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mpnet-base-gooaq

Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
dataset_size:1M<n<10M
loss:MultipleNegativesRankingLoss
Eval Results
Inference Endpoints
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---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1M<n<10M
- loss:MultipleNegativesRankingLoss
base_model: microsoft/mpnet-base
datasets:
- sentence-transformers/gooaq
metrics:
- 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: 11 is what of 8?
sentences:
- '*RARE* CANDY AXE AND RED NOSED RAIDER IS BACK - FORTNITE ITEM SHOP 8TH DECEMBER
2019.'
- 'Convert fraction (ratio) 8 / 11 Answer: 72.727272727273%'
- Old-age pensions are not included in taxable income under the personal income
tax.
- source_sentence: is 50 shades of grey on prime?
sentences:
- 'Amazon.com: Watch Fifty Shades of Grey. Prime Video.'
- 'How much is 22 out of 100 written as a percentage? Convert fraction (ratio) 22
/ 100 Answer: 22%'
- Petco ferrets are neutered and as social animals, they enjoy each other's company.
- source_sentence: 20 of what is 18?
sentences:
- '20 percent (calculated percentage %) of what number equals 18? Answer: 90.'
- There are 3.35 x 1019 H2O molecules in a 1 mg snowflake.
- There are 104 total Power Moons and 100 Purple Coins in the Mushroom Kingdom.
- source_sentence: 63 up itv when is it on?
sentences:
- Mark Twain Quotes If you tell the truth, you don't have to remember anything.
- 63 Up is on ITV for three consecutive nights, Tuesday 4 – Thursday 6 June, at
9pm.
- In a language, the smallest units of meaning are. Morphemes.
- source_sentence: what is ikit in tagalog?
sentences:
- 'Definition: aunt. the sister of one''s father or mother; the wife of one''s uncle
(n.)'
- 'How much is 12 out of 29 written as a percentage? Convert fraction (ratio) 12
/ 29 Answer: 41.379310344828%'
- Iberia offers Wi-Fi on all long-haul aircraft so that you can stay connected using
your own devices.
pipeline_tag: sentence-similarity
co2_eq_emissions:
emissions: 636.2415070661234
energy_consumed: 1.636836206312608
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: 4.514
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: MPNet base trained on GooAQ Question-Answer tuples
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: gooaq dev
type: gooaq-dev
metrics:
- type: cosine_accuracy@1
value: 0.7198
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.884
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9305
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9709
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7198
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.29466666666666663
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1861
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09709000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7198
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.884
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9305
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9709
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8490972112228806
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8095713888888812
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8111457785591406
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.7073
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.877
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9244
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9669
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.7073
name: Dot Precision@1
- type: dot_precision@3
value: 0.2923333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.18488000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.09669000000000003
name: Dot Precision@10
- type: dot_recall@1
value: 0.7073
name: Dot Recall@1
- type: dot_recall@3
value: 0.877
name: Dot Recall@3
- type: dot_recall@5
value: 0.9244
name: Dot Recall@5
- type: dot_recall@10
value: 0.9669
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8412144933973646
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8004067857142795
name: Dot Mrr@10
- type: dot_map@100
value: 0.8022667466578848
name: Dot Map@100
---
# MPNet base trained on GooAQ Question-Answer tuples
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) 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.
