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---
language:
- en
license: apache-2.0
tags:
- biencoder
- sentence-transformers
- text-classification
- sentence-pair-classification
- semantic-similarity
- semantic-search
- retrieval
- reranking
- generated_from_trainer
- dataset_size:9233417
- loss:ArcFaceInBatchLoss
base_model: answerdotai/ModernBERT-base
widget:
- source_sentence: Hayley Vaughan portrayed Ripa on the ABC daytime soap opera , ``
All My Children `` , between 1990 and 2002 .
sentences:
- Traxxpad is a music application for Sony 's PlayStation Portable published by
Definitive Studios and developed by Eidos Interactive .
- Between 1990 and 2002 , Hayley Vaughan Ripa portrayed in the ABC soap opera ``
All My Children `` .
- Between 1990 and 2002 , Ripa Hayley portrayed Vaughan in the ABC soap opera ``
All My Children `` .
- source_sentence: Olivella monilifera is a species of dwarf sea snail , small gastropod
mollusk in the family Olivellidae , the marine olives .
sentences:
- Olivella monilifera is a species of the dwarf - sea snail , small gastropod mollusk
in the Olivellidae family , the marine olives .
- He was cut by the Browns after being signed by the Bills in 2013 . He was later
released .
- Olivella monilifera is a kind of sea snail , marine gastropod mollusk in the Olivellidae
family , the dwarf olives .
- source_sentence: Hayashi said that Mackey `` is a sort of `` of the original model
for Tenchi .
sentences:
- In the summer of 2009 , Ellick shot a documentary about Malala Yousafzai .
- Hayashi said that Mackey is `` sort of `` the original model for Tenchi .
- Mackey said that Hayashi is `` sort of `` the original model for Tenchi .
- source_sentence: Much of the film was shot on location in Los Angeles and in nearby
Burbank and Glendale .
sentences:
- Much of the film was shot on location in Los Angeles and in nearby Burbank and
Glendale .
- Much of the film was shot on site in Burbank and Glendale and in the nearby Los
Angeles .
- Traxxpad is a music application for the Sony PlayStation Portable developed by
the Definitive Studios and published by Eidos Interactive .
- source_sentence: According to him , the earth is the carrier of his artistic work
, which is only integrated into the creative process by minimal changes .
sentences:
- National players are Bold players .
- According to him , earth is the carrier of his artistic work being integrated
into the creative process only by minimal changes .
- According to him , earth is the carrier of his creative work being integrated
into the artistic process only by minimal changes .
datasets:
- redis/langcache-sentencepairs-v2
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_precision@1
- cosine_recall@1
- cosine_ndcg@10
- cosine_mrr@1
- cosine_map@100
model-index:
- name: Redis fine-tuned BiEncoder model for semantic caching on LangCache
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: test
type: test
metrics:
- type: cosine_accuracy@1
value: 0.6032809198037179
name: Cosine Accuracy@1
- type: cosine_precision@1
value: 0.6032809198037179
name: Cosine Precision@1
- type: cosine_recall@1
value: 0.585771482488324
name: Cosine Recall@1
- type: cosine_ndcg@10
value: 0.7747479314468421
name: Cosine Ndcg@10
- type: cosine_mrr@1
value: 0.6032809198037179
name: Cosine Mrr@1
- type: cosine_map@100
value: 0.7280398908979986
name: Cosine Map@100
---
# Redis fine-tuned BiEncoder model for semantic caching on LangCache
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for sentence pair similarity.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
- **Maximum Sequence Length:** 100 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2)
- **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': 100, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(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("redis/langcache-embed-v3")
# Run inference
sentences = [
'According to him , the earth is the carrier of his artistic work , which is only integrated into the creative process by minimal changes .',
'According to him , earth is the carrier of his artistic work being integrated into the creative process only by minimal changes .',
'According to him , earth is the carrier of his creative work being integrated into the artistic process only by minimal changes .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9180, 0.4531],
# [0.9180, 1.0000, 0.4746],
# [0.4531, 0.4746, 1.0000]], dtype=torch.bfloat16)
```
<!--
### 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: `test`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| cosine_accuracy@1 | 0.6033 |
| cosine_precision@1 | 0.6033 |
| cosine_recall@1 | 0.5858 |
| **cosine_ndcg@10** | **0.7747** |
| cosine_mrr@1 | 0.6033 |
| cosine_map@100 | 0.728 |
<!--
## 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
#### LangCache Sentence Pairs (all)
* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2)
* Size: 126,938 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 26.54 tokens</li><li>max: 61 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>how can I get financial freedom as soon as possible?</code> |
| <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The older Punts are still very much in existence today and race in the same fleets as the newer boats .</code> |
| <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley , , was located at Turner Valley Bar N Ranch Airport , southwest of Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> |
* Loss: <code>losses.ArcFaceInBatchLoss</code> with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Evaluation Dataset
