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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:1047690
- loss:BinaryCrossEntropyLoss
base_model: Alibaba-NLP/gte-reranker-modernbert-base
datasets:
- aditeyabaral-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: val
type: val
metrics:
- type: accuracy
value: 0.773111243307555
name: Accuracy
- type: accuracy_threshold
value: 0.7637044787406921
name: Accuracy Threshold
- type: f1
value: 0.6950724637681159
name: F1
- type: f1_threshold
value: 0.04638597369194031
name: F1 Threshold
- type: precision
value: 0.6454912516823688
name: Precision
- type: recall
value: 0.7529042386185243
name: Recall
- type: average_precision
value: 0.7833280130154174
name: Average Precision
- task:
type: cross-encoder-classification
name: Cross Encoder Classification
dataset:
name: test
type: test
metrics:
- type: accuracy
value: 0.7230292965285952
name: Accuracy
- type: accuracy_threshold
value: 0.9352303147315979
name: Accuracy Threshold
- type: f1
value: 0.7144263194410831
name: F1
- type: f1_threshold
value: 0.9142870903015137
name: F1 Threshold
- type: precision
value: 0.6302559284880577
name: Precision
- type: recall
value: 0.8245437616387337
name: Recall
- type: average_precision
value: 0.6906882331078481
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 [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) on the [LangCache Sentence Pairs (all)](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:** [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) <!-- at revision f7481e6055501a30fb19d090657df9ec1f79ab2c -->
- **Maximum Sequence Length:** 8192 tokens
- **Number of Output Labels:** 1 label
- **Training Dataset:**
- [LangCache Sentence Pairs (all)](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("aditeyabaral-redis/langcache-reranker-v1-wdwr")
# Get scores for pairs of texts
pairs = [
["He said the foodservice pie business doesn 't fit the company 's long-term growth strategy .", '" The foodservice pie business does not fit our long-term growth strategy .'],
['Magnarelli said Racicot hated the Iraqi regime and looked forward to using his long years of training in the war .', 'His wife said he was " 100 percent behind George Bush " and looked forward to using his years of training in the war .'],
['The dollar was at 116.92 yen against the yen , flat on the session , and at 1.2891 against the Swiss franc , also flat .', 'The dollar was at 116.78 yen JPY = , virtually flat on the session , and at 1.2871 against the Swiss franc CHF = , down 0.1 percent .'],
['The AFL-CIO is waiting until October to decide if it will endorse a candidate .', 'The AFL-CIO announced Wednesday that it will decide in October whether to endorse a candidate before the primaries .'],
['No dates have been set for the civil or the criminal trial .', 'No dates have been set for the criminal or civil cases , but Shanley has pleaded not guilty .'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
"He said the foodservice pie business doesn 't fit the company 's long-term growth strategy .",
[
'" The foodservice pie business does not fit our long-term growth strategy .',
'His wife said he was " 100 percent behind George Bush " and looked forward to using his years of training in the war .',
'The dollar was at 116.78 yen JPY = , virtually flat on the session , and at 1.2871 against the Swiss franc CHF = , down 0.1 percent .',
