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---
language:
- en
license: apache-2.0
tags:
- cross-encoder
- sentence-transformers
- text-classification
- sentence-pair-classification
- semantic-similarity
- semantic-search
- retrieval
- reranking
- generated_from_trainer
- dataset_size:1452533
- loss:MultipleNegativesRankingLoss
base_model: cross-encoder/ms-marco-MiniLM-L6-v2
datasets:
- redis/langcache-sentencepairs-v1
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- accuracy
- accuracy_threshold
- f1
- f1_threshold
- precision
- recall
- average_precision
model-index:
- name: Redis fine-tuned CrossEncoder model for semantic caching on LangCache
results:
- task:
type: cross-encoder-classification
name: Cross Encoder Classification
dataset:
name: test cls
type: test_cls
metrics:
- type: accuracy
value: 0.8275422840650747
name: Accuracy
- type: accuracy_threshold
value: 0.00318145751953125
name: Accuracy Threshold
- type: f1
value: 0.8104219459514619
name: F1
- type: f1_threshold
value: -0.298828125
name: F1 Threshold
- type: precision
value: 0.7457510407211493
name: Precision
- type: recall
value: 0.8873743016759776
name: Recall
- type: average_precision
value: 0.8721928487901052
name: Average Precision
---
# Redis fine-tuned CrossEncoder model for semantic caching on LangCache
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) on the [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for sentence pair classification.
## Model Details
### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) <!-- at revision c5ee24cb16019beea0893ab7796b1df96625c6b8 -->
- **Maximum Sequence Length:** 512 tokens
- **Number of Output Labels:** 1 label
- **Training Dataset:**
- [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
## 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 CrossEncoder
# Download from the 🤗 Hub
model = CrossEncoder("redis/langcache-reranker-v1-miniL6-softmnrl-triplet")
# Get scores for pairs of texts
pairs = [
[' What high potential jobs are there other than computer science?', ' What high potential jobs are there other than computer science?'],
[' Would India ever be able to develop a missile system like S300 or S400 missile?', ' Would India ever be able to develop a missile system like S300 or S400 missile?'],
[' water from the faucet is being drunk by a yellow dog', 'A yellow dog is drinking water from the faucet'],
[' water from the faucet is being drunk by a yellow dog', 'The yellow dog is drinking water from a bottle'],
['! colspan = `` 14 `` `` Players who appeared for Colchester who left during the season ``', '! colspan = `` 14 `` `` Players who appeared for Colchester who left during the season ``'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
' What high potential jobs are there other than computer science?',
[
' What high potential jobs are there other than computer science?',
' Would India ever be able to develop a missile system like S300 or S400 missile?',
'A yellow dog is drinking water from the faucet',
'The yellow dog is drinking water from a bottle',
'! colspan = `` 14 `` `` Players who appeared for Colchester who left during the season ``',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```
<!--
