---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3012496
- loss:MultipleNegativesRankingLoss
base_model: nreimers/MiniLM-L6-H384-uncased
widget:
- source_sentence: what is the current old age pension uk?
sentences:
- Unlike divorce, a legal separation does not put an end to the marriage, it enables
you to live separately but remain married. ... Issues that can be addressed in
a separation agreement are division of assets and debts, child custody and support,
visitation schedules and spousal support.
- The full basic State Pension is £134.25 per week. There are ways you can increase
your State Pension up to or above the full amount. You may have to pay tax on
your State Pension. To get information about your State Pension, contact the Pension
Service.
- Most often, chili seasoning is a mix of 5-8 spices including chili powder, cumin,
garlic, oregano, and others. Chili seasoning is similar to homemade taco seasoning
and fajita seasoning, with many of the same ingredients but has more of an emphasis
on chili powder.
- source_sentence: how to calculate percentage of ratio?
sentences:
- Ratios are often expressed in the form m:n or m/n. To convert a ratio into the
form of a percentage, simply divide m by n and then multiply the result by 100.
For example, If the ratio is 12:4, convert it to the form 12/4, which is an equation
we can solve. After that multiply the result by 100 to get the percentage.
- For anyone new to Roblox here's a quick explanation as to what an obby is. An
obby is, quite simply, an obstacle course that you need to get around in order
to complete it. They can include jumps, climbing, guessing games and trampolines
to name just a few obstacles.
- “relative” means, with respect to a public official, an individual who is related
to the public official as father, mother, son, daughter, brother, sister, uncle,
aunt, first cousin, nephew, niece, husband, wife, father-in-law, mother-in-law,
son-in-law, daughter-in-law, brother-in-law, sister-in-law, stepfather, ...
- source_sentence: if you block someone on facebook do you lose your messages?
sentences:
- 1 Answer. If you block someone on Facebook or messenger, you both will not be
able to each others activities and also not be able to send messages. Old conversation
will be still in inbox but name of that person will not be clickable.
- Your hourly wage of 37 dollars would end up being about $76,960 per year in salary.
- '[''Tap Download while watching a video in the YouTube app.'', ''Tap Library to
find your downloads.'', ''Tap Downloads. From here, you can tap the More button
(the three dots) to delete videos from your device.'']'
- source_sentence: fifa 20 how to drag back?
sentences:
- Component is a directive which use shadow DOM to create encapsulate visual behavior
called components. Components are typically used to create UI widgets. Directives
is used to add behavior to an existing DOM element. Component is used to break
up the application into smaller components.
- Enabling debug output in LWIP To enable specific debug messages in LWIP, just
set the specific define value for the LWIP *_DEBUG value to " LWIP_DBG_ON". A
full list of debug defines that can be enabled can be found in the opts. h file.
Just copy the defines for the debug messages you want to enable into the lwipopts.
- Drag Back (2 Star Skill Move) The drag back has been a popular skill move in FIFA
for years now, and remains highly effective in FIFA 20. Again, it's fairly simple
- hold the RB or R1 button, and then push the left stick away from the direction
you're facing to drag the ball backwards.
- source_sentence: is jordyn a boy or girl?
sentences:
- 'Gender Popularity of the Name "Jordyn" Jordyn: It''s a girl! Since 1880, a total
of 2,696 boys have been given the name Jordyn while 39,618 girls were named Jordyn.'
- Temporary Infertility After Depo But not every woman will get their cycle back
5 months after the last injection. In some cases, it may take up to 22 months—or
almost two years—for fertility to return after the last injection.
