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NeoBERT-gooaq-8e-05

Sentence Similarity
sentence-transformers
Safetensors
English
neobert
feature-extraction
Generated from Trainer
dataset_size:3011496
loss:CachedMultipleNegativesRankingLoss
custom_code
Eval Results
Inference Endpoints
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NeoBERT-gooaq-8e-05 / README.md
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---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3011496
- loss:CachedMultipleNegativesRankingLoss
base_model: chandar-lab/NeoBERT
widget:
- source_sentence: how much percent of alcohol is in scotch?
sentences:
- Our 24-hour day comes from the ancient Egyptians who divided day-time into 10
hours they measured with devices such as shadow clocks, and added a twilight hour
at the beginning and another one at the end of the day-time, says Lomb. "Night-time
was divided in 12 hours, based on the observations of stars.
- After distillation, a Scotch Whisky can be anywhere between 60-75% ABV, with American
Whiskey rocketing right into the 90% region. Before being placed in casks, Scotch
is usually diluted to around 63.5% ABV (68% for grain); welcome to the stage cask
strength Whisky.
- Money For Nothing. In season four Dominic West, the ostensible star of the series,
requested a reduced role so that he could spend more time with his family in London.
On the show it was explained that Jimmy McNulty had taken a patrol job which required
less strenuous work.
- source_sentence: what are the major causes of poor listening?
sentences:
- The four main causes of poor listening are due to not concentrating, listening
too hard, jumping to conclusions and focusing on delivery and personal appearance.
Sometimes we just don't feel attentive enough and hence don't concentrate.
- That's called being idle. “System Idle Process” is the software that runs when
the computer has absolutely nothing better to do. It has the lowest possible priority
and uses as few resources as possible, so that if anything at all comes along
for the CPU to work on, it can.
- 'No alcohol wine: how it''s made It''s not easy. There are three main methods
currently in use. Vacuum distillation sees alcohol and other volatiles removed
at a relatively low temperature (25°C-30°C), with aromatics blended back in afterwards.'
- source_sentence: are jess and justin still together?
sentences:
- Download photos and videos to your device On your iPhone, iPad, or iPod touch,
tap Settings > [your name] > iCloud > Photos. Then select Download and Keep Originals
and import the photos to your computer. On your Mac, open the Photos app. Select
the photos and videos you want to copy.
- Later, Justin reunites with Jessica at prom and the two get back together. ...
After a tearful goodbye to Jessica, the Jensens, and his friends, Justin dies
just before graduation.
- Incumbent president Muhammadu Buhari won his reelection bid, defeating his closest
rival Atiku Abubakar by over 3 million votes. He was issued a Certificate of Return,
and was sworn in on May 29, 2019, the former date of Democracy Day (Nigeria).
- source_sentence: when humans are depicted in hindu art?
sentences:
- 'Answer: Humans are depicted in Hindu art often in sensuous and erotic postures.'
- Bettas are carnivores. They require foods high in animal protein. Their preferred
diet in nature includes insects and insect larvae. In captivity, they thrive on
a varied diet of pellets or flakes made from fish meal, as well as frozen or freeze-dried
bloodworms.
- An active continental margin is found on the leading edge of the continent where
it is crashing into an oceanic plate. ... Passive continental margins are found
along the remaining coastlines.
- source_sentence: what is the difference between 18 and 20 inch tires?
sentences:
- '[''Alienware m17 R3. The best gaming laptop overall offers big power in slim,
redesigned chassis. ... '', ''Dell G3 15. ... '', ''Asus ROG Zephyrus G14. ...
'', ''Lenovo Legion Y545. ... '', ''Alienware Area 51m. ... '', ''Asus ROG Mothership.
... '', ''Asus ROG Strix Scar III. ... '', ''HP Omen 17 (2019)'']'
- So extracurricular activities are just activities that you do outside of class.
The Common App says that extracurricular activities "include arts, athletics,
clubs, employment, personal commitments, and other pursuits."
- The only real difference is a 20" rim would be more likely to be damaged, as you
pointed out. Beyond looks, there is zero benefit for the 20" rim. Also, just the
availability of tires will likely be much more limited for the larger rim. ...
Tire selection is better for 18" wheels than 20" wheels.