#### LangCache Sentence Pairs (all)
* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2)
* Size: 126,938 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 26.54 tokens</li><li>max: 61 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>how can I get financial freedom as soon as possible?</code> |
| <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The older Punts are still very much in existence today and race in the same fleets as the newer boats .</code> |
| <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley , , was located at Turner Valley Bar N Ranch Airport , southwest of Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> |
* Loss: <code>losses.ArcFaceInBatchLoss</code> with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `weight_decay`: 0.001
- `adam_beta2`: 0.98
- `adam_epsilon`: 1e-06
- `max_steps`: 100000
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `optim`: stable_adamw
- `ddp_find_unused_parameters`: False
- `push_to_hub`: True
- `hub_model_id`: redis/langcache-embed-v3
- `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`: 5e-05
- `weight_decay`: 0.001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.98
- `adam_epsilon`: 1e-06
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3.0
- `max_steps`: 100000
- `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`: False
- `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`: True
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: stable_adamw
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: False
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: redis/langcache-embed-v3
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `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
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | test_cosine_ndcg@10 |
|:----------:|:---------:|:-------------:|:---------------:|:-------------------:|
| -1 | -1 | - | - | 0.5952 |
| 0.0069 | 500 | 3.4812 | 0.6932 | 0.6810 |
| 0.0139 | 1000 | 0.6045 | 0.4804 | 0.7354 |
| 0.0208 | 1500 | 0.3127 | 0.4128 | 0.7437 |
| 0.0277 | 2000 | 0.2424 | 0.4077 | 0.7440 |
| 0.0347 | 2500 | 0.2027 | 0.3707 | 0.7501 |
| 0.0416 | 3000 | 0.1752 | 0.3453 | 0.7551 |
| 0.0485 | 3500 | 0.1622 | 0.3380 | 0.7540 |
| 0.0555 | 4000 | 0.1466 | 0.3185 | 0.7583 |
| 0.0624 | 4500 | 0.1392 | 0.3092 | 0.7588 |
| 0.0693 | 5000 | 0.1342 | 0.3054 | 0.7566 |
| 0.0762 | 5500 | 0.1291 | 0.2960 | 0.7582 |
| 0.0832 | 6000 | 0.1291 | 0.2856 | 0.7616 |
| 0.0901 | 6500 | 0.1199 | 0.2803 | 0.7624 |
| 0.0970 | 7000 | 0.1171 | 0.2692 | 0.7648 |
| 0.1040 | 7500 | 0.1097 | 0.2811 | 0.7629 |
| 0.1109 | 8000 | 0.1089 | 0.2901 | 0.7621 |
| 0.1178 | 8500 | 0.1088 | 0.2986 | 0.7568 |
| 0.1248 | 9000 | 0.109 | 0.2806 | 0.7628 |
| 0.1317 | 9500 | 0.1046 | 0.3050 | 0.7587 |
| 0.1386 | 10000 | 0.1035 | 0.2925 | 0.7596 |
| 0.1456 | 10500 | 0.1041 | 0.2940 | 0.7573 |
