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---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/stsb-distilbert-base
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
- 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
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
widget:
- source_sentence: How metro works?
sentences:
- How can Turing machine works?
- What are the best C++ books?
- What should I learn first in PHP?
- source_sentence: How fast is fast?
sentences:
- How does light travel so fast?
- How could I become an actor?
- Was Muhammad a pedophile?
- source_sentence: What is a kernel?
sentences:
- What is a tensor?
- What does copyright protect?
- Can we increase height after 23?
- source_sentence: What is a tensor?
sentences:
- What is reliance jio?
- What are the reasons of war?
- Does speed reading really work?
- source_sentence: Is Cicret a scam?
sentences:
- Is the Cicret Bracelet a scam?
- Can you eat only once a day?
- What books should every man read?
pipeline_tag: sentence-similarity
co2_eq_emissions:
emissions: 15.153912802318576
energy_consumed: 0.038985939877640395
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.169
hardware_used: 1 x NVIDIA GeForce RTX 3090
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: 0.816
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7866689562797546
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.7285714285714286
name: Cosine F1
- type: cosine_f1_threshold
value: 0.735264778137207
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.6746031746031746
name: Cosine Precision
- type: cosine_recall
value: 0.7919254658385093
name: Cosine Recall
- type: cosine_ap
value: 0.7731120768804719
name: Cosine Ap
- type: dot_accuracy
value: 0.807
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 150.97946166992188
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.7223796033994335
name: Dot F1
- type: dot_f1_threshold
value: 137.3444366455078
name: Dot F1 Threshold
- type: dot_precision
value: 0.6640625
name: Dot Precision
- type: dot_recall
value: 0.7919254658385093
name: Dot Recall
- type: dot_ap
value: 0.749212069604305
name: Dot Ap
- type: manhattan_accuracy
value: 0.81
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 195.88662719726562
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.7246376811594203
name: Manhattan F1
- type: manhattan_f1_threshold
value: 237.68594360351562
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.6292906178489702
name: Manhattan Precision
- type: manhattan_recall
value: 0.8540372670807453
name: Manhattan Recall
- type: manhattan_ap
value: 0.7610544151599187
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.81
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 8.773942947387695
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.7260812581913498
name: Euclidean F1
- type: euclidean_f1_threshold
value: 10.843769073486328
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.6281179138321995
name: Euclidean Precision
- type: euclidean_recall
value: 0.860248447204969
name: Euclidean Recall
- type: euclidean_ap
value: 0.7611533877712096
name: Euclidean Ap
- type: max_accuracy
value: 0.816
name: Max Accuracy
- type: max_accuracy_threshold
value: 195.88662719726562
name: Max Accuracy Threshold
- type: max_f1
value: 0.7285714285714286
name: Max F1
- type: max_f1_threshold
value: 237.68594360351562
name: Max F1 Threshold
- type: max_precision
value: 0.6746031746031746
name: Max Precision
- type: max_recall
value: 0.860248447204969
name: Max Recall
- type: max_ap
value: 0.7731120768804719
name: Max Ap
- task:
type: paraphrase-mining
name: Paraphrase Mining
dataset:
name: quora duplicates dev
type: quora-duplicates-dev
metrics:
- type: average_precision
value: 0.5348666252858723
name: Average Precision
- type: f1
value: 0.5395064090300363
name: F1
- type: precision
value: 0.5174549291251892
name: Precision
- type: recall
value: 0.5635210071439276
name: Recall
- type: threshold
value: 0.762035459280014
name: Threshold
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.9646
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9926
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9956
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9986
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9646
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4293333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2754