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
model-index:
- name: SentenceTransformer based on chandar-lab/NeoBERT
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.46
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.64
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.76
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.46
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14400000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.43
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.62
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.68
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.73
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.592134936685869
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5606666666666666
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5501347879979241
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.32
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.58
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.68
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.74
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.19333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.136
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07400000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.32
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.58
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.68
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.74
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5415424816174165
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4768333333333334
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.49019229786708785
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.39
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.61
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.69
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.75
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.39
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.20666666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07700000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.375
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.6
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.68
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.735
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5668387091516427
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.51875
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.520163542932506
name: Cosine Map@100
---
# SentenceTransformer based on chandar-lab/NeoBERT
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [chandar-lab/NeoBERT](https://huggingface.co/chandar-lab/NeoBERT) on the [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 has been finetuned using [train_st_gooaq.py](train_st_gooaq.py) using an RTX 3090. It used the same training script as [tomaarsen/ModernBERT-base-gooaq](https://huggingface.co/tomaarsen/ModernBERT-base-gooaq).
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [chandar-lab/NeoBERT](https://huggingface.co/chandar-lab/NeoBERT) <!-- at revision d97a4acdc851efed665d0550ea5704f00ad3ef76 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
- **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': 8192, 'do_lower_case': False}) with Transformer model: NeoBERT
(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/NeoBERT-gooaq-8e-05")
# Run inference
sentences = [
'what is the difference between 18 and 20 inch tires?',
