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---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:100000
- loss:OnlineContrastiveLoss
base_model: sentence-transformers/stsb-distilbert-base
widget:
- source_sentence: Can I retrieve my deleted text messages on my LG phone?
sentences:
- Why do we sleep?
- How do I recover a deleted text message from my phone without a computer?
- What are subjects to study in upsc?
- source_sentence: How can I prepare for IPS?
sentences:
- What should I prepare for ips?
- I am trying to find a meaning to life, to give a purpose to my life. Is there
any book that can help me find my answer, or at least give me the tools?
- What are the health benefits of Turmeric?
- source_sentence: Which is the best game development laptop for ₹60,000 to ₹70,000
INR?
sentences:
- Why doesn't Palestine appear on Google Maps as of 2016?
- Which is the best laptop for game development under ₹70,000 INR?
- What is meant by judicial review in the context of the Indian Judiciary?
- source_sentence: Although light beam bouncing between two plates inside a clock
is often used to explain time dilation, how can other practical cases be explained?
sentences:
- Is Run Ze Cao's falsification of Einstein's relativity valid?
- If India denies Pakistan water, will Pakistan give up its nuclear weapons?
- How do I revise class 12 syllabus in 1 month?
- source_sentence: How can I lose weight quickly? Need serious help.
sentences:
- Which is the best romantic movie?
- Why are there so many half-built, abandoned buildings in Mexico?
- How can you lose weight really quick?
datasets:
- sentence-transformers/quora-duplicates
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
- average_precision
- f1
- precision
- recall
- threshold
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on sentence-transformers/stsb-distilbert-base
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: quora duplicates
type: quora-duplicates
metrics:
- type: cosine_accuracy
value: 0.866
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7860240340232849
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.8320802005012532
name: Cosine F1
- type: cosine_f1_threshold
value: 0.7848798036575317
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.7811764705882352
name: Cosine Precision
- type: cosine_recall
value: 0.8900804289544236
name: Cosine Recall
- type: cosine_ap
value: 0.8772887253419398
name: Cosine Ap
- type: cosine_mcc
value: 0.7256385093029618
name: Cosine Mcc
- task:
type: paraphrase-mining
name: Paraphrase Mining
dataset:
name: quora duplicates dev
type: quora-duplicates-dev
metrics:
- type: average_precision
value: 0.6392503009812087
name: Average Precision
- type: f1
value: 0.6435291762586327
name: F1
- type: precision
value: 0.644658344613225
name: Precision
- type: recall
value: 0.6424039566368587
name: Recall
- type: threshold
value: 0.8726956844329834
name: Threshold
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.9172
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9588