### 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
#### Cross Encoder Classification
* Dataset: `test_cls`
* Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator)
| Metric | Value |
|:----------------------|:-----------|
| accuracy | 0.8275 |
| accuracy_threshold | 0.0032 |
| f1 | 0.8104 |
| f1_threshold | -0.2988 |
| precision | 0.7458 |
| recall | 0.8874 |
| **average_precision** | **0.8722** |
<!--
## 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 (subsets=['all'], train+val=True)
* Dataset: [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1)
* Size: 1,452,533 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative_1 |
|:--------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 24 characters</li><li>mean: 114.25 characters</li><li>max: 268 characters</li></ul> | <ul><li>min: 19 characters</li><li>mean: 114.1 characters</li><li>max: 226 characters</li></ul> | <ul><li>min: 4 characters</li><li>mean: 93.04 characters</li><li>max: 234 characters</li></ul> |
* Samples:
| anchor | positive | negative_1 |
|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|
| <code> Any Canadian teachers (B.Ed. holders) teaching in U.S. schools?</code> | <code> Any Canadian teachers (B.Ed. holders) teaching in U.S. schools?</code> | <code>Are there many Canadians living and working illegally in the United States?</code> |
| <code> Are there any underlying psychological tricks/tactics that are used when designing the lines for rides at amusement parks?</code> | <code> Are there any underlying psychological tricks/tactics that are used when designing the lines for rides at amusement parks?</code> | <code>Is there any tricks for straight lines mcqs?</code> |
| <code> Can I pay with a debit card on PayPal?</code> | <code> Can I pay with a debit card on PayPal?</code> | <code>Can you transfer PayPal funds onto a debit card/credit card?</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"num_negatives": 1,
"activation_fn": "torch.nn.modules.activation.Sigmoid"
}
```
### Evaluation Dataset
#### LangCache Sentence Pairs (split=test)
* Dataset: [LangCache Sentence Pairs (split=test)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1)
* Size: 110,066 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative_1 |
|:--------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 3 characters</li><li>mean: 97.95 characters</li><li>max: 314 characters</li></ul> | <ul><li>min: 3 characters</li><li>mean: 97.03 characters</li><li>max: 314 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 74.49 characters</li><li>max: 295 characters</li></ul> |
* Samples:
| anchor | positive | negative_1 |
|:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
| <code> What high potential jobs are there other than computer science?</code> | <code> What high potential jobs are there other than computer science?</code> | <code>Why IT or Computer Science jobs are being over rated than other Engineering jobs?</code> |
| <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code>Should India buy the Russian S400 air defence missile system?</code> |
| <code> water from the faucet is being drunk by a yellow dog</code> | <code>A yellow dog is drinking water from the faucet</code> | <code>Do you get more homework in 9th grade than 8th?</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"num_negatives": 1,