'The only real difference is a 20" rim would be more likely to be damaged, as you pointed out. Beyond looks, there is zero benefit for the 20" rim. Also, just the availability of tires will likely be much more limited for the larger rim. ... Tire selection is better for 18" wheels than 20" wheels.',
'So extracurricular activities are just activities that you do outside of class. The Common App says that extracurricular activities "include arts, athletics, clubs, employment, personal commitments, and other pursuits."',
]
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
* Datasets: `NanoNQ` and `NanoMSMARCO`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | NanoNQ | NanoMSMARCO |
|:--------------------|:-----------|:------------|
| cosine_accuracy@1 | 0.46 | 0.32 |
| cosine_accuracy@3 | 0.64 | 0.58 |
| cosine_accuracy@5 | 0.7 | 0.68 |
| cosine_accuracy@10 | 0.76 | 0.74 |
| cosine_precision@1 | 0.46 | 0.32 |
| cosine_precision@3 | 0.22 | 0.1933 |
| cosine_precision@5 | 0.144 | 0.136 |
| cosine_precision@10 | 0.08 | 0.074 |
| cosine_recall@1 | 0.43 | 0.32 |
| cosine_recall@3 | 0.62 | 0.58 |
| cosine_recall@5 | 0.68 | 0.68 |
| cosine_recall@10 | 0.73 | 0.74 |
| **cosine_ndcg@10** | **0.5921** | **0.5415** |
| cosine_mrr@10 | 0.5607 | 0.4768 |
| cosine_map@100 | 0.5501 | 0.4902 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.39 |
| cosine_accuracy@3 | 0.61 |
| cosine_accuracy@5 | 0.69 |
| cosine_accuracy@10 | 0.75 |
| cosine_precision@1 | 0.39 |
| cosine_precision@3 | 0.2067 |
| cosine_precision@5 | 0.14 |
| cosine_precision@10 | 0.077 |
| cosine_recall@1 | 0.375 |
| cosine_recall@3 | 0.6 |
| cosine_recall@5 | 0.68 |
| cosine_recall@10 | 0.735 |
| **cosine_ndcg@10** | **0.5668** |
| cosine_mrr@10 | 0.5188 |
| cosine_map@100 | 0.5202 |
<!--
## 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: 3,011,496 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: 11.87 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.09 tokens</li><li>max: 201 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is the difference between clay and mud mask?</code> | <code>The main difference between the two is that mud is a skin-healing agent, while clay is a cosmetic, drying agent. Clay masks are most useful for someone who has oily skin and is prone to breakouts of acne and blemishes.</code> |
| <code>myki how much on card?</code> | <code>A full fare myki card costs $6 and a concession, seniors or child myki costs $3. For more information about how to use your myki, visit ptv.vic.gov.au or call 1800 800 007.</code> |
| <code>how to find out if someone blocked your phone number on iphone?</code> | <code>If you get a notification like "Message Not Delivered" or you get no notification at all, that's a sign of a potential block. Next, you could try calling the person. If the call goes right to voicemail or rings once (or a half ring) then goes to voicemail, that's further evidence you may have been blocked.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) 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: 1,000 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: 11.88 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>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 2048
- `per_device_eval_batch_size`: 2048
- `learning_rate`: 8e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.05
- `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`: 2048
- `per_device_eval_batch_size`: 2048
- `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`: 8e-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`: 0.05
- `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`: 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
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | NanoNQ_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:---------------------:|:--------------------------:|:----------------------------:|