'The AFL-CIO announced Wednesday that it will decide in October whether to endorse a candidate before the primaries .',
'No dates have been set for the criminal or civil cases , but Shanley has pleaded not guilty .',
]
)
# [{'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
* Datasets: `val` and `test`
* Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator)
| Metric | val | test |
|:----------------------|:-----------|:-----------|
| accuracy | 0.7731 | 0.723 |
| accuracy_threshold | 0.7637 | 0.9352 |
| f1 | 0.6951 | 0.7144 |
| f1_threshold | 0.0464 | 0.9143 |
| precision | 0.6455 | 0.6303 |
| recall | 0.7529 | 0.8245 |
| **average_precision** | **0.7833** | **0.6907** |
<!--
## 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/aditeyabaral-redis/langcache-sentencepairs-v1)
* Size: 8,405 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 28 characters</li><li>mean: 116.35 characters</li><li>max: 227 characters</li></ul> | <ul><li>min: 15 characters</li><li>mean: 113.13 characters</li><li>max: 243 characters</li></ul> | <ul><li>0: ~45.80%</li><li>1: ~54.20%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>He said the foodservice pie business doesn 't fit the company 's long-term growth strategy .</code> | <code>" The foodservice pie business does not fit our long-term growth strategy .</code> | <code>1</code> |
| <code>Magnarelli said Racicot hated the Iraqi regime and looked forward to using his long years of training in the war .</code> | <code>His wife said he was " 100 percent behind George Bush " and looked forward to using his years of training in the war .</code> | <code>0</code> |
| <code>The dollar was at 116.92 yen against the yen , flat on the session , and at 1.2891 against the Swiss franc , also flat .</code> | <code>The dollar was at 116.78 yen JPY = , virtually flat on the session , and at 1.2871 against the Swiss franc CHF = , down 0.1 percent .</code> | <code>0</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": null
}
```
### Evaluation Dataset
#### LangCache Sentence Pairs (all)
* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1)
* Size: 8,405 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 28 characters</li><li>mean: 116.35 characters</li><li>max: 227 characters</li></ul> | <ul><li>min: 15 characters</li><li>mean: 113.13 characters</li><li>max: 243 characters</li></ul> | <ul><li>0: ~45.80%</li><li>1: ~54.20%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>He said the foodservice pie business doesn 't fit the company 's long-term growth strategy .</code> | <code>" The foodservice pie business does not fit our long-term growth strategy .</code> | <code>1</code> |
| <code>Magnarelli said Racicot hated the Iraqi regime and looked forward to using his long years of training in the war .</code> | <code>His wife said he was " 100 percent behind George Bush " and looked forward to using his years of training in the war .</code> | <code>0</code> |
| <code>The dollar was at 116.92 yen against the yen , flat on the session , and at 1.2891 against the Swiss franc , also flat .</code> | <code>The dollar was at 116.78 yen JPY = , virtually flat on the session , and at 1.2871 against the Swiss franc CHF = , down 0.1 percent .</code> | <code>0</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": null