- Currently there is no research showing that juice cleanses are beneficial to weight
loss or that they should be recommended at all. Even though it is possible to
cut a significant amount of calories by only drinking juice, you could also be
missing out on some essential nutrition - like protein, fiber and healthy fats.
datasets:
- sentence-transformers/gooaq
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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
co2_eq_emissions:
emissions: 22.00215266567056
energy_consumed: 0.056604166342520905
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: 0.206
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: MPNet base trained on AllNLI triplets
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: gooaq dev
type: gooaq-dev
metrics:
- type: cosine_accuracy@1
value: 0.5589
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7234
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7801
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8456
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5589
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2411333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15602000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08456
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5589
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7234
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7801
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8456
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7000016898403962
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6536087301587268
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.659379113770559
name: Cosine Map@100
---
# MPNet base trained on AllNLI triplets
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 384-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:** [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) <!-- at revision 3276f0fac9d818781d7a1327b3ff818fc4e643c0 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [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: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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/MiniLM-L6-H384-uncased-gooaq-no-asym")
# Run inference
sentences = [
'is jordyn a boy or girl?',
'Gender Popularity of the Name "Jordyn" Jordyn: It\'s a girl! Since 1880, a total of 2,696 boys have been given the name Jordyn while 39,618 girls were named Jordyn.',
'Currently there is no research showing that juice cleanses are beneficial to weight loss or that they should be recommended at all. Even though it is possible to cut a significant amount of calories by only drinking juice, you could also be missing out on some essential nutrition - like protein, fiber and healthy fats.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# 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 | 0.5589 |
| cosine_accuracy@3 | 0.7234 |
| cosine_accuracy@5 | 0.7801 |
| cosine_accuracy@10 | 0.8456 |
| cosine_precision@1 | 0.5589 |
| cosine_precision@3 | 0.2411 |
| cosine_precision@5 | 0.156 |
| cosine_precision@10 | 0.0846 |
| cosine_recall@1 | 0.5589 |
| cosine_recall@3 | 0.7234 |
| cosine_recall@5 | 0.7801 |
| cosine_recall@10 | 0.8456 |
| **cosine_ndcg@10** | **0.7** |
| cosine_mrr@10 | 0.6536 |
| cosine_map@100 | 0.6594 |
<!--
## 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
#### gooaq
* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 3,012,496 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: 11.86 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.48 tokens</li><li>max: 138 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is the difference between broilers and layers?</code> | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> |
| <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> |
| <code>is kamagra same as viagra?</code> | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</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
#### gooaq
* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 3,012,496 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: 11.88 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 61.03 tokens</li><li>max: 127 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>how do i program my directv remote with my tv?</code> | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> |