"activation_fn": "torch.nn.modules.activation.Sigmoid"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 48
- `per_device_eval_batch_size`: 48
- `learning_rate`: 0.0002
- `weight_decay`: 0.001
- `num_train_epochs`: 50
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `optim`: adamw_torch
- `ddp_find_unused_parameters`: False
- `push_to_hub`: True
- `hub_model_id`: redis/langcache-reranker-v1-miniL6-softmnrl-triplet
- `eval_on_start`: 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`: 48
- `per_device_eval_batch_size`: 48
- `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`: 0.0002
- `weight_decay`: 0.001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 50
- `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`: 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`: True
- `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`: adamw_torch
- `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-reranker-v1-miniL6-softmnrl-triplet
- `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`: True
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `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_cls_average_precision |
|:-----------:|:---------:|:-------------:|:---------------:|:--------------------------:|
| 0 | 0 | - | 0.3223 | 0.5734 |
| 0.1322 | 1000 | 0.4286 | 0.3215 | 0.5736 |
| 0.2644 | 2000 | 0.4241 | 0.3151 | 0.5742 |
| 0.3966 | 3000 | 0.4182 | 0.3038 | 0.5755 |
| 0.5288 | 4000 | 0.4036 | 0.2876 | 0.5770 |
| 0.6609 | 5000 | 0.3919 | 0.2619 | 0.5824 |
| 0.7931 | 6000 | 0.3694 | 0.2290 | 0.5908 |
| 0.9253 | 7000 | 0.3481 | 0.1966 | 0.6039 |
| 1.0575 | 8000 | 0.3109 | 0.1650 | 0.6231 |
| 1.1897 | 9000 | 0.2665 | 0.1384 | 0.6565 |
| 1.3219 | 10000 | 0.2281 | 0.1154 | 0.6911 |
| 1.4541 | 11000 | 0.1984 | 0.0928 | 0.7130 |
| 1.5863 | 12000 | 0.1794 | 0.0814 | 0.7341 |
| 1.7184 | 13000 | 0.1619 | 0.0698 | 0.7376 |
| 1.8506 | 14000 | 0.1498 | 0.0619 | 0.7523 |
| 1.9828 | 15000 | 0.1409 | 0.0581 | 0.7584 |
| 2.1150 | 16000 | 0.1315 | 0.0537 | 0.7699 |
| 2.2472 | 17000 | 0.1239 | 0.0495 | 0.7712 |
| 2.3794 | 18000 | 0.1157 | 0.0471 | 0.7847 |
| 2.5116 | 19000 | 0.1093 | 0.0415 | 0.7978 |
| 2.6438 | 20000 | 0.1026 | 0.0428 | 0.8013 |
| 2.7759 | 21000 | 0.0958 | 0.0393 | 0.8096 |
| 2.9081 | 22000 | 0.0922 | 0.0387 | 0.8105 |
| 3.0403 | 23000 | 0.0873 | 0.0415 | 0.8138 |
| 3.1725 | 24000 | 0.0823 | 0.0382 | 0.8178 |
| 3.3047 | 25000 | 0.0807 | 0.0369 | 0.8084 |
| 3.4369 | 26000 | 0.0772 | 0.0370 | 0.8199 |
| 3.5691 | 27000 | 0.0734 | 0.0348 | 0.8261 |
| 3.7013 | 28000 | 0.0709 | 0.0335 | 0.8286 |
| 3.8334 | 29000 | 0.067 | 0.0363 | 0.8374 |
| 3.9656 | 30000 | 0.0675 | 0.0359 | 0.8271 |
| 4.0978 | 31000 | 0.0629 | 0.0337 | 0.8275 |
| 4.2300 | 32000 | 0.0611 | 0.0350 | 0.8378 |
| 4.3622 | 33000 | 0.0618 | 0.0372 | 0.8441 |
| 4.4944 | 34000 | 0.0585 | 0.0341 | 0.8423 |
| 4.6266 | 35000 | 0.0569 | 0.0364 | 0.8469 |
| 4.7588 | 36000 | 0.055 | 0.0355 | 0.8398 |
| 4.8909 | 37000 | 0.0529 | 0.0316 | 0.8474 |
| 5.0231 | 38000 | 0.0522 | 0.0346 | 0.8442 |
| 5.1553 | 39000 | 0.0501 | 0.0384 | 0.8468 |
| 5.2875 | 40000 | 0.0503 | 0.0345 | 0.8534 |
| 5.4197 | 41000 | 0.0487 | 0.0321 | 0.8523 |