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9672
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9762
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9172
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4102
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2644
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.14058
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7868590910037675
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.91981069059372
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9442488336402158
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9641439212486859
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9388257874901692
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9393049206349205
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9258332306777016
name: Cosine Map@100
---
# SentenceTransformer based on sentence-transformers/stsb-distilbert-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) on the [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) 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:** [sentence-transformers/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) <!-- at revision a560fa5fec90547a51a4a41a392d4aef93b49f16 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
- **Language:** en
<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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("CalebR84/stsb-distilbert-base-ocl")
# Run inference
sentences = [
'How can I lose weight quickly? Need serious help.',
'How can you lose weight really quick?',
'Why are there so many half-built, abandoned buildings in Mexico?',
]
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
#### Binary Classification
* Dataset: `quora-duplicates`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:--------------------------|:-----------|
| cosine_accuracy | 0.866 |
| cosine_accuracy_threshold | 0.786 |
| cosine_f1 | 0.8321 |
| cosine_f1_threshold | 0.7849 |
| cosine_precision | 0.7812 |
| cosine_recall | 0.8901 |
| **cosine_ap** | **0.8773** |
| cosine_mcc | 0.7256 |
#### Paraphrase Mining
* Dataset: `quora-duplicates-dev`
* Evaluated with [<code>ParaphraseMiningEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator) with these parameters:
```json
{'add_transitive_closure': <function ParaphraseMiningEvaluator.add_transitive_closure at 0x00000219B2FE09A0>, 'max_pairs': 500000, 'top_k': 100}
```
| Metric | Value |
|:----------------------|:-----------|
| **average_precision** | **0.6393** |
| f1 | 0.6435 |
| precision | 0.6447 |
| recall | 0.6424 |
| threshold | 0.8727 |
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9172 |
| cosine_accuracy@3 | 0.9588 |
| cosine_accuracy@5 | 0.9672 |
| cosine_accuracy@10 | 0.9762 |
| cosine_precision@1 | 0.9172 |
| cosine_precision@3 | 0.4102 |
| cosine_precision@5 | 0.2644 |
| cosine_precision@10 | 0.1406 |
| cosine_recall@1 | 0.7869 |
| cosine_recall@3 | 0.9198 |
| cosine_recall@5 | 0.9442 |
| cosine_recall@10 | 0.9641 |
