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mpnet-base-gooaq-hard-negatives

Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
Generated from Trainer
dataset_size:2286783
loss:MultipleNegativesRankingLoss
Eval Results
Carbon Emissions
Inference Endpoints
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---
base_model: microsoft/mpnet-base
datasets:
- tomaarsen/gooaq-hard-negatives
- sentence-transformers/gooaq
language:
- en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2286783
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: how to download a youtube video onto usb?
sentences:
- Copy YouTube URL to Download Go to YouTube video you want to download to USB and
copy its URL. Paste the link to download YouTube. Choose a necessary video or
audio format and quality.
- Before surgeons are qualified to operate, they must meet a set of challenging
education requirements. These generally include four years of undergraduate study,
four years of medical school leading to a Doctor of Medicine (M.D.) degree, and
three to eight years of surgical residency at a hospital.
- A Roman numeral representing the number eighteen (18).
- source_sentence: what is the best diet for a leaky gut?
sentences:
- When a woman is pregnant, she does not continue to ovulate and will not have a
period. Menstruation only occurs when a person is not pregnant. Although it is
possible for women to experience some bleeding during pregnancy, this will not
be due to their menstrual cycle.
- To combat leaky gut, eat foods that promote the growth of healthy gut bacteria,
including fruits, cultured dairy products, healthy fats, lean meats, and fibrous
and fermented vegetables.
- Popcorn Ceiling vs Asbestos Popcorn Ceiling Removal Cost CostHelper says Popcorn
ceilings not containing asbestos can expect to pay about $1 to $3 per square foot
or $250 to $900 to remove a popcorn ceiling from a 15'x20' room or $1,200 to $1,400
for a 1,6000 sq.
- source_sentence: what is the difference between joint tenancy and common tenancy?
sentences:
- You (TV series) You is an American psychological thriller television series developed
by Greg Berlanti and Sera Gamble. ... In December 2018, it was announced that
the series would move to Netflix as a Netflix Original title. The second season
was released exclusively on Netflix on December 26, 2019.
- A normal resting heart rate range is between 60 and 100 bpm.
- Joint tenancy also differs from tenancy in common because when one joint tenant
dies, the other remaining joint tenants inherit the deceased tenant's interest
in the property. However, a joint tenancy does allow owners to sell their interests.
If one owner sells, the tenancy is converted to a tenancy in common.
- source_sentence: what is the cause of blood clots in urine?
sentences:
- If sufficient blood is present in the urine, the blood may form a clot. The clot
can completely block the flow of urine, causing sudden extreme pain and inability
to urinate. Bleeding severe enough to cause such a clot is usually caused by an
injury to the urinary tract.
- Distance is the magnitude (length) of the displacement vector. Path length is
how far the object moved as it traveled from its initial position to its final
position.
- In fact, the brand is consistently ranked near the top of automakers in terms
of the most expensive cars to maintain. The total maintenance costs of the average
Audi over a 10-year span is $12,400. ... All cars are different, and many require
more maintenance than some depending on their age and driving history.
- source_sentence: are hard seltzers malt liquor?
sentences:
- The BCD method measures the distance from the apex of the breast down to the wire
line directly below it. That measurement in inches will determine your cup and
frame size. Then take your Rib Cage measurement directly under your bra. ... For
example, the BCD might be 4.0 and the Rib Cage of 32.
- Seltzer is carbonated water. “Hard seltzer” is a flavored malt beverage — essentially
the same as a Lime-A-Rita or a Colt 45 or a Smirnoff Ice. These products derive
their alcohol from fermented malted grains and are then carbonated, flavored and
sweetened.