| 5.5519 | 42000 | 0.0465 | 0.0321 | 0.8519 |
| 5.6841 | 43000 | 0.0453 | 0.0316 | 0.8527 |
| 5.8163 | 44000 | 0.0426 | 0.0355 | 0.8600 |
| 5.9484 | 45000 | 0.043 | 0.0329 | 0.8527 |
| 6.0806 | 46000 | 0.0405 | 0.0358 | 0.8568 |
| 6.2128 | 47000 | 0.0398 | 0.0345 | 0.8514 |
| 6.3450 | 48000 | 0.0406 | 0.0336 | 0.8499 |
| 6.4772 | 49000 | 0.0381 | 0.0324 | 0.8589 |
| 6.6094 | 50000 | 0.0377 | 0.0322 | 0.8534 |
| 6.7416 | 51000 | 0.0357 | 0.0321 | 0.8518 |
| 6.8738 | 52000 | 0.035 | 0.0338 | 0.8554 |
| 7.0059 | 53000 | 0.035 | 0.0348 | 0.8585 |
| 7.1381 | 54000 | 0.033 | 0.0341 | 0.8582 |
| 7.2703 | 55000 | 0.0347 | 0.0341 | 0.8591 |
| 7.4025 | 56000 | 0.0339 | 0.0327 | 0.8575 |
| 7.5347 | 57000 | 0.0325 | 0.0315 | 0.8636 |
| 7.6669 | 58000 | 0.0313 | 0.0353 | 0.8628 |
| 7.7991 | 59000 | 0.0305 | 0.0353 | 0.8638 |
| 7.9313 | 60000 | 0.0296 | 0.0358 | 0.8641 |
| 8.0635 | 61000 | 0.0292 | 0.0348 | 0.8625 |
| 8.1956 | 62000 | 0.0301 | 0.0366 | 0.8521 |
| 8.3278 | 63000 | 0.03 | 0.0336 | 0.8608 |
| 8.4600 | 64000 | 0.0287 | 0.0336 | 0.8695 |
| 8.5922 | 65000 | 0.0279 | 0.0315 | 0.8627 |
| 8.7244 | 66000 | 0.027 | 0.0322 | 0.8617 |
| 8.8566 | 67000 | 0.026 | 0.0336 | 0.8613 |
| 8.9888 | 68000 | 0.0268 | 0.0369 | 0.8648 |
| 9.1210 | 69000 | 0.0259 | 0.0333 | 0.8646 |
| 9.2531 | 70000 | 0.0261 | 0.0350 | 0.8559 |
| 9.3853 | 71000 | 0.0261 | 0.0332 | 0.8613 |
| 9.5175 | 72000 | 0.0253 | 0.0336 | 0.8666 |
| 9.6497 | 73000 | 0.0252 | 0.0342 | 0.8629 |
| 9.7819 | 74000 | 0.0243 | 0.0348 | 0.8635 |
| 9.9141 | 75000 | 0.0244 | 0.0338 | 0.8656 |
| 10.0463 | 76000 | 0.0238 | 0.0349 | 0.8643 |
| 10.1785 | 77000 | 0.0239 | 0.0359 | 0.8650 |
| 10.3106 | 78000 | 0.0241 | 0.0337 | 0.8628 |
| 10.4428 | 79000 | 0.0236 | 0.0349 | 0.8689 |
| 10.5750 | 80000 | 0.0234 | 0.0348 | 0.8675 |
| 10.7072 | 81000 | 0.0225 | 0.0345 | 0.8668 |
| 10.8394 | 82000 | 0.0217 | 0.0354 | 0.8722 |
| 10.9716 | 83000 | 0.0226 | 0.0339 | 0.8706 |
| 11.1038 | 84000 | 0.0215 | 0.0354 | 0.8680 |
| 11.2360 | 85000 | 0.022 | 0.0364 | 0.8653 |
| 11.3681 | 86000 | 0.022 | 0.0348 | 0.8678 |
| 11.5003 | 87000 | 0.0217 | 0.0353 | 0.8712 |
| 11.6325 | 88000 | 0.0221 | 0.0338 | 0.8682 |
| 11.7647 | 89000 | 0.0213 | 0.0324 | 0.8642 |
| **11.8969** | **90000** | **0.021** | **0.0336** | **0.869** |
| 12.0291 | 91000 | 0.0206 | 0.0352 | 0.8707 |
| 12.1613 | 92000 | 0.0203 | 0.0344 | 0.8686 |
| 12.2935 | 93000 | 0.0207 | 0.0349 | 0.8658 |
| 12.4256 | 94000 | 0.0206 | 0.0339 | 0.8668 |
| 12.5578 | 95000 | 0.0199 | 0.0342 | 0.8687 |
| 12.6900 | 96000 | 0.0202 | 0.0323 | 0.8709 |
| 12.8222 | 97000 | 0.0192 | 0.0357 | 0.8697 |
| 12.9544 | 98000 | 0.0196 | 0.0359 | 0.8716 |
| 13.0866 | 99000 | 0.0196 | 0.0357 | 0.8723 |
| 13.2188 | 100000 | 0.0195 | 0.0347 | 0.8687 |
| 13.3510 | 101000 | 0.0198 | 0.0343 | 0.8681 |
| 13.4831 | 102000 | 0.0192 | 0.0329 | 0.8724 |
| 13.6153 | 103000 | 0.0191 | 0.0336 | 0.8680 |
| 13.7475 | 104000 | 0.0186 | 0.0326 | 0.8685 |
| 13.8797 | 105000 | 0.0183 | 0.0338 | 0.8708 |
| 14.0119 | 106000 | 0.0186 | 0.0346 | 0.8681 |
| 14.1441 | 107000 | 0.0177 | 0.0357 | 0.8698 |
| 14.2763 | 108000 | 0.0193 | 0.0344 | 0.8677 |
| 14.4085 | 109000 | 0.0186 | 0.0323 | 0.8692 |
| 14.5406 | 110000 | 0.018 | 0.0336 | 0.8676 |