| **cosine_ndcg@10** | **0.9388** |
| cosine_mrr@10 | 0.9393 |
| cosine_map@100 | 0.9258 |
<!--
## 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
#### quora-duplicates
* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 100,000 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.56 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.73 tokens</li><li>max: 84 tokens</li></ul> | <ul><li>0: ~63.20%</li><li>1: ~36.80%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------|:---------------|
| <code>What are some of the greatest books not adapted into film yet?</code> | <code>What book should be made into a movie?</code> | <code>0</code> |
| <code>How can I increase my communication skills?</code> | <code>How we improve our communication skills?</code> | <code>1</code> |
| <code>Heymen I have a note5 it give me this message when a turn it on and shout down (custom pinary are blocked by frp lock) I try odin and kies butnot work?</code> | <code>Setup dubbing studio with very less budget in India?</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Evaluation Dataset
#### quora-duplicates
* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 1,000 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 3 tokens</li><li>mean: 15.37 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.63 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>0: ~62.70%</li><li>1: ~37.30%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:---------------|
| <code>Which is the best book to learn data structures and algorithms?</code> | <code>Which book is the best book for algorithm and datastructure?</code> | <code>1</code> |
| <code>Does modafinil shows up on a drug test? Because my urine smells a lot of medicine?</code> | <code>Can Modafinil come out in a drug test?</code> | <code>0</code> |
| <code>Does the size of a penis matter?</code> | <code>Does penis size matters for girls?</code> | <code>1</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `fp16`: 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`: 64
- `per_device_eval_batch_size`: 64
- `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`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: 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
- `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>
| Epoch | Step | Training Loss | Validation Loss | quora-duplicates_cosine_ap | quora-duplicates-dev_average_precision | cosine_ndcg@10 |
|:------:|:-----:|:-------------:|:---------------:|:--------------------------:|:--------------------------------------:|:--------------:|
| 0 | 0 | - | - | 0.6905 | 0.4200 | 0.9397 |
| 0.0640 | 100 | 2.6402 | - | - | - | - |
| 0.1280 | 200 | 2.4398 | - | - | - | - |
| 0.1599 | 250 | - | 2.4217 | 0.7392 | 0.4765 | 0.9426 |
| 0.1919 | 300 | 2.2461 | - | - | - | - |
| 0.2559 | 400 | 2.1433 | - | - | - | - |
| 0.3199 | 500 | 2.0417 | 2.1120 | 0.7970 | 0.4566 | 0.9429 |
| 0.3839 | 600 | 2.0441 | - | - | - | - |
| 0.4479 | 700 | 1.8907 | - | - | - | - |