- Bleaching action of chlorine is based on oxidation while that of sulphur is based
on reduction. Chlorine acts with water to produce nascent oxygen. ... Sulphour
dioxide removes oxygen from the coloured substance and makes it colourless.
co2_eq_emissions:
emissions: 1550.677005890232
energy_consumed: 3.989372336366245
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: 11.599
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: MPNet base trained on GooAQ triplets with hard negatives
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: gooaq dev
type: gooaq-dev
metrics:
- type: cosine_accuracy@1
value: 0.7413
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8697
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9055
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9427
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7413
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2899
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1811
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09427000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7413
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8697
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9055
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9427
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8441925656083314
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8123759920634883
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8147743017171518
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.7384
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.8669
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9039
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9389
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.7384
name: Dot Precision@1
- type: dot_precision@3
value: 0.28896666666666665
name: Dot Precision@3
- type: dot_precision@5
value: 0.18078000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.09389000000000002
name: Dot Precision@10
- type: dot_recall@1
value: 0.7384
name: Dot Recall@1
- type: dot_recall@3
value: 0.8669
name: Dot Recall@3
- type: dot_recall@5
value: 0.9039
name: Dot Recall@5
- type: dot_recall@10
value: 0.9389
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8410831459293242
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8094504365079324
name: Dot Mrr@10
- type: dot_map@100
value: 0.8120497186357559
name: Dot Map@100
---
# MPNet base trained on GooAQ triplets with hard negatives
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [train](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives) 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:** [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:**
- [train](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives)
- **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-hard-negatives")
# Run inference
sentences = [
'are hard seltzers malt liquor?',
'Seltzer is carbonated water. “Hard seltzer” is a flavored malt beverage — essentially the same as a Lime-A-Rita or a Colt 45 or a Smirnoff Ice. These products derive their alcohol from fermented malted grains and are then carbonated, flavored and sweetened.',
'Bleaching action of chlorine is based on oxidation while that of sulphur is based on reduction. Chlorine acts with water to produce nascent oxygen. ... Sulphour dioxide removes oxygen from the coloured substance and makes it colourless.',
]
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>
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## 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 | 0.7413 |
| cosine_accuracy@3 | 0.8697 |
| cosine_accuracy@5 | 0.9055 |
| cosine_accuracy@10 | 0.9427 |
| cosine_precision@1 | 0.7413 |
| cosine_precision@3 | 0.2899 |
| cosine_precision@5 | 0.1811 |
| cosine_precision@10 | 0.0943 |
| cosine_recall@1 | 0.7413 |