}
```
### 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`: 0.01
- `num_train_epochs`: 20
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `optim`: adamw_torch
- `push_to_hub`: True
- `hub_model_id`: aditeyabaral-redis/langcache-reranker-v1-wdwr
#### 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`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 20
- `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}
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: aditeyabaral-redis/langcache-reranker-v1-wdwr
- `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`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `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 | val_average_precision | test_average_precision |
|:----------:|:--------:|:-------------:|:---------------:|:---------------------:|:----------------------:|
| -1 | -1 | - | - | 0.7676 | 0.6907 |
| 0.1833 | 1000 | 0.3563 | 0.4805 | 0.7831 | - |
| **0.3666** | **2000** | **0.2065** | **0.5394** | **0.8221** | **-** |
| 0.5499 | 3000 | 0.1983 | 0.5019 | 0.8178 | - |
| 0.7331 | 4000 | 0.1923 | 0.5109 | 0.7960 | - |
| 0.9164 | 5000 | 0.1886 | 0.4726 | 0.8058 | - |
| 1.0997 | 6000 | 0.183 | 0.5062 | 0.8032 | - |
| 1.2830 | 7000 | 0.1838 | 0.5152 | 0.8021 | - |
| 1.4663 | 8000 | 0.1858 | 0.5105 | 0.7926 | - |
| 1.6496 | 9000 | 0.1905 | 0.5052 | 0.7859 | - |
| 1.8328 | 10000 | 0.1926 | 0.5316 | 0.7895 | - |
| 2.0161 | 11000 | 0.1951 | 0.5340 | 0.7681 | - |
| 2.1994 | 12000 | 0.1853 | 0.5573 | 0.7577 | - |
| 2.3827 | 13000 | 0.1848 | 0.5530 | 0.7946 | - |
| 2.5660 | 14000 | 0.1813 | 0.5754 | 0.7655 | - |
| 2.7493 | 15000 | 0.1793 | 0.5316 | 0.7514 | - |
| 2.9326 | 16000 | 0.1778 | 0.5230 | 0.7868 | - |
| 3.1158 | 17000 | 0.1681 | 0.5246 | 0.7816 | - |
| 3.2991 | 18000 | 0.1662 | 0.4946 | 0.7732 | - |
| 3.4824 | 19000 | 0.1648 | 0.5262 | 0.7853 | - |
| 3.6657 | 20000 | 0.1649 | 0.5007 | 0.7871 | - |
| 3.8490 | 21000 | 0.1633 | 0.5368 | 0.7807 | - |
| 4.0323 | 22000 | 0.1602 | 0.5559 | 0.7769 | - |
| 4.2155 | 23000 | 0.149 | 0.5796 | 0.7697 | - |
| 4.3988 | 24000 | 0.1486 | 0.5322 | 0.7608 | - |
| 4.5821 | 25000 | 0.1495 | 0.5142 | 0.7713 | - |
| 4.7654 | 26000 | 0.1493 | 0.5203 | 0.7866 | - |
| 4.9487 | 27000 | 0.1498 | 0.5433 | 0.7738 | - |
| 5.1320 | 28000 | 0.1391 | 0.5589 | 0.7803 | - |
| 5.3152 | 29000 | 0.1346 | 0.5267 | 0.7713 | - |
| 5.4985 | 30000 | 0.1367 | 0.5657 | 0.7803 | - |
| 5.6818 | 31000 | 0.1358 | 0.5631 | 0.7646 | - |
| 5.8651 | 32000 | 0.136 | 0.5444 | 0.7753 | - |
| 6.0484 | 33000 | 0.1346 | 0.5605 | 0.7703 | - |
| 6.2317 | 34000 | 0.1222 | 0.5399 | 0.7776 | - |
| 6.4150 | 35000 | 0.1241 | 0.5272 | 0.7899 | - |
| 6.5982 | 36000 | 0.1243 | 0.6096 | 0.7723 | - |
| 6.7815 | 37000 | 0.1266 | 0.5661 | 0.7609 | - |
| 6.9648 | 38000 | 0.1246 | 0.5341 | 0.7889 | - |
| 7.1481 | 39000 | 0.1128 | 0.6223 | 0.7884 | - |
| 7.3314 | 40000 | 0.1124 | 0.5485 | 0.7743 | - |
| 7.5147 | 41000 | 0.1127 | 0.5375 | 0.7842 | - |
| 7.6979 | 42000 | 0.1122 | 0.5231 | 0.7939 | - |
| 7.8812 | 43000 | 0.1141 | 0.5608 | 0.7705 | - |
| 8.0645 | 44000 | 0.1088 | 0.6511 | 0.7813 | - |
| 8.2478 | 45000 | 0.0998 | 0.6217 | 0.7648 | - |
| 8.4311 | 46000 | 0.1017 | 0.6000 | 0.7822 | - |
| 8.6144 | 47000 | 0.1031 | 0.5469 | 0.7866 | - |
| 8.7977 | 48000 | 0.1012 | 0.5862 | 0.7790 | - |
| 8.9809 | 49000 | 0.1031 | 0.5527 | 0.7876 | - |