| 0.4798 | 750 | - | 2.0011 | 0.8229 | 0.4820 | 0.9468 |
| 0.5118 | 800 | 1.8985 | - | - | - | - |
| 0.5758 | 900 | 1.7521 | - | - | - | - |
| 0.6398 | 1000 | 1.8888 | 1.8010 | 0.8382 | 0.4925 | 0.9425 |
| 0.7038 | 1100 | 1.8524 | - | - | - | - |
| 0.7678 | 1200 | 1.6956 | - | - | - | - |
| 0.7997 | 1250 | - | 1.8004 | 0.8438 | 0.4283 | 0.9336 |
| 0.8317 | 1300 | 1.7519 | - | - | - | - |
| 0.8957 | 1400 | 1.7515 | - | - | - | - |
| 0.9597 | 1500 | 1.7288 | 1.7434 | 0.8352 | 0.5050 | 0.9428 |
| 1.0237 | 1600 | 1.533 | - | - | - | - |
| 1.0877 | 1700 | 1.2543 | - | - | - | - |
| 1.1196 | 1750 | - | 1.7109 | 0.8514 | 0.5299 | 0.9415 |
| 1.1516 | 1800 | 1.3201 | - | - | - | - |
| 1.2156 | 1900 | 1.3309 | - | - | - | - |
| 1.2796 | 2000 | 1.3256 | 1.7111 | 0.8528 | 0.5138 | 0.9393 |
| 1.3436 | 2100 | 1.2865 | - | - | - | - |
| 1.4075 | 2200 | 1.2659 | - | - | - | - |
| 1.4395 | 2250 | - | 1.7974 | 0.8468 | 0.5320 | 0.9390 |
| 1.4715 | 2300 | 1.2601 | - | - | - | - |
| 1.5355 | 2400 | 1.3337 | - | - | - | - |
| 1.5995 | 2500 | 1.3319 | 1.6922 | 0.8575 | 0.5399 | 0.9416 |
| 1.6635 | 2600 | 1.3232 | - | - | - | - |
| 1.7274 | 2700 | 1.3684 | - | - | - | - |
| 1.7594 | 2750 | - | 1.5772 | 0.8581 | 0.5592 | 0.9484 |
| 1.7914 | 2800 | 1.2706 | - | - | - | - |
| 1.8554 | 2900 | 1.3186 | - | - | - | - |
| 1.9194 | 3000 | 1.2336 | 1.5423 | 0.8656 | 0.5749 | 0.9433 |
| 1.9834 | 3100 | 1.2193 | - | - | - | - |
| 2.0473 | 3200 | 0.868 | - | - | - | - |
| 2.0793 | 3250 | - | 1.6575 | 0.8632 | 0.5735 | 0.9395 |
| 2.1113 | 3300 | 0.6411 | - | - | - | - |
| 2.1753 | 3400 | 0.7127 | - | - | - | - |
| 2.2393 | 3500 | 0.7044 | 1.5778 | 0.8718 | 0.5823 | 0.9387 |
| 2.3033 | 3600 | 0.6299 | - | - | - | - |
| 2.3672 | 3700 | 0.7162 | - | - | - | - |
| 2.3992 | 3750 | - | 1.6300 | 0.8595 | 0.5936 | 0.9414 |
| 2.4312 | 3800 | 0.6642 | - | - | - | - |
| 2.4952 | 3900 | 0.6902 | - | - | - | - |
| 2.5592 | 4000 | 0.7959 | 1.6070 | 0.8637 | 0.6006 | 0.9363 |
| 2.6232 | 4100 | 0.7588 | - | - | - | - |
| 2.6871 | 4200 | 0.6925 | - | - | - | - |
| 2.7191 | 4250 | - | 1.6787 | 0.8682 | 0.6006 | 0.9411 |
| 2.7511 | 4300 | 0.7226 | - | - | - | - |
| 2.8151 | 4400 | 0.7507 | - | - | - | - |
| 2.8791 | 4500 | 0.7563 | 1.6040 | 0.8658 | 0.6061 | 0.9416 |
| 2.9431 | 4600 | 0.7737 | - | - | - | - |
| 3.0070 | 4700 | 0.6525 | - | - | - | - |
| 3.0390 | 4750 | - | 1.6782 | 0.8652 | 0.5983 | 0.9401 |
| 3.0710 | 4800 | 0.3831 | - | - | - | - |
| 3.1350 | 4900 | 0.297 | - | - | - | - |
| 3.1990 | 5000 | 0.3725 | 1.7229 | 0.8588 | 0.6175 | 0.9418 |
| 3.2630 | 5100 | 0.4142 | - | - | - | - |
| 3.3269 | 5200 | 0.4415 | - | - | - | - |
| 3.3589 | 5250 | - | 1.6564 | 0.8635 | 0.6026 | 0.9379 |
| 3.3909 | 5300 | 0.3729 | - | - | - | - |
| 3.4549 | 5400 | 0.4164 | - | - | - | - |
| 3.5189 | 5500 | 0.3668 | 1.5964 | 0.8677 | 0.6105 | 0.9358 |
| 3.5829 | 5600 | 0.4184 | - | - | - | - |
| 3.6468 | 5700 | 0.4311 | - | - | - | - |
| 3.6788 | 5750 | - | 1.6523 | 0.8680 | 0.6130 | 0.9365 |
| 3.7108 | 5800 | 0.4222 | - | - | - | - |