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.14515999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.830104138622815
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9609072390452685
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9808022997296821
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9934541226453286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9795490191788223
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9789640476190478
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.971751123151301
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.9574
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.9876
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9924
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9978
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.9574
name: Dot Precision@1
- type: dot_precision@3
value: 0.4257333333333334
name: Dot Precision@3
- type: dot_precision@5
value: 0.27368000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.14468000000000003
name: Dot Precision@10
- type: dot_recall@1
value: 0.8237692901379665
name: Dot Recall@1
- type: dot_recall@3
value: 0.9538191510221804
name: Dot Recall@3
- type: dot_recall@5
value: 0.9764249670623496
name: Dot Recall@5
- type: dot_recall@10
value: 0.9918117957075603
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9740754474178193
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9731360317460321
name: Dot Mrr@10
- type: dot_map@100
value: 0.9646398037726347
name: Dot 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 [sentence-transformers/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 82ad392c08f81be9be9bf065339670b23f2e1493 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [sentence-transformers/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("tomaarsen/stsb-distilbert-base-mnrl")
# Run inference
sentences = [
'Is Cicret a scam?',
'Is the Cicret Bracelet a scam?',
'Can you eat only once a day?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(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/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.816 |
| cosine_accuracy_threshold | 0.7867 |
| cosine_f1 | 0.7286 |
| cosine_f1_threshold | 0.7353 |
| cosine_precision | 0.6746 |
| cosine_recall | 0.7919 |
| cosine_ap | 0.7731 |
| dot_accuracy | 0.807 |
| dot_accuracy_threshold | 150.9795 |
| dot_f1 | 0.7224 |
| dot_f1_threshold | 137.3444 |
| dot_precision | 0.6641 |
| dot_recall | 0.7919 |
| dot_ap | 0.7492 |
| manhattan_accuracy | 0.81 |
| manhattan_accuracy_threshold | 195.8866 |
| manhattan_f1 | 0.7246 |
| manhattan_f1_threshold | 237.6859 |
| manhattan_precision | 0.6293 |
| manhattan_recall | 0.854 |
| manhattan_ap | 0.7611 |
| euclidean_accuracy | 0.81 |
| euclidean_accuracy_threshold | 8.7739 |
| euclidean_f1 | 0.7261 |
| euclidean_f1_threshold | 10.8438 |
| euclidean_precision | 0.6281 |
| euclidean_recall | 0.8602 |
| euclidean_ap | 0.7612 |
| max_accuracy | 0.816 |
| max_accuracy_threshold | 195.8866 |
| max_f1 | 0.7286 |
| max_f1_threshold | 237.6859 |
| max_precision | 0.6746 |
| max_recall | 0.8602 |
| **max_ap** | **0.7731** |
#### Paraphrase Mining
* Dataset: `quora-duplicates-dev`
* Evaluated with [<code>ParaphraseMiningEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator)
| Metric | Value |
|:----------------------|:-----------|
| **average_precision** | **0.5349** |
| f1 | 0.5395 |
| precision | 0.5175 |
| recall | 0.5635 |
| threshold | 0.762 |
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9646 |
| cosine_accuracy@3 | 0.9926 |
| cosine_accuracy@5 | 0.9956 |
| cosine_accuracy@10 | 0.9986 |
| cosine_precision@1 | 0.9646 |
| cosine_precision@3 | 0.4293 |
| cosine_precision@5 | 0.2754 |
| cosine_precision@10 | 0.1452 |
| cosine_recall@1 | 0.8301 |
| cosine_recall@3 | 0.9609 |
| cosine_recall@5 | 0.9808 |
| cosine_recall@10 | 0.9935 |
| cosine_ndcg@10 | 0.9795 |
| cosine_mrr@10 | 0.979 |
| **cosine_map@100** | **0.9718** |
| dot_accuracy@1 | 0.9574 |
| dot_accuracy@3 | 0.9876 |
| dot_accuracy@5 | 0.9924 |
| dot_accuracy@10 | 0.9978 |
| dot_precision@1 | 0.9574 |
| dot_precision@3 | 0.4257 |
| dot_precision@5 | 0.2737 |
| dot_precision@10 | 0.1447 |
| dot_recall@1 | 0.8238 |
| dot_recall@3 | 0.9538 |
| dot_recall@5 | 0.9764 |
| dot_recall@10 | 0.9918 |
| dot_ndcg@10 | 0.9741 |
| dot_mrr@10 | 0.9731 |
| dot_map@100 | 0.9646 |
<!--