This model was trained using the [train_script.py](train_script.py) code.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
- **Language:** en
- **License:** apache-2.0
### 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: MPNetModel
(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/mpnet-base-gooaq")
# Run inference
sentences = [
'11 is what of 8?',
'Convert fraction (ratio) 8 / 11 Answer: 72.727272727273%',
'Old-age pensions are not included in taxable income under the personal income tax.',
]
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
#### Information Retrieval
* Dataset: `gooaq-dev`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7198 |
| cosine_accuracy@3 | 0.884 |
| cosine_accuracy@5 | 0.9305 |
| cosine_accuracy@10 | 0.9709 |
| cosine_precision@1 | 0.7198 |
| cosine_precision@3 | 0.2947 |
| cosine_precision@5 | 0.1861 |
| cosine_precision@10 | 0.0971 |
| cosine_recall@1 | 0.7198 |
| cosine_recall@3 | 0.884 |
| cosine_recall@5 | 0.9305 |
| cosine_recall@10 | 0.9709 |
| cosine_ndcg@10 | 0.8491 |
| cosine_mrr@10 | 0.8096 |
| **cosine_map@100** | **0.8111** |
| dot_accuracy@1 | 0.7073 |
| dot_accuracy@3 | 0.877 |
| dot_accuracy@5 | 0.9244 |
| dot_accuracy@10 | 0.9669 |
| dot_precision@1 | 0.7073 |
| dot_precision@3 | 0.2923 |
| dot_precision@5 | 0.1849 |
| dot_precision@10 | 0.0967 |
| dot_recall@1 | 0.7073 |
| dot_recall@3 | 0.877 |
| dot_recall@5 | 0.9244 |
| dot_recall@10 | 0.9669 |
| dot_ndcg@10 | 0.8412 |
| dot_mrr@10 | 0.8004 |
| dot_map@100 | 0.8023 |
<!--
## 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/gooaq
* Dataset: [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 3,002,496 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.89 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 60.37 tokens</li><li>max: 147 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>biotechnology is best defined as?</code> | <code>Biotechnology is best defined as_______________? The science that involves using living organisms to produce needed materials. Which of the following tools of biotechnology, to do investigation, is used when trying crime?</code> |
| <code>how to open xye file?</code> | <code>Firstly, use File then Open and make sure that you can see All Files (*. *) and not just Excel files (the default option!) in the folder containing the *. xye file: Select the file you wish to open and Excel will bring up a wizard menu for importing plain text data into Excel (as shown below).</code> |
| <code>how much does california spend?</code> | <code>Estimated 2016 expenditures The total estimated government spending in California in fiscal year 2016 was $265.9 billion. Per-capita figures are calculated by taking the state's total spending and dividing by the number of state residents according to United States Census Bureau estimates.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### sentence-transformers/gooaq
* Dataset: [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 10,000 evaluation samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.86 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.82 tokens</li><li>max: 166 tokens</li></ul> |