| 14.6728 | 111000 | 0.0177 | 0.0353 | 0.8705 |
| 14.8050 | 112000 | 0.0176 | 0.0338 | 0.8704 |
| 14.9372 | 113000 | 0.0178 | 0.0348 | 0.8715 |
| 15.0694 | 114000 | 0.017 | 0.0353 | 0.8707 |
| 15.2016 | 115000 | 0.0181 | 0.0349 | 0.8698 |
| 15.3338 | 116000 | 0.0182 | 0.0341 | 0.8681 |
| 15.4660 | 117000 | 0.0171 | 0.0343 | 0.8689 |
| 15.5981 | 118000 | 0.0176 | 0.0341 | 0.8682 |
| 15.7303 | 119000 | 0.0173 | 0.0336 | 0.8703 |
| 15.8625 | 120000 | 0.0161 | 0.0342 | 0.8701 |
| 15.9947 | 121000 | 0.0174 | 0.0349 | 0.8714 |
| 16.1269 | 122000 | 0.0171 | 0.0341 | 0.8715 |
| 16.2591 | 123000 | 0.0171 | 0.0342 | 0.8669 |
| 16.3913 | 124000 | 0.0174 | 0.0336 | 0.8682 |
| 16.5235 | 125000 | 0.0167 | 0.0339 | 0.8709 |
| 16.6557 | 126000 | 0.0169 | 0.0344 | 0.8703 |
| 16.7878 | 127000 | 0.016 | 0.0341 | 0.8707 |
| 16.9200 | 128000 | 0.0163 | 0.0342 | 0.8717 |
| 17.0522 | 129000 | 0.0163 | 0.0342 | 0.8706 |
| 17.1844 | 130000 | 0.0163 | 0.0347 | 0.8679 |
| 17.3166 | 131000 | 0.017 | 0.0335 | 0.8683 |
| 17.4488 | 132000 | 0.0166 | 0.0337 | 0.8688 |
| 17.5810 | 133000 | 0.0165 | 0.0334 | 0.8706 |
| 17.7132 | 134000 | 0.0157 | 0.0334 | 0.8708 |
| 17.8453 | 135000 | 0.0154 | 0.0345 | 0.8692 |
| 17.9775 | 136000 | 0.0159 | 0.0340 | 0.8719 |
| 18.1097 | 137000 | 0.0156 | 0.0338 | 0.8698 |
| 18.2419 | 138000 | 0.0162 | 0.0333 | 0.8680 |
| 18.3741 | 139000 | 0.0161 | 0.0337 | 0.8694 |
| 18.5063 | 140000 | 0.0161 | 0.0345 | 0.8715 |
| 18.6385 | 141000 | 0.0163 | 0.0331 | 0.8722 |
| 18.7707 | 142000 | 0.015 | 0.0336 | 0.8733 |
| 18.9028 | 143000 | 0.0153 | 0.0350 | 0.8735 |
| 19.0350 | 144000 | 0.0152 | 0.0355 | 0.8722 |
| 19.1672 | 145000 | 0.0158 | 0.0354 | 0.8708 |
| 19.2994 | 146000 | 0.0158 | 0.0345 | 0.8690 |
| 19.4316 | 147000 | 0.0161 | 0.0327 | 0.8705 |
| 19.5638 | 148000 | 0.0155 | 0.0335 | 0.8721 |
| 19.6960 | 149000 | 0.015 | 0.0330 | 0.8709 |
| 19.8282 | 150000 | 0.0143 | 0.0339 | 0.8717 |
| 19.9603 | 151000 | 0.0156 | 0.0340 | 0.8712 |
| 20.0925 | 152000 | 0.0149 | 0.0337 | 0.8709 |
| 20.2247 | 153000 | 0.0154 | 0.0334 | 0.8701 |
| 20.3569 | 154000 | 0.0155 | 0.0337 | 0.8692 |
| 20.4891 | 155000 | 0.0156 | 0.0335 | 0.8708 |
| 20.6213 | 156000 | 0.0153 | 0.0337 | 0.8698 |
| 20.7535 | 157000 | 0.0149 | 0.0328 | 0.8699 |
| 20.8857 | 158000 | 0.0144 | 0.0331 | 0.8691 |
| 21.0178 | 159000 | 0.0148 | 0.0339 | 0.8729 |
| 21.1500 | 160000 | 0.0152 | 0.0331 | 0.8705 |
| 21.2822 | 161000 | 0.0156 | 0.0333 | 0.8690 |
| 21.4144 | 162000 | 0.0147 | 0.0328 | 0.8706 |
| 21.5466 | 163000 | 0.0148 | 0.0335 | 0.8691 |
| 21.6788 | 164000 | 0.0145 | 0.0342 | 0.8698 |
| 21.8110 | 165000 | 0.0142 | 0.0336 | 0.8701 |
| 21.9432 | 166000 | 0.0141 | 0.0346 | 0.8708 |
| 22.0753 | 167000 | 0.0148 | 0.0344 | 0.8713 |
| 22.2075 | 168000 | 0.0151 | 0.0335 | 0.8712 |
| 22.3397 | 169000 | 0.0147 | 0.0344 | 0.8715 |
| 22.4719 | 170000 | 0.0145 | 0.0343 | 0.8711 |
| 22.6041 | 171000 | 0.0144 | 0.0331 | 0.8709 |
| 22.7363 | 172000 | 0.014 | 0.0333 | 0.8716 |
| 22.8685 | 173000 | 0.0142 | 0.0341 | 0.8718 |
| 23.0007 | 174000 | 0.015 | 0.0344 | 0.8717 |
| 23.1328 | 175000 | 0.0141 | 0.0337 | 0.8713 |
| 23.2650 | 176000 | 0.0146 | 0.0336 | 0.8694 |
| 23.3972 | 177000 | 0.0143 | 0.0338 | 0.8700 |