| cosine_recall@3 | 0.8697 |
| cosine_recall@5 | 0.9055 |
| cosine_recall@10 | 0.9427 |
| cosine_ndcg@10 | 0.8442 |
| cosine_mrr@10 | 0.8124 |
| **cosine_map@100** | **0.8148** |
| dot_accuracy@1 | 0.7384 |
| dot_accuracy@3 | 0.8669 |
| dot_accuracy@5 | 0.9039 |
| dot_accuracy@10 | 0.9389 |
| dot_precision@1 | 0.7384 |
| dot_precision@3 | 0.289 |
| dot_precision@5 | 0.1808 |
| dot_precision@10 | 0.0939 |
| dot_recall@1 | 0.7384 |
| dot_recall@3 | 0.8669 |
| dot_recall@5 | 0.9039 |
| dot_recall@10 | 0.9389 |
| dot_ndcg@10 | 0.8411 |
| dot_mrr@10 | 0.8095 |
| dot_map@100 | 0.812 |
<!--
## 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
#### train
* Dataset: [train](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives) at [87594a1](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives/tree/87594a1e6c58e88b5843afa9da3a97ffd75d01c2)
* Size: 2,286,783 training samples
* Columns: <code>question</code>, <code>answer</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, and <code>negative_5</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string | string | string | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.84 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 59.41 tokens</li><li>max: 158 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 59.09 tokens</li><li>max: 139 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 58.61 tokens</li><li>max: 139 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 58.98 tokens</li><li>max: 173 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 59.43 tokens</li><li>max: 137 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 60.03 tokens</li><li>max: 146 tokens</li></ul> |
* Samples:
| question | answer | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 |
|:---------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>is toprol xl the same as metoprolol?</code> | <code>Metoprolol succinate is also known by the brand name Toprol XL. It is the extended-release form of metoprolol. Metoprolol succinate is approved to treat high blood pressure, chronic chest pain, and congestive heart failure.</code> | <code>Secondly, metoprolol and metoprolol ER have different brand-name equivalents: Brand version of metoprolol: Lopressor. Brand version of metoprolol ER: Toprol XL.</code> | <code>Pill with imprint 1 is White, Round and has been identified as Metoprolol Tartrate 25 mg.</code> | <code>Interactions between your drugs No interactions were found between Allergy Relief and metoprolol. This does not necessarily mean no interactions exist. Always consult your healthcare provider.</code> | <code>Metoprolol is a type of medication called a beta blocker. It works by relaxing blood vessels and slowing heart rate, which improves blood flow and lowers blood pressure. Metoprolol can also improve the likelihood of survival after a heart attack.</code> | <code>Metoprolol starts to work after about 2 hours, but it can take up to 1 week to fully take effect. You may not feel any different when you take metoprolol, but this doesn't mean it's not working. It's important to keep taking your medicine.</code> |
| <code>are you experienced cd steve hoffman?</code> | <code>The Are You Experienced album was apparently mastered from the original stereo UK master tapes (according to Steve Hoffman - one of the very few who has heard both the master tapes and the CDs produced over the years). ... The CD booklets were a little sparse, but at least they stayed true to the album's original design.</code> | <code>I Saw the Light. Showcasing the unique talent and musical influence of country-western artist Hank Williams, this candid biography also sheds light on the legacy of drug abuse and tormented relationships that contributes to the singer's legend.