| -1 | -1 | - | - | 0.0428 | 0.1127 | 0.0777 |
| 0.0068 | 10 | 4.2332 | - | - | - | - |
| 0.0136 | 20 | 1.5303 | - | - | - | - |
| 0.0204 | 30 | 0.887 | - | - | - | - |
| 0.0272 | 40 | 0.6286 | - | - | - | - |
| 0.0340 | 50 | 0.5193 | 0.2091 | 0.4434 | 0.4454 | 0.4444 |
| 0.0408 | 60 | 0.4423 | - | - | - | - |
| 0.0476 | 70 | 0.3842 | - | - | - | - |
| 0.0544 | 80 | 0.3576 | - | - | - | - |
| 0.0612 | 90 | 0.3301 | - | - | - | - |
| 0.0680 | 100 | 0.3135 | 0.1252 | 0.4606 | 0.5150 | 0.4878 |
| 0.0748 | 110 | 0.302 | - | - | - | - |
| 0.0816 | 120 | 0.277 | - | - | - | - |
| 0.0884 | 130 | 0.2694 | - | - | - | - |
| 0.0952 | 140 | 0.2628 | - | - | - | - |
| 0.1020 | 150 | 0.2471 | 0.0949 | 0.5135 | 0.5133 | 0.5134 |
| 0.1088 | 160 | 0.2343 | - | - | - | - |
| 0.1156 | 170 | 0.2386 | - | - | - | - |
| 0.1224 | 180 | 0.219 | - | - | - | - |
| 0.1292 | 190 | 0.217 | - | - | - | - |
| 0.1360 | 200 | 0.2073 | 0.0870 | 0.5281 | 0.4824 | 0.5052 |
| 0.1428 | 210 | 0.2208 | - | - | - | - |
| 0.1496 | 220 | 0.2046 | - | - | - | - |
| 0.1564 | 230 | 0.2045 | - | - | - | - |
| 0.1632 | 240 | 0.1987 | - | - | - | - |
| 0.1700 | 250 | 0.1949 | 0.0734 | 0.5781 | 0.4976 | 0.5378 |
| 0.1768 | 260 | 0.1888 | - | - | - | - |
| 0.1835 | 270 | 0.187 | - | - | - | - |
| 0.1903 | 280 | 0.1834 | - | - | - | - |
| 0.1971 | 290 | 0.1747 | - | - | - | - |
| 0.2039 | 300 | 0.1805 | 0.0663 | 0.5580 | 0.5453 | 0.5516 |
| 0.2107 | 310 | 0.1738 | - | - | - | - |
| 0.2175 | 320 | 0.1707 | - | - | - | - |
| 0.2243 | 330 | 0.1758 | - | - | - | - |
| 0.2311 | 340 | 0.1762 | - | - | - | - |
| 0.2379 | 350 | 0.1649 | 0.0624 | 0.5761 | 0.5310 | 0.5535 |
| 0.2447 | 360 | 0.1682 | - | - | - | - |
| 0.2515 | 370 | 0.1629 | - | - | - | - |
| 0.2583 | 380 | 0.1595 | - | - | - | - |
| 0.2651 | 390 | 0.1571 | - | - | - | - |
| 0.2719 | 400 | 0.1617 | 0.0592 | 0.5865 | 0.5193 | 0.5529 |
| 0.2787 | 410 | 0.1521 | - | - | - | - |
| 0.2855 | 420 | 0.1518 | - | - | - | - |
| 0.2923 | 430 | 0.1583 | - | - | - | - |
| 0.2991 | 440 | 0.1516 | - | - | - | - |
| 0.3059 | 450 | 0.1473 | 0.0570 | 0.5844 | 0.5181 | 0.5512 |
| 0.3127 | 460 | 0.1491 | - | - | - | - |
| 0.3195 | 470 | 0.1487 | - | - | - | - |
| 0.3263 | 480 | 0.1457 | - | - | - | - |
| 0.3331 | 490 | 0.1463 | - | - | - | - |
| 0.3399 | 500 | 0.141 | 0.0571 | 0.5652 | 0.5027 | 0.5340 |
| 0.3467 | 510 | 0.1438 | - | - | - | - |
| 0.3535 | 520 | 0.148 | - | - | - | - |
| 0.3603 | 530 | 0.136 | - | - | - | - |
| 0.3671 | 540 | 0.1359 | - | - | - | - |
| 0.3739 | 550 | 0.1388 | 0.0507 | 0.5457 | 0.4660 | 0.5058 |
| 0.3807 | 560 | 0.1358 | - | - | - | - |
| 0.3875 | 570 | 0.1365 | - | - | - | - |
| 0.3943 | 580 | 0.1328 | - | - | - | - |
| 0.4011 | 590 | 0.1404 | - | - | - | - |
| 0.4079 | 600 | 0.1304 | 0.0524 | 0.5477 | 0.5259 | 0.5368 |
| 0.4147 | 610 | 0.1321 | - | - | - | - |
| 0.4215 | 620 | 0.1322 | - | - | - | - |
| 0.4283 | 630 | 0.1262 | - | - | - | - |
| 0.4351 | 640 | 0.1339 | - | - | - | - |
| 0.4419 | 650 | 0.1257 | 0.0494 | 0.5564 | 0.4920 | 0.5242 |
| 0.4487 | 660 | 0.1247 | - | - | - | - |
| 0.4555 | 670 | 0.1316 | - | - | - | - |
| 0.4623 | 680 | 0.124 | - | - | - | - |
| 0.4691 | 690 | 0.1247 | - | - | - | - |
| 0.4759 | 700 | 0.1212 | 0.0480 | 0.5663 | 0.5040 | 0.5351 |
| 0.4827 | 710 | 0.1194 | - | - | - | - |
| 0.4895 | 720 | 0.1224 | - | - | - | - |
| 0.4963 | 730 | 0.1225 | - | - | - | - |
| 0.5031 | 740 | 0.1209 | - | - | - | - |
| 0.5099 | 750 | 0.1197 | 0.0447 | 0.5535 | 0.5127 | 0.5331 |
| 0.5167 | 760 | 0.1196 | - | - | - | - |
| 0.5235 | 770 | 0.1129 | - | - | - | - |