* Samples:
| question | answer |
|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>how to open nx file?</code> | <code>['Click File > Open. The File Open dialog box opens.', 'Select NX File (*. prt) in the Type box. ... ', 'Select an NX . ... ', 'Select Import in the File Open dialog box. ... ', 'If you do not want to retain the import profile in use, select an import profile from the Profile list. ... ', 'Click OK in the Import New Model dialog box.']</code> |
| <code>how to recover deleted photos from blackberry priv?</code> | <code>['Run Android Data Recovery. ... ', 'Enable USB Debugging Mode. ... ', 'Scan Your BlackBerry PRIV to Find Deleted Photos. ... ', 'Recover Deleted Photos from BlackBerry PRIV.']</code> |
| <code>which subatomic particles are found within the nucleus of an atom?</code> | <code>In the middle of every atom is the nucleus. The nucleus contains two types of subatomic particles, protons and neutrons. The protons have a positive electrical charge and the neutrons have no electrical charge. A third type of subatomic particle, electrons, move around the nucleus.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 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`: 2e-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
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: 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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | loss | gooaq-dev_cosine_map@100 |
|:------:|:-----:|:-------------:|:------:|:------------------------:|
| 0 | 0 | - | - | 0.1379 |
| 0.0000 | 1 | 3.6452 | - | - |
| 0.0053 | 250 | 2.4418 | - | - |
| 0.0107 | 500 | 0.373 | - | - |
| 0.0160 | 750 | 0.183 | - | - |
| 0.0213 | 1000 | 0.1286 | 0.0805 | 0.6796 |
| 0.0266 | 1250 | 0.1099 | - | - |
| 0.0320 | 1500 | 0.091 | - | - |
| 0.0373 | 1750 | 0.0768 | - | - |
| 0.0426 | 2000 | 0.0665 | 0.0526 | 0.7162 |
| 0.0480 | 2250 | 0.0659 | - | - |
| 0.0533 | 2500 | 0.0602 | - | - |
| 0.0586 | 2750 | 0.0548 | - | - |
| 0.0639 | 3000 | 0.0543 | 0.0426 | 0.7328 |
| 0.0693 | 3250 | 0.0523 | - | - |
| 0.0746 | 3500 | 0.0494 | - | - |
| 0.0799 | 3750 | 0.0468 | - | - |
| 0.0853 | 4000 | 0.0494 | 0.0362 | 0.7450 |
| 0.0906 | 4250 | 0.048 | - | - |
| 0.0959 | 4500 | 0.0442 | - | - |
| 0.1012 | 4750 | 0.0442 | - | - |
| 0.1066 | 5000 | 0.0408 | 0.0332 | 0.7519 |
| 0.1119 | 5250 | 0.0396 | - | - |
| 0.1172 | 5500 | 0.0379 | - | - |
| 0.1226 | 5750 | 0.0392 | - | - |
| 0.1279 | 6000 | 0.0395 | 0.0300 | 0.7505 |
| 0.1332 | 6250 | 0.0349 | - | - |
| 0.1386 | 6500 | 0.0383 | - | - |
| 0.1439 | 6750 | 0.0335 | - | - |
| 0.1492 | 7000 | 0.0323 | 0.0253 | 0.7624 |
| 0.1545 | 7250 | 0.0342 | - | - |
| 0.1599 | 7500 | 0.0292 | - | - |
| 0.1652 | 7750 | 0.0309 | - | - |
| 0.1705 | 8000 | 0.0335 | 0.0249 | 0.7631 |
| 0.1759 | 8250 | 0.0304 | - | - |
| 0.1812 | 8500 | 0.0318 | - | - |
| 0.1865 | 8750 | 0.0271 | - | - |
| 0.1918 | 9000 | 0.029 | 0.0230 | 0.7615 |
| 0.1972 | 9250 | 0.0309 | - | - |
