Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
biencoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:483820
loss:MultipleNegativesSymmetricRankingLoss
Eval Results
text-embeddings-inference
File size: 35,362 Bytes
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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:483820
- loss:MultipleNegativesSymmetricRankingLoss
base_model: Alibaba-NLP/gte-modernbert-base
widget:
- source_sentence: 'See Precambrian time scale # Proposed Geologic timeline for another
set of periods 4600 -- 541 MYA .'
sentences:
- In 2014 election , Biju Janata Dal candidate Tathagat Satapathy Bharatiya Janata
party candidate Rudra Narayan Pany defeated with a margin of 1.37,340 votes .
- In Scotland , the Strathclyde Partnership for Transport , formerly known as Strathclyde
Passenger Transport Executive , comprises the former Strathclyde region , which
includes the urban area around Glasgow .
- 'See Precambrian Time Scale # Proposed Geological Timeline for another set of
periods of 4600 -- 541 MYA .'
- source_sentence: It is also 5 kilometers northeast of Tamaqua , 27 miles south of
Allentown and 9 miles northwest of Hazleton .
sentences:
- In 1948 he moved to Massachusetts , and eventually settled in Vermont .
- Suddenly I remembered that I was a New Zealander , I caught the first plane home
and came back .
- It is also 5 miles northeast of Tamaqua , 27 miles south of Allentown , and 9
miles northwest of Hazleton .
- source_sentence: The party has a Member of Parliament , a member of the House of
Lords , three members of the London Assembly and two Members of the European Parliament
.
sentences:
- The party has one Member of Parliament , one member of the House of Lords , three
Members of the London Assembly and two Members of the European Parliament .
- Grapsid crabs dominate in Australia , Malaysia and Panama , while gastropods Cerithidea
scalariformis and Melampus coeffeus are important seed predators in Florida mangroves
.
- Music Story is a music service website and international music data provider that
curates , aggregates and analyses metadata for digital music services .
- source_sentence: 'The play received two 1969 Tony Award nominations : Best Actress
in a Play ( Michael Annals ) and Best Costume Design ( Charlotte Rae ) .'
sentences:
- Ravishanker is a fellow of the International Statistical Institute and an elected
member of the American Statistical Association .
- 'In 1969 , the play received two Tony - Award nominations : Best Actress in a
Theatre Play ( Michael Annals ) and Best Costume Design ( Charlotte Rae ) .'
- AMD and Nvidia both have proprietary methods of scaling , CrossFireX for AMD ,
and SLI for Nvidia .
- source_sentence: He was a close friend of Ángel Cabrera and is a cousin of golfer
Tony Croatto .
sentences:
- He was a close friend of Ángel Cabrera , and is a cousin of golfer Tony Croatto
.
- Eugenijus Bartulis ( born December 7 , 1949 in Kaunas ) is a Lithuanian Roman
Catholic priest , and Bishop of Šiauliai .
- UWIRE also distributes its members content to professional media outlets , including
Yahoo , CNN and CBS News .
datasets:
- redis/langcache-sentencepairs-v1
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: Redis fine-tuned BiEncoder model for semantic caching on LangCache
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: test
type: test
metrics:
- type: cosine_accuracy
value: 0.7276245142774221
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8017503619194031
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.723032161181329
name: Cosine F1
- type: cosine_f1_threshold
value: 0.7345461845397949