| 0.5303 | 780 | 0.1223 | - | - | - | - |
| 0.5370 | 790 | 0.1159 | - | - | - | - |
| 0.5438 | 800 | 0.1178 | 0.0412 | 0.5558 | 0.5275 | 0.5416 |
| 0.5506 | 810 | 0.1186 | - | - | - | - |
| 0.5574 | 820 | 0.1153 | - | - | - | - |
| 0.5642 | 830 | 0.1178 | - | - | - | - |
| 0.5710 | 840 | 0.1155 | - | - | - | - |
| 0.5778 | 850 | 0.1152 | 0.0432 | 0.5738 | 0.5243 | 0.5490 |
| 0.5846 | 860 | 0.1101 | - | - | - | - |
| 0.5914 | 870 | 0.1057 | - | - | - | - |
| 0.5982 | 880 | 0.1141 | - | - | - | - |
| 0.6050 | 890 | 0.1172 | - | - | - | - |
| 0.6118 | 900 | 0.1146 | 0.0414 | 0.5641 | 0.4805 | 0.5223 |
| 0.6186 | 910 | 0.1094 | - | - | - | - |
| 0.6254 | 920 | 0.1116 | - | - | - | - |
| 0.6322 | 930 | 0.111 | - | - | - | - |
| 0.6390 | 940 | 0.1078 | - | - | - | - |
| 0.6458 | 950 | 0.1041 | 0.0424 | 0.5883 | 0.5412 | 0.5647 |
| 0.6526 | 960 | 0.1068 | - | - | - | - |
| 0.6594 | 970 | 0.1076 | - | - | - | - |
| 0.6662 | 980 | 0.1068 | - | - | - | - |
| 0.6730 | 990 | 0.1038 | - | - | - | - |
| 0.6798 | 1000 | 0.1017 | 0.0409 | 0.5850 | 0.5117 | 0.5483 |
| 0.6866 | 1010 | 0.1079 | - | - | - | - |
| 0.6934 | 1020 | 0.1067 | - | - | - | - |
| 0.7002 | 1030 | 0.1079 | - | - | - | - |
| 0.7070 | 1040 | 0.1039 | - | - | - | - |
| 0.7138 | 1050 | 0.1016 | 0.0356 | 0.5927 | 0.5344 | 0.5636 |
| 0.7206 | 1060 | 0.1017 | - | - | - | - |
| 0.7274 | 1070 | 0.1029 | - | - | - | - |
| 0.7342 | 1080 | 0.1038 | - | - | - | - |
| 0.7410 | 1090 | 0.0994 | - | - | - | - |
| 0.7478 | 1100 | 0.0984 | 0.0376 | 0.5618 | 0.5321 | 0.5470 |
| 0.7546 | 1110 | 0.0966 | - | - | - | - |
| 0.7614 | 1120 | 0.1024 | - | - | - | - |
| 0.7682 | 1130 | 0.099 | - | - | - | - |
| 0.7750 | 1140 | 0.1017 | - | - | - | - |
| 0.7818 | 1150 | 0.0951 | 0.0368 | 0.5832 | 0.5073 | 0.5453 |
| 0.7886 | 1160 | 0.1008 | - | - | - | - |
| 0.7954 | 1170 | 0.096 | - | - | - | - |
| 0.8022 | 1180 | 0.0962 | - | - | - | - |
| 0.8090 | 1190 | 0.1004 | - | - | - | - |
| 0.8158 | 1200 | 0.0986 | 0.0321 | 0.5895 | 0.5242 | 0.5568 |
| 0.8226 | 1210 | 0.0966 | - | - | - | - |
| 0.8294 | 1220 | 0.096 | - | - | - | - |
| 0.8362 | 1230 | 0.0962 | - | - | - | - |
| 0.8430 | 1240 | 0.0987 | - | - | - | - |
| 0.8498 | 1250 | 0.096 | 0.0316 | 0.5801 | 0.5434 | 0.5617 |
| 0.8566 | 1260 | 0.097 | - | - | - | - |
| 0.8634 | 1270 | 0.0929 | - | - | - | - |
| 0.8702 | 1280 | 0.0973 | - | - | - | - |
| 0.8770 | 1290 | 0.0973 | - | - | - | - |
| 0.8838 | 1300 | 0.0939 | 0.0330 | 0.5916 | 0.5478 | 0.5697 |
| 0.8906 | 1310 | 0.0968 | - | - | - | - |
| 0.8973 | 1320 | 0.0969 | - | - | - | - |
| 0.9041 | 1330 | 0.0931 | - | - | - | - |
| 0.9109 | 1340 | 0.0919 | - | - | - | - |
| 0.9177 | 1350 | 0.0916 | 0.0324 | 0.5908 | 0.5308 | 0.5608 |
| 0.9245 | 1360 | 0.0903 | - | - | - | - |
| 0.9313 | 1370 | 0.0957 | - | - | - | - |
| 0.9381 | 1380 | 0.0891 | - | - | - | - |
| 0.9449 | 1390 | 0.0909 | - | - | - | - |
| 0.9517 | 1400 | 0.0924 | 0.0318 | 0.5823 | 0.5388 | 0.5605 |
| 0.9585 | 1410 | 0.0932 | - | - | - | - |
| 0.9653 | 1420 | 0.0916 | - | - | - | - |
| 0.9721 | 1430 | 0.0966 | - | - | - | - |
| 0.9789 | 1440 | 0.0864 | - | - | - | - |
| 0.9857 | 1450 | 0.0872 | 0.0311 | 0.5895 | 0.5442 | 0.5668 |
| 0.9925 | 1460 | 0.0897 | - | - | - | - |
| 0.9993 | 1470 | 0.086 | - | - | - | - |
| -1 | -1 | - | - | 0.5921 | 0.5415 | 0.5668 |
</details>
### Framework Versions
- Python: 3.11.10
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.49.0
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.0
- Datasets: 2.21.0
- 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",
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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