| 0.1525 | 11000 | 0.1023 | 0.2790 | 0.7632 |
| 0.1594 | 11500 | 0.0992 | 0.3293 | 0.7542 |
| 0.1664 | 12000 | 0.0996 | 0.2876 | 0.7570 |
| 0.1733 | 12500 | 0.0949 | 0.2881 | 0.7591 |
| 0.1802 | 13000 | 0.0921 | 0.2861 | 0.7598 |
| 0.1871 | 13500 | 0.0912 | 0.2763 | 0.7632 |
| 0.1941 | 14000 | 0.0912 | 0.2785 | 0.7643 |
| 0.2010 | 14500 | 0.0909 | 0.3198 | 0.7629 |
| 0.2079 | 15000 | 0.0911 | 0.3015 | 0.7575 |
| 0.2149 | 15500 | 0.0861 | 0.3029 | 0.7597 |
| 0.2218 | 16000 | 0.0857 | 0.3271 | 0.7568 |
| 0.2287 | 16500 | 0.0843 | 0.2579 | 0.7645 |
| 0.2357 | 17000 | 0.085 | 0.2923 | 0.7625 |
| 0.2426 | 17500 | 0.0846 | 0.3241 | 0.7598 |
| 0.2495 | 18000 | 0.083 | 0.3128 | 0.7616 |
| 0.2565 | 18500 | 0.0794 | 0.2926 | 0.7611 |
| 0.2634 | 19000 | 0.0806 | 0.2665 | 0.7640 |
| 0.2703 | 19500 | 0.0782 | 0.2963 | 0.7615 |
| 0.2773 | 20000 | 0.0786 | 0.2771 | 0.7611 |
| 0.2842 | 20500 | 0.0761 | 0.2853 | 0.7623 |
| 0.2911 | 21000 | 0.0752 | 0.2782 | 0.7626 |
| 0.2980 | 21500 | 0.0777 | 0.2680 | 0.7612 |
| 0.3050 | 22000 | 0.0782 | 0.2731 | 0.7636 |
| 0.3119 | 22500 | 0.0785 | 0.2627 | 0.7627 |
| 0.3188 | 23000 | 0.0741 | 0.2714 | 0.7613 |
| 0.3258 | 23500 | 0.0741 | 0.2713 | 0.7661 |
| 0.3327 | 24000 | 0.072 | 0.2630 | 0.7636 |
| 0.3396 | 24500 | 0.0739 | 0.2839 | 0.7648 |
| 0.3466 | 25000 | 0.07 | 0.2860 | 0.7634 |
| 0.3535 | 25500 | 0.0715 | 0.2612 | 0.7666 |
| 0.3604 | 26000 | 0.0711 | 0.2531 | 0.7671 |
| 0.3674 | 26500 | 0.0701 | 0.2682 | 0.7638 |
| 0.3743 | 27000 | 0.0733 | 0.2708 | 0.7635 |
| 0.3812 | 27500 | 0.0705 | 0.2873 | 0.7636 |
| 0.3882 | 28000 | 0.0663 | 0.2831 | 0.7647 |
| 0.3951 | 28500 | 0.0678 | 0.2825 | 0.7643 |
| 0.4020 | 29000 | 0.0691 | 0.2733 | 0.7654 |
| 0.4089 | 29500 | 0.0696 | 0.2831 | 0.7621 |
| 0.4159 | 30000 | 0.0708 | 0.2893 | 0.7643 |
| 0.4228 | 30500 | 0.0663 | 0.2758 | 0.7653 |
| 0.4297 | 31000 | 0.064 | 0.2589 | 0.7666 |
| 0.4367 | 31500 | 0.0636 | 0.2491 | 0.7681 |
| 0.4436 | 32000 | 0.0644 | 0.2601 | 0.7650 |
| 0.4505 | 32500 | 0.0655 | 0.2611 | 0.7668 |
| 0.4575 | 33000 | 0.0643 | 0.2597 | 0.7664 |
| 0.4644 | 33500 | 0.066 | 0.2696 | 0.7677 |
| 0.4713 | 34000 | 0.0664 | 0.2489 | 0.7690 |
| 0.4783 | 34500 | 0.0654 | 0.2644 | 0.7649 |
| 0.4852 | 35000 | 0.0653 | 0.2704 | 0.7665 |
| 0.4921 | 35500 | 0.0657 | 0.2578 | 0.7689 |
| 0.4991 | 36000 | 0.0634 | 0.2629 | 0.7669 |
| 0.5060 | 36500 | 0.0609 | 0.2631 | 0.7663 |
| 0.5129 | 37000 | 0.0646 | 0.2586 | 0.7667 |
| 0.5198 | 37500 | 0.0634 | 0.2572 | 0.7657 |
| 0.5268 | 38000 | 0.0607 | 0.2624 | 0.7664 |
| 0.5337 | 38500 | 0.0621 | 0.2622 | 0.7668 |
| 0.5406 | 39000 | 0.0614 | 0.2562 | 0.7676 |
| 0.5476 | 39500 | 0.0621 | 0.2560 | 0.7673 |
| 0.5545 | 40000 | 0.0608 | 0.2506 | 0.7684 |
| 0.5614 | 40500 | 0.0621 | 0.2718 | 0.7666 |
| 0.5684 | 41000 | 0.0598 | 0.2599 | 0.7700 |
| 0.5753 | 41500 | 0.06 | 0.2706 | 0.7687 |