</code> | <code>(Read our ranking of his top 10.) And while Howard dresses the part of director, any notion of him as a tortured auteur or dictatorial taskmasker — the clichés of the Hollywood director — are tossed aside. He's very nice.</code> | <code>He was a music star too. Where're you people born and brought up? We 're born and brought up here in Anambra State at Nkpor town, near Onitsha.</code> | <code>At the age of 87 he has now retired from his live shows and all the traveling involved. And although he still picks up his Martin Guitar and does a show now and then, his life is now devoted to writing his memoirs.</code> | <code>The owner of the mysterious voice behind all these videos is a man who's seen a lot, visiting a total of 56 intimate celebrity spaces over the course of five years. His name is Joe Sabia — that's him in the photo — and he's currently the VP of creative development at Condé Nast Entertainment.</code> |
| <code>how are babushka dolls made?</code> | <code>Matryoshka dolls are made of wood from lime, balsa, alder, aspen, and birch trees; lime is probably the most common wood type. ... After cutting, the trees are stripped of most of their bark, although a few inner rings of bark are left to bind the wood and keep it from splitting.</code> | <code>A quick scan of the auction and buy-it-now listings on eBay finds porcelain doll values ranging from around $5 and $10 to several thousand dollars or more but no dolls listed above $10,000.</code> | <code>Japanese dolls are called as ningyō in Japanese and literally translates to 'human form'.</code> | <code>Matyoo: All Fresno Girl dolls come just as real children are born.</code> | <code>As of September 2016, there are over 100 characters. The main toy line includes 13-inch Dolls, the mini-series, and a variety of mini play-sets and plush dolls as well as Lalaloopsy Littles, smaller siblings of the 13-inch dolls. A spin-off known as "Lala-Oopsies" came out in late 2012.</code> | <code>LOL dolls are little baby dolls that come wrapped inside a surprise toy ball. Each ball has layers that contain stickers, secret messages, mix and match accessories–and finally–a doll. ... The doll on the ball is almost never the doll inside. Dolls are released in series, so not every doll is available all the time.</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: 11.89 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 59.65 tokens</li><li>max: 131 tokens</li></ul> |
* Samples:
| question | answer |
|:-------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>how to transfer data from ipad to usb?</code> | <code>First, in “Locations,” tap the “On My iPhone” or “On My iPad” section. Here, tap and hold the empty space, and then select “New Folder.” Name it, and then tap “Done” to create a new folder for the files you want to transfer. Now, from the “Locations” section, select your USB flash drive.</code> |
| <code>what quorn products are syn free?</code> | <code>['bacon style pieces.', 'bacon style rashers, chilled.', 'BBQ sliced fillets.', 'beef style and red onion burgers.', 'pieces.', 'chicken style slices.', 'fajita strips.', 'family roast.']</code> |
| <code>what is the difference between turmeric ginger?</code> | <code>Ginger offers a sweet and spicy zing to dishes. Turmeric provides a golden yellow colour and a warm and bitter taste with a peppery aroma.</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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `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 | - | - | 0.1405 |
| 0.2869 | 20500 | 0.5303 | - | - |
| 0.2939 | 21000 | 0.5328 | - | - |
| 0.3009 | 21500 | 0.515 | - | - |
| 0.3079 | 22000 | 0.5264 | 0.0297 | 0.7919 |
| 0.3149 | 22500 | 0.5189 | - | - |
| 0.3218 | 23000 | 0.5284 | - | - |
| 0.3288 | 23500 | 0.5308 | - | - |
| 0.3358 | 24000 | 0.509 | 0.0281 | 0.7932 |
| 0.3428 | 24500 | 0.5074 | - | - |
| 0.3498 | 25000 | 0.5196 | - | - |
| 0.3568 | 25500 | 0.5041 | - | - |