| 23.5294 | 178000 | 0.0147 | 0.0330 | 0.8700 |
| 23.6616 | 179000 | 0.0141 | 0.0334 | 0.8711 |
| 23.7938 | 180000 | 0.0142 | 0.0329 | 0.8707 |
| 23.9260 | 181000 | 0.014 | 0.0338 | 0.8711 |
| 24.0582 | 182000 | 0.0141 | 0.0334 | 0.8726 |
| 24.1904 | 183000 | 0.0143 | 0.0350 | 0.8712 |
| 24.3225 | 184000 | 0.0144 | 0.0340 | 0.8710 |
| 24.4547 | 185000 | 0.015 | 0.0330 | 0.8707 |
| 24.5869 | 186000 | 0.0144 | 0.0341 | 0.8711 |
| 24.7191 | 187000 | 0.0143 | 0.0332 | 0.8707 |
| 24.8513 | 188000 | 0.014 | 0.0345 | 0.8720 |
| 24.9835 | 189000 | 0.0141 | 0.0353 | 0.8718 |
| 25.1157 | 190000 | 0.0137 | 0.0349 | 0.8716 |
| 25.2479 | 191000 | 0.0142 | 0.0345 | 0.8713 |
| 25.3800 | 192000 | 0.0143 | 0.0334 | 0.8706 |
| 25.5122 | 193000 | 0.0137 | 0.0332 | 0.8709 |
| 25.6444 | 194000 | 0.0143 | 0.0339 | 0.8692 |
| 25.7766 | 195000 | 0.0136 | 0.0338 | 0.8706 |
| 25.9088 | 196000 | 0.0134 | 0.0333 | 0.8705 |
| 26.0410 | 197000 | 0.0136 | 0.0350 | 0.8718 |
| 26.1732 | 198000 | 0.0136 | 0.0345 | 0.8713 |
| 26.3054 | 199000 | 0.0142 | 0.0340 | 0.8701 |
| 26.4375 | 200000 | 0.0141 | 0.0335 | 0.8707 |
| 26.5697 | 201000 | 0.0146 | 0.0343 | 0.8707 |
| 26.7019 | 202000 | 0.0136 | 0.0341 | 0.8700 |
| 26.8341 | 203000 | 0.0131 | 0.0348 | 0.8713 |
| 26.9663 | 204000 | 0.014 | 0.0345 | 0.8719 |
| 27.0985 | 205000 | 0.0135 | 0.0349 | 0.8713 |
| 27.2307 | 206000 | 0.0135 | 0.0337 | 0.8714 |
| 27.3629 | 207000 | 0.0146 | 0.0334 | 0.8713 |
| 27.4950 | 208000 | 0.0138 | 0.0337 | 0.8722 |
| 27.6272 | 209000 | 0.0136 | 0.0331 | 0.8709 |
| 27.7594 | 210000 | 0.0133 | 0.0343 | 0.8712 |
| 27.8916 | 211000 | 0.0137 | 0.0341 | 0.8716 |
| 28.0238 | 212000 | 0.0132 | 0.0340 | 0.8730 |
| 28.1560 | 213000 | 0.0136 | 0.0344 | 0.8718 |
| 28.2882 | 214000 | 0.0143 | 0.0337 | 0.8717 |
| 28.4204 | 215000 | 0.0136 | 0.0340 | 0.8716 |
| 28.5525 | 216000 | 0.014 | 0.0334 | 0.8713 |
| 28.6847 | 217000 | 0.0131 | 0.0338 | 0.8714 |
| 28.8169 | 218000 | 0.0131 | 0.0337 | 0.8716 |
| 28.9491 | 219000 | 0.0136 | 0.0346 | 0.8715 |
| 29.0813 | 220000 | 0.0132 | 0.0347 | 0.8722 |
| 29.2135 | 221000 | 0.0136 | 0.0344 | 0.8719 |
| 29.3457 | 222000 | 0.0137 | 0.0345 | 0.8710 |
| 29.4779 | 223000 | 0.0138 | 0.0337 | 0.8708 |
| 29.6100 | 224000 | 0.013 | 0.0337 | 0.8708 |
| 29.7422 | 225000 | 0.0134 | 0.0343 | 0.8714 |
| 29.8744 | 226000 | 0.0132 | 0.0338 | 0.8717 |
| 30.0066 | 227000 | 0.0133 | 0.0335 | 0.8718 |
| 30.1388 | 228000 | 0.013 | 0.0340 | 0.8718 |
| 30.2710 | 229000 | 0.0144 | 0.0332 | 0.8710 |
| 30.4032 | 230000 | 0.014 | 0.0346 | 0.8716 |
| 30.5354 | 231000 | 0.0137 | 0.0330 | 0.8717 |
| 30.6675 | 232000 | 0.0131 | 0.0342 | 0.8718 |
| 30.7997 | 233000 | 0.0128 | 0.0337 | 0.8721 |
| 30.9319 | 234000 | 0.0135 | 0.0342 | 0.8718 |
| 31.0641 | 235000 | 0.0138 | 0.0346 | 0.8720 |
| 31.1963 | 236000 | 0.0133 | 0.0347 | 0.8717 |
| 31.3285 | 237000 | 0.0137 | 0.0335 | 0.8712 |
| 31.4607 | 238000 | 0.0137 | 0.0337 | 0.8718 |
| 31.5929 | 239000 | 0.0131 | 0.0340 | 0.8719 |
| 31.7250 | 240000 | 0.0129 | 0.0334 | 0.8720 |
| 31.8572 | 241000 | 0.0133 | 0.0336 | 0.8725 |
| 31.9894 | 242000 | 0.0137 | 0.0343 | 0.8722 |
| 32.1216 | 243000 | 0.0132 | 0.0329 | 0.8720 |
| 32.2538 | 244000 | 0.0135 | 0.0338 | 0.8718 |
| 32.3860 | 245000 | 0.0129 | 0.0344 | 0.8724 |