| 0.2025 | 9500 | 0.0305 | - | - |
| 0.2078 | 9750 | 0.0237 | - | - |
| 0.2132 | 10000 | 0.0274 | 0.0220 | 0.7667 |
| 0.2185 | 10250 | 0.0248 | - | - |
| 0.2238 | 10500 | 0.0249 | - | - |
| 0.2291 | 10750 | 0.0272 | - | - |
| 0.2345 | 11000 | 0.0289 | 0.0230 | 0.7664 |
| 0.2398 | 11250 | 0.027 | - | - |
| 0.2451 | 11500 | 0.0259 | - | - |
| 0.2505 | 11750 | 0.0237 | - | - |
| 0.2558 | 12000 | 0.0245 | 0.0220 | 0.7694 |
| 0.2611 | 12250 | 0.0251 | - | - |
| 0.2664 | 12500 | 0.0243 | - | - |
| 0.2718 | 12750 | 0.0229 | - | - |
| 0.2771 | 13000 | 0.0273 | 0.0201 | 0.7725 |
| 0.2824 | 13250 | 0.0244 | - | - |
| 0.2878 | 13500 | 0.0248 | - | - |
| 0.2931 | 13750 | 0.0255 | - | - |
| 0.2984 | 14000 | 0.0244 | 0.0192 | 0.7729 |
| 0.3037 | 14250 | 0.0242 | - | - |
| 0.3091 | 14500 | 0.0235 | - | - |
| 0.3144 | 14750 | 0.0231 | - | - |
| 0.3197 | 15000 | 0.0228 | 0.0190 | 0.7823 |
| 0.3251 | 15250 | 0.0229 | - | - |
| 0.3304 | 15500 | 0.0224 | - | - |
| 0.3357 | 15750 | 0.0216 | - | - |
| 0.3410 | 16000 | 0.0218 | 0.0186 | 0.7787 |
| 0.3464 | 16250 | 0.022 | - | - |
| 0.3517 | 16500 | 0.0233 | - | - |
| 0.3570 | 16750 | 0.0216 | - | - |
| 0.3624 | 17000 | 0.0226 | 0.0169 | 0.7862 |
| 0.3677 | 17250 | 0.0215 | - | - |
| 0.3730 | 17500 | 0.0212 | - | - |
| 0.3784 | 17750 | 0.0178 | - | - |
| 0.3837 | 18000 | 0.0217 | 0.0161 | 0.7813 |
| 0.3890 | 18250 | 0.0217 | - | - |
| 0.3943 | 18500 | 0.0191 | - | - |
| 0.3997 | 18750 | 0.0216 | - | - |
| 0.4050 | 19000 | 0.022 | 0.0157 | 0.7868 |
| 0.4103 | 19250 | 0.0223 | - | - |
| 0.4157 | 19500 | 0.021 | - | - |
| 0.4210 | 19750 | 0.0176 | - | - |
| 0.4263 | 20000 | 0.021 | 0.0162 | 0.7873 |
| 0.4316 | 20250 | 0.0206 | - | - |
| 0.4370 | 20500 | 0.0196 | - | - |
| 0.4423 | 20750 | 0.0186 | - | - |
| 0.4476 | 21000 | 0.0197 | 0.0158 | 0.7907 |
| 0.4530 | 21250 | 0.0156 | - | - |
| 0.4583 | 21500 | 0.0178 | - | - |
| 0.4636 | 21750 | 0.0175 | - | - |
| 0.4689 | 22000 | 0.0187 | 0.0151 | 0.7937 |
| 0.4743 | 22250 | 0.0182 | - | - |
| 0.4796 | 22500 | 0.0185 | - | - |
| 0.4849 | 22750 | 0.0217 | - | - |
| 0.4903 | 23000 | 0.0179 | 0.0156 | 0.7937 |
| 0.4956 | 23250 | 0.0193 | - | - |
| 0.5009 | 23500 | 0.015 | - | - |
| 0.5062 | 23750 | 0.0181 | - | - |
| 0.5116 | 24000 | 0.0173 | 0.0150 | 0.7924 |
| 0.5169 | 24250 | 0.0177 | - | - |
| 0.5222 | 24500 | 0.0183 | - | - |
| 0.5276 | 24750 | 0.0171 | - | - |
| 0.5329 | 25000 | 0.0185 | 0.0140 | 0.7955 |
| 0.5382 | 25250 | 0.0178 | - | - |
| 0.5435 | 25500 | 0.015 | - | - |
| 0.5489 | 25750 | 0.017 | - | - |
| 0.5542 | 26000 | 0.0171 | 0.0139 | 0.7931 |
| 0.5595 | 26250 | 0.0164 | - | - |
| 0.5649 | 26500 | 0.0175 | - | - |
| 0.5702 | 26750 | 0.0175 | - | - |
| 0.5755 | 27000 | 0.0163 | 0.0133 | 0.7954 |
| 0.5809 | 27250 | 0.0179 | - | - |
| 0.5862 | 27500 | 0.016 | - | - |
| 0.5915 | 27750 | 0.0155 | - | - |
| 0.5968 | 28000 | 0.0162 | 0.0138 | 0.7979 |
| 0.6022 | 28250 | 0.0164 | - | - |
| 0.6075 | 28500 | 0.0148 | - | - |
| 0.6128 | 28750 | 0.0152 | - | - |
| 0.6182 | 29000 | 0.0166 | 0.0134 | 0.7987 |
| 0.6235 | 29250 | 0.0159 | - | - |
| 0.6288 | 29500 | 0.0168 | - | - |