| 0.5822 | 42000 | 0.0618 | 0.2635 | 0.7694 |
| 0.5892 | 42500 | 0.0604 | 0.2743 | 0.7660 |
| 0.5961 | 43000 | 0.0576 | 0.2733 | 0.7661 |
| 0.6030 | 43500 | 0.0597 | 0.2644 | 0.7712 |
| 0.6100 | 44000 | 0.0592 | 0.2516 | 0.7694 |
| 0.6169 | 44500 | 0.0599 | 0.2461 | 0.7688 |
| 0.6238 | 45000 | 0.056 | 0.2438 | 0.7686 |
| 0.6307 | 45500 | 0.0573 | 0.2513 | 0.7703 |
| 0.6377 | 46000 | 0.0571 | 0.2526 | 0.7694 |
| 0.6446 | 46500 | 0.0573 | 0.2529 | 0.7702 |
| 0.6515 | 47000 | 0.0553 | 0.2529 | 0.7694 |
| 0.6585 | 47500 | 0.0541 | 0.2518 | 0.7707 |
| 0.6654 | 48000 | 0.0561 | 0.2471 | 0.7725 |
| 0.6723 | 48500 | 0.0558 | 0.2440 | 0.7710 |
| 0.6793 | 49000 | 0.0555 | 0.2556 | 0.7691 |
| 0.6862 | 49500 | 0.056 | 0.2479 | 0.7721 |
| 0.6931 | 50000 | 0.0564 | 0.2463 | 0.7723 |
| 0.7001 | 50500 | 0.0539 | 0.2561 | 0.7728 |
| 0.7070 | 51000 | 0.0526 | 0.2416 | 0.7725 |
| 0.7139 | 51500 | 0.0561 | 0.2501 | 0.7723 |
| 0.7209 | 52000 | 0.0545 | 0.2316 | 0.7732 |
| 0.7278 | 52500 | 0.0545 | 0.2352 | 0.7739 |
| 0.7347 | 53000 | 0.05 | 0.2278 | 0.7734 |
| 0.7416 | 53500 | 0.0515 | 0.2308 | 0.7730 |
| 0.7486 | 54000 | 0.0528 | 0.2524 | 0.7727 |
| 0.7555 | 54500 | 0.0509 | 0.2645 | 0.7717 |
| 0.7624 | 55000 | 0.0514 | 0.2659 | 0.7708 |
| 0.7694 | 55500 | 0.0503 | 0.2570 | 0.7725 |
| 0.7763 | 56000 | 0.0538 | 0.2524 | 0.7724 |
| 0.7832 | 56500 | 0.0477 | 0.2537 | 0.7719 |
| 0.7902 | 57000 | 0.0514 | 0.2333 | 0.7733 |
| 0.7971 | 57500 | 0.05 | 0.2420 | 0.7722 |
| 0.8040 | 58000 | 0.0518 | 0.2342 | 0.7734 |
| 0.8110 | 58500 | 0.0508 | 0.2402 | 0.7730 |
| 0.8179 | 59000 | 0.0474 | 0.2477 | 0.7711 |
| 0.8248 | 59500 | 0.0493 | 0.2465 | 0.7723 |
| 0.8318 | 60000 | 0.0492 | 0.2448 | 0.7731 |
| 0.8387 | 60500 | 0.0496 | 0.2498 | 0.7733 |
| 0.8456 | 61000 | 0.0479 | 0.2505 | 0.7721 |
| 0.8525 | 61500 | 0.0445 | 0.2449 | 0.7745 |
| **0.8595** | **62000** | **0.0477** | **0.2507** | **0.7748** |
| 0.8664 | 62500 | 0.0491 | 0.2551 | 0.7716 |
| 0.8733 | 63000 | 0.0474 | 0.2451 | 0.7743 |
| 0.8803 | 63500 | 0.0452 | 0.2464 | 0.7741 |
| 0.8872 | 64000 | 0.0482 | 0.2412 | 0.7742 |
| 0.8941 | 64500 | 0.0483 | 0.2444 | 0.7736 |
| 0.9011 | 65000 | 0.0485 | 0.2456 | 0.7724 |
| 0.9080 | 65500 | 0.045 | 0.2493 | 0.7730 |
| 0.9149 | 66000 | 0.0496 | 0.2499 | 0.7721 |
| 0.9219 | 66500 | 0.0461 | 0.2474 | 0.7748 |
| 0.9288 | 67000 | 0.0465 | 0.2432 | 0.7743 |
| 0.9357 | 67500 | 0.0477 | 0.2432 | 0.7729 |
| 0.9427 | 68000 | 0.0425 | 0.2491 | 0.7740 |
| 0.9496 | 68500 | 0.0452 | 0.2445 | 0.7736 |
| 0.9565 | 69000 | 0.046 | 0.2397 | 0.7742 |
| 0.9634 | 69500 | 0.0449 | 0.2539 | 0.7731 |
| 0.9704 | 70000 | 0.0462 | 0.2446 | 0.7745 |
| 0.9773 | 70500 | 0.0435 | 0.2385 | 0.7742 |
| 0.9842 | 71000 | 0.0469 | 0.2334 | 0.7750 |
| 0.9912 | 71500 | 0.0447 | 0.2312 | 0.7745 |
| 0.9981 | 72000 | 0.0465 | 0.2361 | 0.7737 |