## 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
#### sentence-transformers/quora-duplicates
* Dataset: [sentence-transformers/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>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 13.85 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.65 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.76 tokens</li><li>max: 64 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------|
| <code>Why in India do we not have one on one political debate as in USA?</code> | <code>Why cant we have a public debate between politicians in India like the one in US?</code> | <code>Can people on Quora stop India Pakistan debate? We are sick and tired seeing this everyday in bulk?</code> |
| <code>What is OnePlus One?</code> | <code>How is oneplus one?</code> | <code>Why is OnePlus One so good?</code> |
| <code>Does our mind control our emotions?</code> | <code>How do smart and successful people control their emotions?</code> | <code>How can I control my positive emotions for the people whom I love but they don't care about me?</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### sentence-transformers/quora-duplicates
* Dataset: [sentence-transformers/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>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 13.84 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.8 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.71 tokens</li><li>max: 56 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Which programming language is best for developing low-end games?</code> | <code>What coding language should I learn first for making games?</code> | <code>I am entering the world of video game programming and want to know what language I should learn? Because there are so many languages I do not know which one to start with. Can you recommend a language that's easy to learn and can be used with many platforms?</code> |
| <code>Was it appropriate for Meryl Streep to use her Golden Globes speech to attack Donald Trump?</code> | <code>Should Meryl Streep be using her position to attack the president?</code> | <code>Why did Kelly Ann Conway say that Meryl Streep incited peoples worst feelings?</code> |
| <code>Where can I found excellent commercial fridges in Sydney?</code> | <code>Where can I found impressive range of commercial fridges in Sydney?</code> | <code>What is the best grocery delivery service in Sydney?</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/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`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 1
- `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`: False
- `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
- `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`: 1
- `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
- `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`: None
- `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`: 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_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | cosine_map@100 | quora-duplicates-dev_average_precision | quora-duplicates_max_ap |
|:------:|:----:|:-------------:|:------:|:--------------:|:--------------------------------------:|:-----------------------:|
| 0 | 0 | - | - | 0.9245 | 0.4200 | 0.6890 |
| 0.0640 | 100 | 0.2535 | - | - | - | - |
| 0.1280 | 200 | 0.1732 | - | - | - | - |
| 0.1599 | 250 | - | 0.1021 | 0.9601 | 0.5033 | 0.7342 |
| 0.1919 | 300 | 0.1465 | - | - | - | - |
| 0.2559 | 400 | 0.1186 | - | - | - | - |
| 0.3199 | 500 | 0.1159 | 0.0773 | 0.9653 | 0.5247 | 0.7453 |
| 0.3839 | 600 | 0.1088 | - | - | - | - |
| 0.4479 | 700 | 0.0993 | - | - | - | - |
| 0.4798 | 750 | - | 0.0665 | 0.9666 | 0.5264 | 0.7655 |
| 0.5118 | 800 | 0.0952 | - | - | - | - |
| 0.5758 | 900 | 0.0799 | - | - | - | - |
| 0.6398 | 1000 | 0.0855 | 0.0570 | 0.9709 | 0.5391 | 0.7717 |
| 0.7038 | 1100 | 0.0804 | - | - | - | - |
| 0.7678 | 1200 | 0.073 | - | - | - | - |
| 0.7997 | 1250 | - | 0.0513 | 0.9719 | 0.5329 | 0.7662 |
| 0.8317 | 1300 | 0.0741 | - | - | - | - |
| 0.8957 | 1400 | 0.0699 | - | - | - | - |
| 0.9597 | 1500 | 0.0755 | 0.0476 | 0.9718 | 0.5349 | 0.7731 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.039 kWh
- **Carbon Emitted**: 0.015 kg of CO2
- **Hours Used**: 0.169 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.0.0.dev0
- Transformers: 4.41.0.dev0
- PyTorch: 2.3.0+cu121
- Accelerate: 0.26.1
- Datasets: 2.18.0
- 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",
}
```
#### 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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