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.6233076217703221
name: Cosine Precision
- type: cosine_recall
value: 0.8607448789571694
name: Cosine Recall
- type: cosine_ap
value: 0.7251364855292874
name: Cosine Ap
- type: cosine_mcc
value: 0.4684913821533736
name: Cosine Mcc
---
# Redis fine-tuned BiEncoder model for semantic caching on LangCache
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) 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:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 -->
- **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-v1)
- **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': True, 'pooling_mode_mean_tokens': False, '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 = [
'He was a close friend of Ángel Cabrera and is a cousin of golfer Tony Croatto .',
'He was a close friend of Ángel Cabrera , and is a cousin of golfer Tony Croatto .',
'UWIRE also distributes its members content to professional media outlets , including Yahoo , CNN and CBS News .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[0.9961, 0.9961, 0.1250],
# [0.9961, 0.9961, 0.1162],
# [0.1250, 0.1162, 1.0078]], 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>
-->
<!--
### 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
#### Binary Classification
* Dataset: `test`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:--------------------------|:-----------|
| cosine_accuracy | 0.7276 |
| cosine_accuracy_threshold | 0.8018 |
| cosine_f1 | 0.723 |
| cosine_f1_threshold | 0.7345 |
| cosine_precision | 0.6233 |
| cosine_recall | 0.8607 |
| **cosine_ap** | **0.7251** |
| cosine_mcc | 0.4685 |
<!--
## 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-v1)
* Size: 26,850 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: 8 tokens</li><li>mean: 27.35 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:----------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <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>1</code> |
| <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code> | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code> | <code>1</code> |
| <code>The 12F was officially homologated on August 21 , 1929 and exhibited at the Paris Salon in 1930 .</code> | <code>The 12F was officially homologated on 21 August 1929 and displayed at the 1930 Paris Salon .</code> | <code>1</code> |
* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) 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-v1)
* Size: 26,850 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: 8 tokens</li><li>mean: 27.35 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:----------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <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>1</code> |
| <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code> | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code> | <code>1</code> |
| <code>The 12F was officially homologated on August 21 , 1929 and exhibited at the Paris Salon in 1930 .</code> | <code>The 12F was officially homologated on 21 August 1929 and displayed at the 1930 Paris Salon .</code> | <code>1</code> |
* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) 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`: 100
- `per_device_eval_batch_size`: 100
- `learning_rate`: 0.0001
- `adam_beta2`: 0.98
- `adam_epsilon`: 1e-06
- `max_steps`: 200000
- `warmup_steps`: 1000
- `load_best_model_at_end`: True
- `optim`: adamw_torch
- `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`: 100
- `per_device_eval_batch_size`: 100