| 32.5182 | 246000 | 0.0136 | 0.0342 | 0.8722 |
| 32.6504 | 247000 | 0.0133 | 0.0331 | 0.8716 |
| 32.7826 | 248000 | 0.0128 | 0.0337 | 0.8718 |
| 32.9147 | 249000 | 0.0127 | 0.0338 | 0.8724 |
| 33.0469 | 250000 | 0.013 | 0.0328 | 0.8724 |
| 33.1791 | 251000 | 0.0135 | 0.0337 | 0.8724 |
| 33.3113 | 252000 | 0.0131 | 0.0334 | 0.8723 |
| 33.4435 | 253000 | 0.0134 | 0.0339 | 0.8726 |
| 33.5757 | 254000 | 0.0135 | 0.0338 | 0.8725 |
| 33.7079 | 255000 | 0.013 | 0.0341 | 0.8730 |
| 33.8401 | 256000 | 0.0126 | 0.0334 | 0.8731 |
| 33.9722 | 257000 | 0.0136 | 0.0338 | 0.8730 |
| 34.1044 | 258000 | 0.0123 | 0.0338 | 0.8727 |
| 34.2366 | 259000 | 0.0135 | 0.0336 | 0.8724 |
| 34.3688 | 260000 | 0.0136 | 0.0343 | 0.8722 |
| 34.5010 | 261000 | 0.0134 | 0.0341 | 0.8723 |
| 34.6332 | 262000 | 0.0136 | 0.0343 | 0.8718 |
| 34.7654 | 263000 | 0.0131 | 0.0344 | 0.8721 |
| 34.8976 | 264000 | 0.0128 | 0.0343 | 0.8724 |
| 35.0297 | 265000 | 0.0129 | 0.0336 | 0.8725 |
| 35.1619 | 266000 | 0.0128 | 0.0334 | 0.8726 |
| 35.2941 | 267000 | 0.013 | 0.0340 | 0.8723 |
| 35.4263 | 268000 | 0.0133 | 0.0341 | 0.8723 |
| 35.5585 | 269000 | 0.0132 | 0.0331 | 0.8722 |
| 35.6907 | 270000 | 0.0127 | 0.0335 | 0.8721 |
| 35.8229 | 271000 | 0.0123 | 0.0334 | 0.8725 |
| 35.9551 | 272000 | 0.0135 | 0.0343 | 0.8726 |
| 36.0872 | 273000 | 0.0125 | 0.0345 | 0.8724 |
| 36.2194 | 274000 | 0.0134 | 0.0336 | 0.8722 |
| 36.3516 | 275000 | 0.0132 | 0.0338 | 0.8721 |
| 36.4838 | 276000 | 0.0136 | 0.0331 | 0.8722 |
| 36.6160 | 277000 | 0.0133 | 0.0335 | 0.8718 |
| 36.7482 | 278000 | 0.0125 | 0.0336 | 0.8721 |
| 36.8804 | 279000 | 0.0122 | 0.0344 | 0.8721 |
| 37.0126 | 280000 | 0.013 | 0.0336 | 0.8725 |
| 37.1447 | 281000 | 0.0132 | 0.0333 | 0.8726 |
| 37.2769 | 282000 | 0.0137 | 0.0333 | 0.8722 |
| 37.4091 | 283000 | 0.0133 | 0.0339 | 0.8723 |
| 37.5413 | 284000 | 0.013 | 0.0335 | 0.8723 |
| 37.6735 | 285000 | 0.0129 | 0.0329 | 0.8721 |
| 37.8057 | 286000 | 0.013 | 0.0327 | 0.8721 |
| 37.9379 | 287000 | 0.0124 | 0.0338 | 0.8722 |
| 38.0701 | 288000 | 0.0131 | 0.0338 | 0.8722 |
| 38.2022 | 289000 | 0.0129 | 0.0342 | 0.8722 |
| 38.3344 | 290000 | 0.013 | 0.0336 | 0.8721 |
| 38.4666 | 291000 | 0.0134 | 0.0335 | 0.8722 |
| 38.5988 | 292000 | 0.0129 | 0.0338 | 0.8720 |
| 38.7310 | 293000 | 0.0122 | 0.0337 | 0.8720 |
| 38.8632 | 294000 | 0.0123 | 0.0338 | 0.8722 |
| 38.9954 | 295000 | 0.0132 | 0.0335 | 0.8723 |
| 39.1276 | 296000 | 0.0128 | 0.0333 | 0.8722 |
| 39.2597 | 297000 | 0.0135 | 0.0336 | 0.8721 |
| 39.3919 | 298000 | 0.0132 | 0.0342 | 0.8722 |
| 39.5241 | 299000 | 0.0136 | 0.0328 | 0.8723 |
| 39.6563 | 300000 | 0.0125 | 0.0339 | 0.8722 |
| 39.7885 | 301000 | 0.0125 | 0.0343 | 0.8722 |
| 39.9207 | 302000 | 0.0126 | 0.0339 | 0.8723 |
| 40.0529 | 303000 | 0.0129 | 0.0338 | 0.8723 |
| 40.1851 | 304000 | 0.0133 | 0.0334 | 0.8723 |
| 40.3173 | 305000 | 0.0134 | 0.0336 | 0.8723 |
| 40.4494 | 306000 | 0.0127 | 0.0336 | 0.8724 |
| 40.5816 | 307000 | 0.0126 | 0.0342 | 0.8723 |
| 40.7138 | 308000 | 0.013 | 0.0340 | 0.8721 |
| 40.8460 | 309000 | 0.013 | 0.0332 | 0.8721 |
| 40.9782 | 310000 | 0.0129 | 0.0337 | 0.8723 |
| 41.1104 | 311000 | 0.0123 | 0.0328 | 0.8723 |
| 41.2426 | 312000 | 0.013 | 0.0336 | 0.8723 |
| 41.3748 | 313000 | 0.0132 | 0.0337 | 0.8722 |