| 3.7748 | 5900 | 0.4302 | - | - | - | - |
| 3.8388 | 6000 | 0.428 | 1.6625 | 0.8674 | 0.6163 | 0.9370 |
| 3.9028 | 6100 | 0.3898 | - | - | - | - |
| 3.9667 | 6200 | 0.4255 | - | - | - | - |
| 3.9987 | 6250 | - | 1.6145 | 0.8680 | 0.6118 | 0.9347 |
| 4.0307 | 6300 | 0.3456 | - | - | - | - |
| 4.0947 | 6400 | 0.2265 | - | - | - | - |
| 4.1587 | 6500 | 0.1913 | 1.7208 | 0.8595 | 0.6339 | 0.9433 |
| 4.2226 | 6600 | 0.2258 | - | - | - | - |
| 4.2866 | 6700 | 0.2484 | - | - | - | - |
| 4.3186 | 6750 | - | 1.6286 | 0.8600 | 0.6313 | 0.9394 |
| 4.3506 | 6800 | 0.1977 | - | - | - | - |
| 4.4146 | 6900 | 0.2013 | - | - | - | - |
| 4.4786 | 7000 | 0.2351 | 1.6910 | 0.8651 | 0.6193 | 0.9401 |
| 4.5425 | 7100 | 0.2356 | - | - | - | - |
| 4.6065 | 7200 | 0.2542 | - | - | - | - |
| 4.6385 | 7250 | - | 1.6955 | 0.8643 | 0.6129 | 0.9357 |
| 4.6705 | 7300 | 0.2592 | - | - | - | - |
| 4.7345 | 7400 | 0.2585 | - | - | - | - |
| 4.7985 | 7500 | 0.2375 | 1.7593 | 0.8647 | 0.6143 | 0.9325 |
| 4.8624 | 7600 | 0.2506 | - | - | - | - |
| 4.9264 | 7700 | 0.2394 | - | - | - | - |
| 4.9584 | 7750 | - | 1.6051 | 0.8720 | 0.6213 | 0.9350 |
| 4.9904 | 7800 | 0.2374 | - | - | - | - |
| 5.0544 | 7900 | 0.1675 | - | - | - | - |
| 5.1184 | 8000 | 0.131 | 1.5864 | 0.8673 | 0.6201 | 0.9377 |
| 5.1823 | 8100 | 0.1308 | - | - | - | - |
| 5.2463 | 8200 | 0.1483 | - | - | - | - |
| 5.2783 | 8250 | - | 1.5976 | 0.8698 | 0.6136 | 0.9359 |
| 5.3103 | 8300 | 0.1413 | - | - | - | - |
| 5.3743 | 8400 | 0.1392 | - | - | - | - |
| 5.4383 | 8500 | 0.1464 | 1.5980 | 0.8661 | 0.6267 | 0.9346 |
| 5.5022 | 8600 | 0.1781 | - | - | - | - |
| 5.5662 | 8700 | 0.151 | - | - | - | - |
| 5.5982 | 8750 | - | 1.5343 | 0.8756 | 0.6245 | 0.9352 |
| 5.6302 | 8800 | 0.1568 | - | - | - | - |
| 5.6942 | 8900 | 0.1702 | - | - | - | - |
| 5.7582 | 9000 | 0.1362 | 1.7121 | 0.8675 | 0.6230 | 0.9362 |
| 5.8221 | 9100 | 0.1371 | - | - | - | - |
| 5.8861 | 9200 | 0.1381 | - | - | - | - |
| 5.9181 | 9250 | - | 1.6326 | 0.8671 | 0.6122 | 0.9302 |
| 5.9501 | 9300 | 0.1691 | - | - | - | - |
| 6.0141 | 9400 | 0.1701 | - | - | - | - |
| 6.0781 | 9500 | 0.0935 | 1.5705 | 0.8709 | 0.6066 | 0.9293 |
| 6.1420 | 9600 | 0.0852 | - | - | - | - |
| 6.2060 | 9700 | 0.0874 | - | - | - | - |
| 6.2380 | 9750 | - | 1.5643 | 0.8724 | 0.6061 | 0.9307 |
| 6.2700 | 9800 | 0.0889 | - | - | - | - |
| 6.3340 | 9900 | 0.0972 | - | - | - | - |
| 6.3980 | 10000 | 0.1011 | 1.5622 | 0.8736 | 0.6153 | 0.9328 |
| 6.4619 | 10100 | 0.0962 | - | - | - | - |
| 6.5259 | 10200 | 0.1259 | - | - | - | - |
| 6.5579 | 10250 | - | 1.5406 | 0.8687 | 0.6293 | 0.9373 |
| 6.5899 | 10300 | 0.0925 | - | - | - | - |
| 6.6539 | 10400 | 0.1138 | - | - | - | - |
| 6.7179 | 10500 | 0.0788 | 1.5450 | 0.8658 | 0.6226 | 0.9349 |
| 6.7818 | 10600 | 0.1112 | - | - | - | - |
| 6.8458 | 10700 | 0.0922 | - | - | - | - |
| 6.8778 | 10750 | - | 1.5063 | 0.8736 | 0.6245 | 0.9370 |
| 6.9098 | 10800 | 0.1173 | - | - | - | - |
| 6.9738 | 10900 | 0.1141 | - | - | - | - |
| 7.0377 | 11000 | 0.0637 | 1.5007 | 0.8741 | 0.6270 | 0.9379 |