| 0.6341 | 29750 | 0.0187 | - | - |
| 0.6395 | 30000 | 0.017 | 0.0137 | 0.7980 |
| 0.6448 | 30250 | 0.0168 | - | - |
| 0.6501 | 30500 | 0.0149 | - | - |
| 0.6555 | 30750 | 0.0159 | - | - |
| 0.6608 | 31000 | 0.0149 | 0.0131 | 0.8017 |
| 0.6661 | 31250 | 0.0149 | - | - |
| 0.6714 | 31500 | 0.0147 | - | - |
| 0.6768 | 31750 | 0.0157 | - | - |
| 0.6821 | 32000 | 0.0151 | 0.0125 | 0.8011 |
| 0.6874 | 32250 | 0.015 | - | - |
| 0.6928 | 32500 | 0.0157 | - | - |
| 0.6981 | 32750 | 0.0153 | - | - |
| 0.7034 | 33000 | 0.0141 | 0.0123 | 0.8012 |
| 0.7087 | 33250 | 0.0143 | - | - |
| 0.7141 | 33500 | 0.0121 | - | - |
| 0.7194 | 33750 | 0.0164 | - | - |
| 0.7247 | 34000 | 0.014 | 0.0121 | 0.8014 |
| 0.7301 | 34250 | 0.0147 | - | - |
| 0.7354 | 34500 | 0.0149 | - | - |
| 0.7407 | 34750 | 0.014 | - | - |
| 0.7460 | 35000 | 0.0156 | 0.0117 | 0.8022 |
| 0.7514 | 35250 | 0.0153 | - | - |
| 0.7567 | 35500 | 0.0146 | - | - |
| 0.7620 | 35750 | 0.0144 | - | - |
| 0.7674 | 36000 | 0.0139 | 0.0111 | 0.8035 |
| 0.7727 | 36250 | 0.0134 | - | - |
| 0.7780 | 36500 | 0.013 | - | - |
| 0.7833 | 36750 | 0.0156 | - | - |
| 0.7887 | 37000 | 0.0144 | 0.0108 | 0.8048 |
| 0.7940 | 37250 | 0.0133 | - | - |
| 0.7993 | 37500 | 0.0154 | - | - |
| 0.8047 | 37750 | 0.0132 | - | - |
| 0.8100 | 38000 | 0.013 | 0.0108 | 0.8063 |
| 0.8153 | 38250 | 0.0126 | - | - |
| 0.8207 | 38500 | 0.0135 | - | - |
| 0.8260 | 38750 | 0.014 | - | - |
| 0.8313 | 39000 | 0.013 | 0.0109 | 0.8086 |
| 0.8366 | 39250 | 0.0136 | - | - |
| 0.8420 | 39500 | 0.0141 | - | - |
| 0.8473 | 39750 | 0.0155 | - | - |
| 0.8526 | 40000 | 0.0153 | 0.0106 | 0.8075 |
| 0.8580 | 40250 | 0.0131 | - | - |
| 0.8633 | 40500 | 0.0128 | - | - |
| 0.8686 | 40750 | 0.013 | - | - |
| 0.8739 | 41000 | 0.0133 | 0.0109 | 0.8060 |
| 0.8793 | 41250 | 0.0119 | - | - |
| 0.8846 | 41500 | 0.0144 | - | - |
| 0.8899 | 41750 | 0.0142 | - | - |
| 0.8953 | 42000 | 0.0138 | 0.0105 | 0.8083 |
| 0.9006 | 42250 | 0.014 | - | - |
| 0.9059 | 42500 | 0.0134 | - | - |
| 0.9112 | 42750 | 0.0134 | - | - |
| 0.9166 | 43000 | 0.0124 | 0.0106 | 0.8113 |
| 0.9219 | 43250 | 0.0122 | - | - |
| 0.9272 | 43500 | 0.0126 | - | - |
| 0.9326 | 43750 | 0.0121 | - | - |
| 0.9379 | 44000 | 0.0137 | 0.0103 | 0.8105 |
| 0.9432 | 44250 | 0.0132 | - | - |
| 0.9485 | 44500 | 0.012 | - | - |
| 0.9539 | 44750 | 0.0136 | - | - |
| 0.9592 | 45000 | 0.0133 | 0.0104 | 0.8112 |
| 0.9645 | 45250 | 0.0118 | - | - |
| 0.9699 | 45500 | 0.0132 | - | - |
| 0.9752 | 45750 | 0.0118 | - | - |
| 0.9805 | 46000 | 0.012 | 0.0102 | 0.8104 |
| 0.9858 | 46250 | 0.0127 | - | - |
| 0.9912 | 46500 | 0.0134 | - | - |
| 0.9965 | 46750 | 0.0121 | - | - |
| 1.0 | 46914 | - | - | 0.8111 |
</details>
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 1.637 kWh
- **Carbon Emitted**: 0.636 kg of CO2
- **Hours Used**: 4.514 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: 3.1.0.dev0
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- Datasets: 2.19.1
- 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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