| 41.5069 | 314000 | 0.0132 | 0.0335 | 0.8722 |
| 41.6391 | 315000 | 0.0131 | 0.0343 | 0.8722 |
| 41.7713 | 316000 | 0.0122 | 0.0339 | 0.8722 |
| 41.9035 | 317000 | 0.0125 | 0.0340 | 0.8722 |
| 42.0357 | 318000 | 0.0122 | 0.0342 | 0.8722 |
| 42.1679 | 319000 | 0.0129 | 0.0337 | 0.8721 |
| 42.3001 | 320000 | 0.013 | 0.0330 | 0.8721 |
| 42.4323 | 321000 | 0.013 | 0.0332 | 0.8721 |
| 42.5644 | 322000 | 0.0141 | 0.0349 | 0.8721 |
| 42.6966 | 323000 | 0.013 | 0.0334 | 0.8720 |
| 42.8288 | 324000 | 0.0125 | 0.0339 | 0.8721 |
| 42.9610 | 325000 | 0.0126 | 0.0342 | 0.8721 |
| 43.0932 | 326000 | 0.0127 | 0.0339 | 0.8721 |
| 43.2254 | 327000 | 0.0126 | 0.0330 | 0.8721 |
| 43.3576 | 328000 | 0.013 | 0.0343 | 0.8721 |
| 43.4898 | 329000 | 0.0135 | 0.0334 | 0.8721 |
| 43.6219 | 330000 | 0.0131 | 0.0327 | 0.8721 |
| 43.7541 | 331000 | 0.0124 | 0.0334 | 0.8722 |
| 43.8863 | 332000 | 0.0126 | 0.0344 | 0.8721 |
| 44.0185 | 333000 | 0.0131 | 0.0338 | 0.8722 |
| 44.1507 | 334000 | 0.0121 | 0.0340 | 0.8722 |
| 44.2829 | 335000 | 0.0131 | 0.0336 | 0.8721 |
| 44.4151 | 336000 | 0.0135 | 0.0340 | 0.8722 |
| 44.5473 | 337000 | 0.0131 | 0.0335 | 0.8722 |
| 44.6794 | 338000 | 0.0132 | 0.0340 | 0.8722 |
| 44.8116 | 339000 | 0.0128 | 0.0333 | 0.8722 |
| 44.9438 | 340000 | 0.0124 | 0.0333 | 0.8722 |
| 45.0760 | 341000 | 0.0131 | 0.0337 | 0.8722 |
| 45.2082 | 342000 | 0.0129 | 0.0341 | 0.8722 |
| 45.3404 | 343000 | 0.0133 | 0.0335 | 0.8722 |
| 45.4726 | 344000 | 0.0133 | 0.0341 | 0.8722 |
| 45.6048 | 345000 | 0.013 | 0.0334 | 0.8722 |
| 45.7369 | 346000 | 0.0129 | 0.0343 | 0.8722 |
| 45.8691 | 347000 | 0.0125 | 0.0335 | 0.8722 |
| 46.0013 | 348000 | 0.0133 | 0.0344 | 0.8722 |
| 46.1335 | 349000 | 0.013 | 0.0332 | 0.8722 |
| 46.2657 | 350000 | 0.0128 | 0.0337 | 0.8722 |
| 46.3979 | 351000 | 0.0132 | 0.0334 | 0.8722 |
| 46.5301 | 352000 | 0.0127 | 0.0343 | 0.8722 |
| 46.6623 | 353000 | 0.0127 | 0.0334 | 0.8722 |
| 46.7944 | 354000 | 0.0126 | 0.0332 | 0.8722 |
| 46.9266 | 355000 | 0.013 | 0.0339 | 0.8722 |
| 47.0588 | 356000 | 0.0126 | 0.0340 | 0.8722 |
| 47.1910 | 357000 | 0.0132 | 0.0336 | 0.8722 |
| 47.3232 | 358000 | 0.0138 | 0.0334 | 0.8722 |
| 47.4554 | 359000 | 0.0133 | 0.0336 | 0.8722 |
| 47.5876 | 360000 | 0.0135 | 0.0340 | 0.8722 |
| 47.7198 | 361000 | 0.0129 | 0.0341 | 0.8722 |
| 47.8519 | 362000 | 0.0123 | 0.0334 | 0.8722 |
| 47.9841 | 363000 | 0.0126 | 0.0334 | 0.8722 |
| 48.1163 | 364000 | 0.0121 | 0.0337 | 0.8722 |
| 48.2485 | 365000 | 0.0127 | 0.0342 | 0.8722 |
| 48.3807 | 366000 | 0.0124 | 0.0336 | 0.8722 |
| 48.5129 | 367000 | 0.0125 | 0.0338 | 0.8722 |
| 48.6451 | 368000 | 0.0125 | 0.0341 | 0.8721 |
| 48.7773 | 369000 | 0.0122 | 0.0333 | 0.8722 |
| 48.9095 | 370000 | 0.0123 | 0.0336 | 0.8722 |
| 49.0416 | 371000 | 0.0124 | 0.0341 | 0.8722 |
| 49.1738 | 372000 | 0.0132 | 0.0330 | 0.8722 |
| 49.3060 | 373000 | 0.0128 | 0.0342 | 0.8722 |
| 49.4382 | 374000 | 0.0132 | 0.0341 | 0.8722 |
| 49.5704 | 375000 | 0.013 | 0.0334 | 0.8722 |
| 49.7026 | 376000 | 0.0126 | 0.0340 | 0.8722 |
| 49.8348 | 377000 | 0.0126 | 0.0337 | 0.8722 |
| 49.9670 | 378000 | 0.0131 | 0.0337 | 0.8722 |
* 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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