| 1.0050 | 72500 | 0.0341 | 0.2359 | 0.7728 |
| 1.0120 | 73000 | 0.03 | 0.2405 | 0.7727 |
| 1.0189 | 73500 | 0.029 | 0.2241 | 0.7724 |
| 1.0258 | 74000 | 0.0284 | 0.2297 | 0.7740 |
| 1.0328 | 74500 | 0.0273 | 0.2317 | 0.7735 |
| 1.0397 | 75000 | 0.0291 | 0.2352 | 0.7727 |
| 1.0466 | 75500 | 0.0286 | 0.2439 | 0.7724 |
| 1.0536 | 76000 | 0.0268 | 0.2336 | 0.7732 |
| 1.0605 | 76500 | 0.0276 | 0.2298 | 0.7728 |
| 1.0674 | 77000 | 0.0279 | 0.2268 | 0.7726 |
| 1.0743 | 77500 | 0.0283 | 0.2206 | 0.7738 |
| 1.0813 | 78000 | 0.0277 | 0.2263 | 0.7733 |
| 1.0882 | 78500 | 0.0285 | 0.2228 | 0.7740 |
| 1.0951 | 79000 | 0.0283 | 0.2250 | 0.7729 |
| 1.1021 | 79500 | 0.0276 | 0.2200 | 0.7730 |
| 1.1090 | 80000 | 0.0276 | 0.2221 | 0.7739 |
| 1.1159 | 80500 | 0.0268 | 0.2279 | 0.7730 |
| 1.1229 | 81000 | 0.0274 | 0.2302 | 0.7733 |
| 1.1298 | 81500 | 0.0281 | 0.2286 | 0.7736 |
| 1.1367 | 82000 | 0.0267 | 0.2306 | 0.7733 |
| 1.1437 | 82500 | 0.0267 | 0.2348 | 0.7731 |
| 1.1506 | 83000 | 0.0278 | 0.2301 | 0.7729 |
| 1.1575 | 83500 | 0.028 | 0.2240 | 0.7738 |
| 1.1645 | 84000 | 0.0282 | 0.2196 | 0.7744 |
| 1.1714 | 84500 | 0.0264 | 0.2241 | 0.7737 |
| 1.1783 | 85000 | 0.0258 | 0.2252 | 0.7736 |
| 1.1852 | 85500 | 0.027 | 0.2196 | 0.7742 |
| 1.1922 | 86000 | 0.0256 | 0.2189 | 0.7739 |
| 1.1991 | 86500 | 0.0259 | 0.2174 | 0.7749 |
| 1.2060 | 87000 | 0.0262 | 0.2209 | 0.7751 |
| 1.2130 | 87500 | 0.0265 | 0.2202 | 0.7739 |
| 1.2199 | 88000 | 0.025 | 0.2228 | 0.7737 |
| 1.2268 | 88500 | 0.0266 | 0.2233 | 0.7739 |
| 1.2338 | 89000 | 0.0261 | 0.2255 | 0.7736 |
| 1.2407 | 89500 | 0.0271 | 0.2219 | 0.7746 |
| 1.2476 | 90000 | 0.0256 | 0.2185 | 0.7757 |
| 1.2546 | 90500 | 0.0257 | 0.2190 | 0.7758 |
| 1.2615 | 91000 | 0.0239 | 0.2210 | 0.7750 |
| 1.2684 | 91500 | 0.0252 | 0.2236 | 0.7743 |
| 1.2754 | 92000 | 0.0245 | 0.2238 | 0.7743 |
| 1.2823 | 92500 | 0.0267 | 0.2234 | 0.7747 |
| 1.2892 | 93000 | 0.025 | 0.2235 | 0.7746 |
| 1.2961 | 93500 | 0.0246 | 0.2298 | 0.7740 |
| 1.3031 | 94000 | 0.0266 | 0.2239 | 0.7744 |
| 1.3100 | 94500 | 0.0256 | 0.2231 | 0.7740 |
| 1.3169 | 95000 | 0.0265 | 0.2214 | 0.7744 |
| 1.3239 | 95500 | 0.0253 | 0.2221 | 0.7747 |
| 1.3308 | 96000 | 0.0251 | 0.2222 | 0.7742 |
| 1.3377 | 96500 | 0.0244 | 0.2211 | 0.7748 |
| 1.3447 | 97000 | 0.0249 | 0.2216 | 0.7750 |
| 1.3516 | 97500 | 0.0257 | 0.2215 | 0.7745 |
| 1.3585 | 98000 | 0.0263 | 0.2215 | 0.7749 |
| 1.3655 | 98500 | 0.0258 | 0.2209 | 0.7749 |
| 1.3724 | 99000 | 0.0255 | 0.2212 | 0.7748 |
| 1.3793 | 99500 | 0.0252 | 0.2213 | 0.7751 |
| 1.3863 | 100000 | 0.0257 | 0.2213 | 0.7747 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 5.1.0
- Transformers: 4.56.0
- PyTorch: 2.8.0+cu128
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## 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",
}
```
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