 0.1507 | 77 | 0.2239 | - | - |
| 0.1526 | 78 | 0.2181 | - | - |
| 0.1546 | 79 | 0.1285 | - | - |
| 0.1566 | 80 | 0.1067 | 0.1292 | 0.9079 |
| 0.1585 | 81 | 0.1189 | - | - |
| 0.1605 | 82 | 0.236 | - | - |
| 0.1624 | 83 | 0.1584 | - | - |
| 0.1644 | 84 | 0.1925 | - | - |
| 0.1663 | 85 | 0.129 | 0.1278 | 0.9079 |
| 0.1683 | 86 | 0.1376 | - | - |
| 0.1703 | 87 | 0.1691 | - | - |
| 0.1722 | 88 | 0.1045 | - | - |
| 0.1742 | 89 | 0.165 | - | - |
| 0.1761 | 90 | 0.2926 | 0.1267 | 0.9076 |
| 0.1781 | 91 | 0.1048 | - | - |
| 0.1800 | 92 | 0.1596 | - | - |
| 0.1820 | 93 | 0.2474 | - | - |
| 0.1840 | 94 | 0.1652 | - | - |
| 0.1859 | 95 | 0.2483 | 0.1253 | 0.9076 |
| 0.1879 | 96 | 0.1623 | - | - |
| 0.1898 | 97 | 0.1955 | - | - |
| 0.1918 | 98 | 0.2023 | - | - |
| 0.1937 | 99 | 0.1886 | - | - |
| 0.1957 | 100 | 0.1284 | 0.1229 | 0.9079 |
| 0.1977 | 101 | 0.2005 | - | - |
| 0.1996 | 102 | 0.2301 | - | - |
| 0.2016 | 103 | 0.2249 | - | - |
| 0.2035 | 104 | 0.214 | - | - |
| 0.2055 | 105 | 0.1429 | 0.1208 | 0.9077 |
| 0.2074 | 106 | 0.17 | - | - |
| 0.2094 | 107 | 0.1955 | - | - |
| 0.2114 | 108 | 0.1964 | - | - |
| 0.2133 | 109 | 0.1246 | - | - |
| 0.2153 | 110 | 0.1295 | 0.1190 | 0.9072 |
| 0.2172 | 111 | 0.2203 | - | - |
| 0.2192 | 112 | 0.2195 | - | - |
| 0.2211 | 113 | 0.1823 | - | - |
| 0.2231 | 114 | 0.174 | - | - |
| 0.2250 | 115 | 0.207 | 0.1175 | 0.9069 |
| 0.2270 | 116 | 0.2156 | - | - |
| 0.2290 | 117 | 0.2202 | - | - |
| 0.2309 | 118 | 0.2718 | - | - |
| 0.2329 | 119 | 0.1387 | - | - |
| 0.2348 | 120 | 0.1506 | 0.1168 | 0.9069 |
| 0.2368 | 121 | 0.1185 | - | - |
| 0.2387 | 122 | 0.1681 | - | - |
| 0.2407 | 123 | 0.2321 | - | - |
| 0.2427 | 124 | 0.1457 | - | - |
| 0.2446 | 125 | 0.2027 | 0.1165 | 0.9071 |
| 0.2466 | 126 | 0.1821 | - | - |
| 0.2485 | 127 | 0.1258 | - | - |
| 0.2505 | 128 | 0.184 | - | - |
| 0.2524 | 129 | 0.2015 | - | - |
| 0.2544 | 130 | 0.1323 | 0.1154 | 0.9074 |
| 0.2564 | 131 | 0.1939 | - | - |
| 0.2583 | 132 | 0.1428 | - | - |
| 0.2603 | 133 | 0.1063 | - | - |
| 0.2622 | 134 | 0.1602 | - | - |
| 0.2642 | 135 | 0.1814 | 0.1139 | 0.9067 |
| 0.2661 | 136 | 0.1518 | - | - |
| 0.2681 | 137 | 0.1379 | - | - |
| 0.2701 | 138 | 0.1708 | - | - |
| 0.2720 | 139 | 0.2046 | - | - |
| 0.2740 | 140 | 0.1259 | 0.1124 | 0.9063 |
| 0.2759 | 141 | 0.1181 | - | - |
| 0.2779 | 142 | 0.2144 | - | - |
| 0.2798 | 143 | 0.1822 | - | - |
| 0.2818 | 144 | 0.1667 | - | - |
| 0.2838 | 145 | 0.0779 | 0.1118 | 0.9060 |