| <code>are rodrigues fruit bats nocturnal?</code> | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> |
| <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</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`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 24
- `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`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `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`: 24
- `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`: 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
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `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
| Epoch | Step | Training Loss | Validation Loss | gooaq-dev_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:------------------------:|
| -1 | -1 | - | - | 0.0303 |
| 0.0003 | 1 | 4.2106 | - | - |
| 0.0128 | 50 | 4.1241 | - | - |
| 0.0256 | 100 | 3.3791 | - | - |
| 0.0384 | 150 | 1.8925 | - | - |
| 0.0512 | 200 | 1.1582 | - | - |
| 0.0640 | 250 | 0.8751 | - | - |
| 0.0768 | 300 | 0.6851 | - | - |
| 0.0896 | 350 | 0.5779 | - | - |
| 0.1024 | 400 | 0.5251 | - | - |
| 0.1152 | 450 | 0.4873 | - | - |
| 0.1280 | 500 | 0.4467 | 0.3056 | 0.6054 |
| 0.1408 | 550 | 0.3989 | - | - |
| 0.1536 | 600 | 0.398 | - | - |
| 0.1664 | 650 | 0.3708 | - | - |
| 0.1792 | 700 | 0.3656 | - | - |
| 0.1920 | 750 | 0.3382 | - | - |
| 0.2048 | 800 | 0.3333 | - | - |
| 0.2176 | 850 | 0.3006 | - | - |
| 0.2304 | 900 | 0.3065 | - | - |
| 0.2432 | 950 | 0.3277 | - | - |
| 0.2560 | 1000 | 0.2941 | 0.2089 | 0.6556 |
| 0.2687 | 1050 | 0.2918 | - | - |
| 0.2815 | 1100 | 0.2935 | - | - |
| 0.2943 | 1150 | 0.2834 | - | - |
| 0.3071 | 1200 | 0.2795 | - | - |
| 0.3199 | 1250 | 0.2783 | - | - |
| 0.3327 | 1300 | 0.2828 | - | - |
| 0.3455 | 1350 | 0.2727 | - | - |
| 0.3583 | 1400 | 0.2626 | - | - |
| 0.3711 | 1450 | 0.2519 | - | - |
| 0.3839 | 1500 | 0.2461 | 0.1769 | 0.6743 |
| 0.3967 | 1550 | 0.2602 | - | - |
| 0.4095 | 1600 | 0.2398 | - | - |
| 0.4223 | 1650 | 0.2421 | - | - |
| 0.4351 | 1700 | 0.2365 | - | - |
| 0.4479 | 1750 | 0.2351 | - | - |
| 0.4607 | 1800 | 0.2412 | - | - |
| 0.4735 | 1850 | 0.2308 | - | - |
| 0.4863 | 1900 | 0.2217 | - | - |
| 0.4991 | 1950 | 0.2315 | - | - |
| 0.5119 | 2000 | 0.2295 | 0.1598 | 0.6856 |
| 0.5247 | 2050 | 0.2157 | - | - |
| 0.5375 | 2100 | 0.2123 | - | - |
| 0.5503 | 2150 | 0.2236 | - | - |
| 0.5631 | 2200 | 0.2098 | - | - |
| 0.5759 | 2250 | 0.2208 | - | - |
| 0.5887 | 2300 | 0.2159 | - | - |
| 0.6015 | 2350 | 0.2087 | - | - |
| 0.6143 | 2400 | 0.22 | - | - |
| 0.6271 | 2450 | 0.2002 | - | - |
| 0.6399 | 2500 | 0.1999 | 0.1466 | 0.6915 |
| 0.6527 | 2550 | 0.1986 | - | - |
| 0.6655 | 2600 | 0.2238 | - | - |
| 0.6783 | 2650 | 0.2141 | - | - |
| 0.6911 | 2700 | 0.2154 | - | - |
| 0.7039 | 2750 | 0.1993 | - | - |
| 0.7167 | 2800 | 0.1946 | - | - |
| 0.7295 | 2850 | 0.2064 | - | - |
| 0.7423 | 2900 | 0.2179 | - | - |
| 0.7551 | 2950 | 0.1976 | - | - |
| 0.7679 | 3000 | 0.2081 | 0.1384 | 0.6964 |
| 0.7807 | 3050 | 0.1863 | - | - |
| 0.7934 | 3100 | 0.2022 | - | - |
| 0.8062 | 3150 | 0.2132 | - | - |
| 0.8190 | 3200 | 0.1991 | - | - |
| 0.8318 | 3250 | 0.1904 | - | - |
| 0.8446 | 3300 | 0.1804 | - | - |
| 0.8574 | 3350 | 0.1944 | - | - |
| 0.8702 | 3400 | 0.1981 | - | - |
| 0.8830 | 3450 | 0.195 | - | - |
| 0.8958 | 3500 | 0.1984 | 0.1357 | 0.6994 |
| 0.9086 | 3550 | 0.1947 | - | - |
| 0.9214 | 3600 | 0.1912 | - | - |
| 0.9342 | 3650 | 0.1898 | - | - |
| 0.9470 | 3700 | 0.1945 | - | - |
| 0.9598 | 3750 | 0.1893 | - | - |
| 0.9726 | 3800 | 0.1919 | - | - |
| 0.9854 | 3850 | 0.1994 | - | - |
| 0.9982 | 3900 | 0.1864 | - | - |
| -1 | -1 | - | - | 0.7000 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.057 kWh
- **Carbon Emitted**: 0.022 kg of CO2
- **Hours Used**: 0.206 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: 3.5.0.dev0
- Transformers: 4.49.0.dev0
- PyTorch: 2.5.0+cu121
- Accelerate: 1.3.0
- Datasets: 2.20.0
- Tokenizers: 0.21.0
## 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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