| 0.3638 | 26000 | 0.4976 | 0.0291 | 0.7950 |
| 0.3708 | 26500 | 0.5025 | - | - |
| 0.3778 | 27000 | 0.5175 | - | - |
| 0.3848 | 27500 | 0.4921 | - | - |
| 0.3918 | 28000 | 0.4924 | 0.0298 | 0.7938 |
| 0.3988 | 28500 | 0.49 | - | - |
| 0.4058 | 29000 | 0.4924 | - | - |
| 0.4128 | 29500 | 0.4902 | - | - |
| 0.4198 | 30000 | 0.4846 | 0.0269 | 0.7966 |
| 0.4268 | 30500 | 0.4815 | - | - |
| 0.4338 | 31000 | 0.4881 | - | - |
| 0.4408 | 31500 | 0.4848 | - | - |
| 0.4478 | 32000 | 0.4882 | 0.0264 | 0.8004 |
| 0.4548 | 32500 | 0.4809 | - | - |
| 0.4618 | 33000 | 0.4896 | - | - |
| 0.4688 | 33500 | 0.4744 | - | - |
| 0.4758 | 34000 | 0.4827 | 0.0252 | 0.8038 |
| 0.4828 | 34500 | 0.4703 | - | - |
| 0.4898 | 35000 | 0.4765 | - | - |
| 0.4968 | 35500 | 0.4625 | - | - |
| 0.5038 | 36000 | 0.4698 | 0.0269 | 0.8025 |
| 0.5108 | 36500 | 0.4666 | - | - |
| 0.5178 | 37000 | 0.4594 | - | - |
| 0.5248 | 37500 | 0.4621 | - | - |
| 0.5318 | 38000 | 0.4538 | 0.0266 | 0.8047 |
| 0.5387 | 38500 | 0.4576 | - | - |
| 0.5457 | 39000 | 0.4594 | - | - |
| 0.5527 | 39500 | 0.4503 | - | - |
| 0.5597 | 40000 | 0.4538 | 0.0265 | 0.8038 |
| 0.5667 | 40500 | 0.4521 | - | - |
| 0.5737 | 41000 | 0.4575 | - | - |
| 0.5807 | 41500 | 0.4544 | - | - |
| 0.5877 | 42000 | 0.4462 | 0.0245 | 0.8077 |
| 0.5947 | 42500 | 0.4491 | - | - |
| 0.6017 | 43000 | 0.4651 | - | - |
| 0.6087 | 43500 | 0.4549 | - | - |
| 0.6157 | 44000 | 0.4461 | 0.0262 | 0.8046 |
| 0.6227 | 44500 | 0.4571 | - | - |
| 0.6297 | 45000 | 0.4478 | - | - |
| 0.6367 | 45500 | 0.4482 | - | - |
| 0.6437 | 46000 | 0.4439 | 0.0244 | 0.8070 |
| 0.6507 | 46500 | 0.4384 | - | - |
| 0.6577 | 47000 | 0.446 | - | - |
| 0.6647 | 47500 | 0.4425 | - | - |
| 0.6717 | 48000 | 0.4308 | 0.0248 | 0.8067 |
| 0.6787 | 48500 | 0.4374 | - | - |
| 0.6857 | 49000 | 0.4342 | - | - |
| 0.6927 | 49500 | 0.4455 | - | - |
| 0.6997 | 50000 | 0.4322 | 0.0242 | 0.8077 |
| 0.7067 | 50500 | 0.4288 | - | - |
| 0.7137 | 51000 | 0.4317 | - | - |
| 0.7207 | 51500 | 0.4295 | - | - |
| 0.7277 | 52000 | 0.4291 | 0.0231 | 0.8130 |
| 0.7347 | 52500 | 0.4279 | - | - |
| 0.7417 | 53000 | 0.4287 | - | - |
| 0.7486 | 53500 | 0.4252 | - | - |
| 0.7556 | 54000 | 0.4341 | 0.0243 | 0.8112 |
| 0.7626 | 54500 | 0.419 | - | - |
| 0.7696 | 55000 | 0.4323 | - | - |
| 0.7766 | 55500 | 0.4252 | - | - |
| 0.7836 | 56000 | 0.4313 | 0.0264 | 0.8107 |
| 0.7906 | 56500 | 0.4222 | - | - |
| 0.7976 | 57000 | 0.4226 | - | - |
| 0.8046 | 57500 | 0.4152 | - | - |
| 0.8116 | 58000 | 0.4222 | 0.0236 | 0.8131 |
| 0.8186 | 58500 | 0.4184 | - | - |
| 0.8256 | 59000 | 0.4144 | - | - |
| 0.8326 | 59500 | 0.4242 | - | - |
| 0.8396 | 60000 | 0.4148 | 0.0242 | 0.8125 |
| 0.8466 | 60500 | 0.4222 | - | - |
| 0.8536 | 61000 | 0.4184 | - | - |
| 0.8606 | 61500 | 0.4138 | - | - |
| 0.8676 | 62000 | 0.4119 | 0.0240 | 0.8133 |
| 0.8746 | 62500 | 0.411 | - | - |
| 0.8816 | 63000 | 0.4172 | - | - |
| 0.8886 | 63500 | 0.4145 | - | - |
| 0.8956 | 64000 | 0.4168 | 0.0240 | 0.8137 |
| 0.9026 | 64500 | 0.4071 | - | - |
| 0.9096 | 65000 | 0.4119 | - | - |
| 0.9166 | 65500 | 0.403 | - | - |
| 0.9236 | 66000 | 0.4092 | 0.0238 | 0.8141 |
| 0.9306 | 66500 | 0.4079 | - | - |
| 0.9376 | 67000 | 0.4129 | - | - |
| 0.9446 | 67500 | 0.4082 | - | - |
| 0.9516 | 68000 | 0.4054 | 0.0235 | 0.8149 |
| 0.9586 | 68500 | 0.4129 | - | - |
| 0.9655 | 69000 | 0.4085 | - | - |
| 0.9725 | 69500 | 0.414 | - | - |
| 0.9795 | 70000 | 0.4075 | 0.0239 | 0.8142 |
| 0.9865 | 70500 | 0.4104 | - | - |
| 0.9935 | 71000 | 0.4087 | - | - |
| 1.0 | 71462 | - | - | 0.8148 |
</details>
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 3.989 kWh
- **Carbon Emitted**: 1.551 kg of CO2
- **Hours Used**: 11.599 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: 0.31.0
- Datasets: 2.20.0
- 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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