- `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`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.98
- `adam_epsilon`: 1e-06
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3.0
- `max_steps`: 200000
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 1000
- `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`: 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-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_ap |
|:----------:|:--------:|:-------------:|:---------------:|:--------------:|
| -1 | -1 | - | - | 0.6476 |
| 0.2067 | 1000 | 0.0165 | 0.1033 | 0.6705 |
| 0.4133 | 2000 | 0.0067 | 0.0977 | 0.6597 |
| 0.6200 | 3000 | 0.0061 | 0.0955 | 0.6670 |
| **0.8266** | **4000** | **0.0063** | **0.0945** | **0.6678** |
| 1.0333 | 5000 | 0.0059 | 0.0950 | 0.6786 |
| 1.2399 | 6000 | 0.0054 | 0.0880 | 0.6779 |
| 1.4466 | 7000 | 0.0054 | 0.0876 | 0.6791 |
| 1.6532 | 8000 | 0.0054 | 0.0833 | 0.6652 |
| 1.8599 | 9000 | 0.0051 | 0.0821 | 0.6760 |
| 2.0665 | 10000 | 0.0048 | 0.0818 | 0.6767 |
| 2.2732 | 11000 | 0.0044 | 0.0796 | 0.6732 |
| 2.4799 | 12000 | 0.0048 | 0.0790 | 0.6717 |
| 2.6865 | 13000 | 0.0043 | 0.0804 | 0.6748 |
| 2.8932 | 14000 | 0.0048 | 0.0790 | 0.6745 |
| 3.0998 | 15000 | 0.0033 | 0.0775 | 0.6693 |
| 3.3065 | 16000 | 0.0044 | 0.0769 | 0.6767 |
| 3.5131 | 17000 | 0.005 | 0.0770 | 0.6768 |
| 3.7198 | 18000 | 0.0044 | 0.0760 | 0.6761 |
| 3.9264 | 19000 | 0.0039 | 0.0741 | 0.6799 |
| 4.1331 | 20000 | 0.0044 | 0.0750 | 0.6888 |
| 4.3397 | 21000 | 0.0041 | 0.0751 | 0.7019 |
| 4.5464 | 22000 | 0.0044 | 0.0707 | 0.7009 |
| 4.7530 | 23000 | 0.0039 | 0.0726 | 0.7041 |
| 4.9597 | 24000 | 0.0042 | 0.0712 | 0.6971 |
| 5.1664 | 25000 | 0.0038 | 0.0718 | 0.6978 |
| 5.3730 | 26000 | 0.004 | 0.0703 | 0.7035 |
| 5.5797 | 27000 | 0.004 | 0.0706 | 0.6976 |
| 5.7863 | 28000 | 0.0042 | 0.0699 | 0.6964 |
| 5.9930 | 29000 | 0.0044 | 0.0699 | 0.6911 |
| 6.1996 | 30000 | 0.0035 | 0.0702 | 0.6791 |
| 6.4063 | 31000 | 0.0035 | 0.0690 | 0.6955 |
| 6.6129 | 32000 | 0.0037 | 0.0693 | 0.6917 |
| 6.8196 | 33000 | 0.0035 | 0.0691 | 0.6972 |
| 7.0262 | 34000 | 0.004 | 0.0695 | 0.7083 |
| 7.2329 | 35000 | 0.0037 | 0.0690 | 0.6994 |
| 7.4396 | 36000 | 0.0036 | 0.0670 | 0.7060 |
| 7.6462 | 37000 | 0.0042 | 0.0682 | 0.6963 |
| 7.8529 | 38000 | 0.0037 | 0.0678 | 0.7049 |
| 8.0595 | 39000 | 0.0039 | 0.0682 | 0.7014 |
| 8.2662 | 40000 | 0.0039 | 0.0684 | 0.6969 |
| 8.4728 | 41000 | 0.0041 | 0.0677 | 0.7007 |
| 8.6795 | 42000 | 0.0038 | 0.0671 | 0.7126 |
| 8.8861 | 43000 | 0.0035 | 0.0684 | 0.7150 |
| 9.0928 | 44000 | 0.0035 | 0.0671 | 0.7043 |
| 9.2994 | 45000 | 0.0038 | 0.0681 | 0.7021 |
| 9.5061 | 46000 | 0.0038 | 0.0687 | 0.7129 |
| 9.7128 | 47000 | 0.0038 | 0.0684 | 0.7215 |
| 9.9194 | 48000 | 0.0039 | 0.0668 | 0.7179 |
| 10.1261 | 49000 | 0.0031 | 0.0661 | 0.7129 |
| 10.3327 | 50000 | 0.0033 | 0.0664 | 0.7119 |
| 10.5394 | 51000 | 0.0034 | 0.0668 | 0.7162 |
| 10.7460 | 52000 | 0.0038 | 0.0666 | 0.7181 |
| 10.9527 | 53000 | 0.0034 | 0.0674 | 0.7046 |
| 11.1593 | 54000 | 0.0034 | 0.0657 | 0.7100 |
| 11.3660 | 55000 | 0.0035 | 0.0656 | 0.7163 |
| 11.5726 | 56000 | 0.0034 | 0.0656 | 0.7003 |
| 11.7793 | 57000 | 0.0036 | 0.0643 | 0.7009 |
| 11.9859 | 58000 | 0.0038 | 0.0649 | 0.7166 |
| 12.1926 | 59000 | 0.0039 | 0.0659 | 0.7168 |
| 12.3993 | 60000 | 0.0039 | 0.0647 | 0.7080 |
| 12.6059 | 61000 | 0.0032 | 0.0649 | 0.7114 |
| 12.8126 | 62000 | 0.0034 | 0.0646 | 0.7165 |
| 13.0192 | 63000 | 0.0034 | 0.0654 | 0.7197 |
| 13.2259 | 64000 | 0.0035 | 0.0657 | 0.7179 |
| 13.4325 | 65000 | 0.0031 | 0.0652 | 0.7107 |
| 13.6392 | 66000 | 0.0032 | 0.0649 | 0.7089 |
| 13.8458 | 67000 | 0.0034 | 0.0655 | 0.7089 |
| 14.0525 | 68000 | 0.0031 | 0.0668 | 0.7163 |