| 7.1017 | 11100 | 0.0713 | - | - | - | - |
| 7.1657 | 11200 | 0.0754 | - | - | - | - |
| 7.1977 | 11250 | - | 1.5081 | 0.8725 | 0.6273 | 0.9376 |
| 7.2297 | 11300 | 0.04 | - | - | - | - |
| 7.2937 | 11400 | 0.0695 | - | - | - | - |
| 7.3576 | 11500 | 0.034 | 1.5598 | 0.8710 | 0.6179 | 0.9350 |
| 7.4216 | 11600 | 0.0513 | - | - | - | - |
| 7.4856 | 11700 | 0.0749 | - | - | - | - |
| 7.5176 | 11750 | - | 1.6118 | 0.8694 | 0.6264 | 0.9380 |
| 7.5496 | 11800 | 0.0708 | - | - | - | - |
| 7.6136 | 11900 | 0.0939 | - | - | - | - |
| 7.6775 | 12000 | 0.059 | 1.6282 | 0.8708 | 0.6271 | 0.9354 |
| 7.7415 | 12100 | 0.0847 | - | - | - | - |
| 7.8055 | 12200 | 0.0521 | - | - | - | - |
| 7.8375 | 12250 | - | 1.5478 | 0.8683 | 0.6359 | 0.9388 |
| 7.8695 | 12300 | 0.0394 | - | - | - | - |
| 7.9335 | 12400 | 0.0619 | - | - | - | - |
| 7.9974 | 12500 | 0.0593 | 1.5440 | 0.8771 | 0.6387 | 0.9393 |
| 8.0614 | 12600 | 0.0292 | - | - | - | - |
| 8.1254 | 12700 | 0.0267 | - | - | - | - |
| 8.1574 | 12750 | - | 1.5419 | 0.8773 | 0.6290 | 0.9388 |
| 8.1894 | 12800 | 0.0334 | - | - | - | - |
| 8.2534 | 12900 | 0.05 | - | - | - | - |
| 8.3173 | 13000 | 0.0439 | 1.5589 | 0.8740 | 0.6322 | 0.9384 |
| 8.3813 | 13100 | 0.0409 | - | - | - | - |
| 8.4453 | 13200 | 0.03 | - | - | - | - |
| 8.4773 | 13250 | - | 1.5472 | 0.8730 | 0.6347 | 0.9398 |
| 8.5093 | 13300 | 0.0373 | - | - | - | - |
| 8.5733 | 13400 | 0.0404 | - | - | - | - |
| 8.6372 | 13500 | 0.0357 | 1.5332 | 0.8749 | 0.6327 | 0.9404 |
| 8.7012 | 13600 | 0.023 | - | - | - | - |
| 8.7652 | 13700 | 0.0256 | - | - | - | - |
| 8.7972 | 13750 | - | 1.5154 | 0.8781 | 0.6337 | 0.9379 |
| 8.8292 | 13800 | 0.0563 | - | - | - | - |
| 8.8932 | 13900 | 0.029 | - | - | - | - |
| 8.9571 | 14000 | 0.0395 | 1.5503 | 0.8771 | 0.6344 | 0.9390 |
| 9.0211 | 14100 | 0.0296 | - | - | - | - |
| 9.0851 | 14200 | 0.0308 | - | - | - | - |
| 9.1171 | 14250 | - | 1.5385 | 0.8771 | 0.6363 | 0.9391 |
| 9.1491 | 14300 | 0.035 | - | - | - | - |
| 9.2131 | 14400 | 0.0217 | - | - | - | - |
| 9.2770 | 14500 | 0.0192 | 1.5592 | 0.8777 | 0.6373 | 0.9393 |
| 9.3410 | 14600 | 0.0369 | - | - | - | - |
| 9.4050 | 14700 | 0.0186 | - | - | - | - |
| 9.4370 | 14750 | - | 1.5626 | 0.8771 | 0.6368 | 0.9389 |
| 9.4690 | 14800 | 0.0303 | - | - | - | - |
| 9.5329 | 14900 | 0.0181 | - | - | - | - |
| 9.5969 | 15000 | 0.0217 | 1.5466 | 0.8782 | 0.6387 | 0.9390 |
| 9.6609 | 15100 | 0.0463 | - | - | - | - |
| 9.7249 | 15200 | 0.0211 | - | - | - | - |
| 9.7569 | 15250 | - | 1.5440 | 0.8772 | 0.6401 | 0.9395 |
| 9.7889 | 15300 | 0.0216 | - | - | - | - |
| 9.8528 | 15400 | 0.0328 | - | - | - | - |
| 9.9168 | 15500 | 0.0154 | 1.5399 | 0.8773 | 0.6393 | 0.9388 |
| 9.9808 | 15600 | 0.0263 | - | - | - | - |
</details>
### Framework Versions
- Python: 3.12.9
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.7.0+cu126
- Accelerate: 1.7.0
- Datasets: 3.6.0
- 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",
}
```
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