| 0.2857 | 146 | 0.147 | - | - |
| 0.2877 | 147 | 0.1913 | - | - |
| 0.2896 | 148 | 0.1357 | - | - |
| 0.2916 | 149 | 0.1128 | - | - |
| 0.2935 | 150 | 0.0996 | 0.1113 | 0.9054 |
| 0.2955 | 151 | 0.1956 | - | - |
| 0.2975 | 152 | 0.0942 | - | - |
| 0.2994 | 153 | 0.1406 | - | - |
| 0.3014 | 154 | 0.2868 | - | - |
| 0.3033 | 155 | 0.1102 | 0.1114 | 0.9048 |
| 0.3053 | 156 | 0.1659 | - | - |
| 0.3072 | 157 | 0.1645 | - | - |
| 0.3092 | 158 | 0.151 | - | - |
| 0.3112 | 159 | 0.158 | - | - |
| 0.3131 | 160 | 0.2323 | 0.1113 | 0.9048 |
| 0.3151 | 161 | 0.1157 | - | - |
| 0.3170 | 162 | 0.1507 | - | - |
| 0.3190 | 163 | 0.1879 | - | - |
| 0.3209 | 164 | 0.143 | - | - |
| 0.3229 | 165 | 0.2227 | 0.1116 | 0.9050 |
| 0.3249 | 166 | 0.1624 | - | - |
| 0.3268 | 167 | 0.1345 | - | - |
| 0.3288 | 168 | 0.1765 | - | - |
| 0.3307 | 169 | 0.1368 | - | - |
| 0.3327 | 170 | 0.0962 | 0.1113 | 0.9056 |
| 0.3346 | 171 | 0.1783 | - | - |
| 0.3366 | 172 | 0.2019 | - | - |
| 0.3386 | 173 | 0.1761 | - | - |
| 0.3405 | 174 | 0.1855 | - | - |
| 0.3425 | 175 | 0.1922 | 0.1106 | 0.9054 |
| 0.3444 | 176 | 0.1538 | - | - |
| 0.3464 | 177 | 0.1049 | - | - |
| 0.3483 | 178 | 0.1619 | - | - |
| 0.3503 | 179 | 0.0731 | - | - |
| 0.3523 | 180 | 0.1205 | 0.1097 | 0.9059 |
| 0.3542 | 181 | 0.169 | - | - |
| 0.3562 | 182 | 0.1688 | - | - |
| 0.3581 | 183 | 0.1274 | - | - |
| 0.3601 | 184 | 0.1477 | - | - |
| 0.3620 | 185 | 0.1418 | 0.1094 | 0.9055 |
| 0.3640 | 186 | 0.2477 | - | - |
| 0.3659 | 187 | 0.1713 | - | - |
| 0.3679 | 188 | 0.1703 | - | - |
| 0.3699 | 189 | 0.1176 | - | - |
| 0.3718 | 190 | 0.1811 | 0.1084 | 0.9048 |
| 0.3738 | 191 | 0.162 | - | - |
| 0.3757 | 192 | 0.1141 | - | - |
| 0.3777 | 193 | 0.154 | - | - |
| 0.3796 | 194 | 0.2461 | - | - |
| 0.3816 | 195 | 0.1573 | 0.1076 | 0.9046 |
| 0.3836 | 196 | 0.1197 | - | - |
| 0.3855 | 197 | 0.1395 | - | - |
| 0.3875 | 198 | 0.0847 | - | - |
| 0.3894 | 199 | 0.1848 | - | - |
| 0.3914 | 200 | 0.1377 | 0.1072 | 0.9047 |
| 0.3933 | 201 | 0.1109 | - | - |
| 0.3953 | 202 | 0.1051 | - | - |
| 0.3973 | 203 | 0.0975 | - | - |
| 0.3992 | 204 | 0.127 | - | - |
| 0.4012 | 205 | 0.1297 | 0.1069 | 0.9049 |
| 0.4031 | 206 | 0.0783 | - | - |
</details>
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.44.0
- PyTorch: 2.4.0
- Accelerate: 0.33.0
- Datasets: 2.21.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
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