| 9.1642 | 50000 | 0.0921 | 0.5460 | 0.7788 | - |
| 9.3475 | 51000 | 0.0909 | 0.5820 | 0.7815 | - |
| 9.5308 | 52000 | 0.0919 | 0.5589 | 0.7841 | - |
| 9.7141 | 53000 | 0.0939 | 0.5521 | 0.7821 | - |
| 9.8974 | 54000 | 0.0925 | 0.6942 | 0.7797 | - |
| 10.0806 | 55000 | 0.0863 | 0.6208 | 0.7729 | - |
| 10.2639 | 56000 | 0.0803 | 0.6632 | 0.7911 | - |
| 10.4472 | 57000 | 0.0797 | 0.6583 | 0.7833 | - |
| 10.6305 | 58000 | 0.0824 | 0.6194 | 0.7862 | - |
| 10.8138 | 59000 | 0.0829 | 0.6136 | 0.7783 | - |
| 10.9971 | 60000 | 0.0819 | 0.5833 | 0.7727 | - |
| 11.1804 | 61000 | 0.0693 | 0.6491 | 0.7881 | - |
| 11.3636 | 62000 | 0.0709 | 0.6449 | 0.7784 | - |
| 11.5469 | 63000 | 0.0721 | 0.6158 | 0.7838 | - |
| 11.7302 | 64000 | 0.0721 | 0.6649 | 0.7841 | - |
| 11.9135 | 65000 | 0.0732 | 0.6403 | 0.7702 | - |
| 12.0968 | 66000 | 0.0679 | 0.6079 | 0.7817 | - |
| 12.2801 | 67000 | 0.0615 | 0.6862 | 0.7787 | - |
| 12.4633 | 68000 | 0.0629 | 0.7239 | 0.7824 | - |
| 12.6466 | 69000 | 0.0643 | 0.6419 | 0.7897 | - |
| 12.8299 | 70000 | 0.0635 | 0.6743 | 0.7762 | - |
| 13.0132 | 71000 | 0.064 | 0.7135 | 0.7741 | - |
| 13.1965 | 72000 | 0.0545 | 0.6643 | 0.7723 | - |
| 13.3798 | 73000 | 0.0548 | 0.6508 | 0.7758 | - |
| 13.5630 | 74000 | 0.0547 | 0.7003 | 0.7785 | - |
| 13.7463 | 75000 | 0.0548 | 0.7170 | 0.7846 | - |
| 13.9296 | 76000 | 0.0553 | 0.6917 | 0.7722 | - |
| 14.1129 | 77000 | 0.0508 | 0.7000 | 0.7767 | - |
| 14.2962 | 78000 | 0.0474 | 0.7336 | 0.7730 | - |
| 14.4795 | 79000 | 0.0465 | 0.7122 | 0.7795 | - |
| 14.6628 | 80000 | 0.0478 | 0.7321 | 0.7779 | - |
| 14.8460 | 81000 | 0.0468 | 0.7112 | 0.7796 | - |
| 15.0293 | 82000 | 0.0465 | 0.7534 | 0.7788 | - |
| 15.2126 | 83000 | 0.0395 | 0.7238 | 0.7808 | - |
| 15.3959 | 84000 | 0.0401 | 0.7686 | 0.7905 | - |
| 15.5792 | 85000 | 0.0408 | 0.7296 | 0.7900 | - |
| 15.7625 | 86000 | 0.0414 | 0.7533 | 0.7822 | - |
| 15.9457 | 87000 | 0.0402 | 0.7748 | 0.7867 | - |
| 16.1290 | 88000 | 0.0352 | 0.8267 | 0.7844 | - |
| 16.3123 | 89000 | 0.0354 | 0.7488 | 0.7912 | - |
| 16.4956 | 90000 | 0.0337 | 0.7850 | 0.7857 | - |
| 16.6789 | 91000 | 0.0333 | 0.7812 | 0.7815 | - |
| 16.8622 | 92000 | 0.0341 | 0.8184 | 0.7786 | - |
| 17.0455 | 93000 | 0.0333 | 0.8166 | 0.7781 | - |
| 17.2287 | 94000 | 0.0288 | 0.7980 | 0.7803 | - |
| 17.4120 | 95000 | 0.0282 | 0.8195 | 0.7774 | - |
| 17.5953 | 96000 | 0.0285 | 0.7864 | 0.7829 | - |
| 17.7786 | 97000 | 0.0284 | 0.8000 | 0.7838 | - |
| 17.9619 | 98000 | 0.0279 | 0.8118 | 0.7873 | - |
| 18.1452 | 99000 | 0.0245 | 0.8727 | 0.7866 | - |
| 18.3284 | 100000 | 0.0235 | 0.8695 | 0.7836 | - |
| 18.5117 | 101000 | 0.0236 | 0.8246 | 0.7820 | - |
| 18.6950 | 102000 | 0.0232 | 0.8543 | 0.7828 | - |
| 18.8783 | 103000 | 0.0234 | 0.8840 | 0.7793 | - |
| 19.0616 | 104000 | 0.0219 | 0.8804 | 0.7783 | - |
| 19.2449 | 105000 | 0.0201 | 0.8885 | 0.7812 | - |
| 19.4282 | 106000 | 0.0194 | 0.8901 | 0.7821 | - |
| 19.6114 | 107000 | 0.0197 | 0.8850 | 0.7824 | - |
| 19.7947 | 108000 | 0.0196 | 0.8835 | 0.7830 | - |
| 19.9780 | 109000 | 0.0197 | 0.8803 | 0.7833 | - |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 5.1.0
- Transformers: 4.55.0
- PyTorch: 2.8.0+cu128
- Accelerate: 1.10.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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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