| 14.2591 | 69000 | 0.0035 | 0.0644 | 0.7213 |
| 14.4658 | 70000 | 0.0035 | 0.0634 | 0.7057 |
| 14.6725 | 71000 | 0.0035 | 0.0635 | 0.7049 |
| 14.8791 | 72000 | 0.0033 | 0.0627 | 0.7094 |
| 15.0858 | 73000 | 0.0037 | 0.0620 | 0.7140 |
| 15.2924 | 74000 | 0.0035 | 0.0628 | 0.7237 |
| 15.4991 | 75000 | 0.003 | 0.0625 | 0.7127 |
| 15.7057 | 76000 | 0.0036 | 0.0635 | 0.7127 |
| 15.9124 | 77000 | 0.0037 | 0.0621 | 0.7104 |
| 16.1190 | 78000 | 0.0033 | 0.0624 | 0.7132 |
| 16.3257 | 79000 | 0.0035 | 0.0632 | 0.7132 |
| 16.5323 | 80000 | 0.003 | 0.0626 | 0.7193 |
| 16.7390 | 81000 | 0.0033 | 0.0628 | 0.7179 |
| 16.9456 | 82000 | 0.0036 | 0.0630 | 0.7210 |
| 17.1523 | 83000 | 0.0033 | 0.0628 | 0.7222 |
| 17.3590 | 84000 | 0.0034 | 0.0629 | 0.7226 |
| 17.5656 | 85000 | 0.0029 | 0.0621 | 0.7207 |
| 17.7723 | 86000 | 0.0032 | 0.0618 | 0.7182 |
| 17.9789 | 87000 | 0.0034 | 0.0620 | 0.7177 |
| 18.1856 | 88000 | 0.0034 | 0.0625 | 0.7148 |
| 18.3922 | 89000 | 0.0032 | 0.0624 | 0.7131 |
| 18.5989 | 90000 | 0.0032 | 0.0622 | 0.7126 |
| 18.8055 | 91000 | 0.0031 | 0.0617 | 0.7185 |
| 19.0122 | 92000 | 0.0032 | 0.0620 | 0.7231 |
| 19.2188 | 93000 | 0.0028 | 0.0623 | 0.7202 |
| 19.4255 | 94000 | 0.003 | 0.0625 | 0.7194 |
| 19.6322 | 95000 | 0.003 | 0.0619 | 0.7139 |
| 19.8388 | 96000 | 0.0031 | 0.0621 | 0.7151 |
| 20.0455 | 97000 | 0.0031 | 0.0617 | 0.7188 |
| 20.2521 | 98000 | 0.0031 | 0.0619 | 0.7161 |
| 20.4588 | 99000 | 0.0027 | 0.0612 | 0.7164 |
| 20.6654 | 100000 | 0.0033 | 0.0616 | 0.7173 |
| 20.8721 | 101000 | 0.0033 | 0.0614 | 0.7182 |
| 21.0787 | 102000 | 0.003 | 0.0611 | 0.7194 |
| 21.2854 | 103000 | 0.0031 | 0.0614 | 0.7191 |
| 21.4920 | 104000 | 0.0031 | 0.0615 | 0.7187 |
| 21.6987 | 105000 | 0.0035 | 0.0609 | 0.7143 |
| 21.9054 | 106000 | 0.0033 | 0.0614 | 0.7180 |
| 22.1120 | 107000 | 0.0029 | 0.0608 | 0.7215 |
| 22.3187 | 108000 | 0.0032 | 0.0609 | 0.7250 |
| 22.5253 | 109000 | 0.0029 | 0.0611 | 0.7248 |
| 22.7320 | 110000 | 0.003 | 0.0612 | 0.7224 |
| 22.9386 | 111000 | 0.0029 | 0.0612 | 0.7180 |
| 23.1453 | 112000 | 0.0032 | 0.0610 | 0.7169 |
| 23.3519 | 113000 | 0.0032 | 0.0609 | 0.7174 |
| 23.5586 | 114000 | 0.0028 | 0.0613 | 0.7204 |
| 23.7652 | 115000 | 0.0033 | 0.0613 | 0.7222 |
| 23.9719 | 116000 | 0.0033 | 0.0613 | 0.7240 |
| 24.1785 | 117000 | 0.003 | 0.0610 | 0.7244 |
| 24.3852 | 118000 | 0.0027 | 0.0613 | 0.7239 |
| 24.5919 | 119000 | 0.0028 | 0.0615 | 0.7248 |
| 24.7985 | 120000 | 0.003 | 0.0608 | 0.7259 |
| 25.0052 | 121000 | 0.0033 | 0.0605 | 0.7270 |
| 25.2118 | 122000 | 0.0035 | 0.0604 | 0.7240 |
| 25.4185 | 123000 | 0.003 | 0.0607 | 0.7245 |
| 25.6251 | 124000 | 0.003 | 0.0608 | 0.7238 |
| 25.8318 | 125000 | 0.0032 | 0.0605 | 0.7208 |
| 26.0384 | 126000 | 0.0029 | 0.0605 | 0.7208 |
| 26.2451 | 127000 | 0.0034 | 0.0603 | 0.7212 |
| 26.4517 | 128000 | 0.003 | 0.0605 | 0.7222 |
| 26.6584 | 129000 | 0.003 | 0.0604 | 0.7236 |
| 26.8651 | 130000 | 0.003 | 0.0608 | 0.7271 |
| 27.0717 | 131000 | 0.0028 | 0.0608 | 0.7242 |
| 27.2784 | 132000 | 0.0028 | 0.0612 | 0.7239 |
| 27.4850 | 133000 | 0.0025 | 0.0609 | 0.7270 |
| 27.6917 | 134000 | 0.0026 | 0.0607 | 0.7277 |
| 27.8983 | 135000 | 0.003 | 0.0608 | 0.7263 |
| 28.1050 | 136000 | 0.003 | 0.0609 | 0.7250 |
| 28.3116 | 137000 | 0.0029 | 0.0607 | 0.7262 |
| 28.5183 | 138000 | 0.0029 | 0.0609 | 0.7269 |
| 28.7249 | 139000 | 0.0029 | 0.0607 | 0.7250 |
| 28.9316 | 140000 | 0.0025 | 0.0608 | 0.7254 |
| 29.1383 | 141000 | 0.0031 | 0.0609 | 0.7262 |
| 29.3449 | 142000 | 0.0027 | 0.0606 | 0.7247 |
| 29.5516 | 143000 | 0.003 | 0.0607 | 0.7244 |
| 29.7582 | 144000 | 0.0028 | 0.0606 | 0.7240 |
| 29.9649 | 145000 | 0.0028 | 0.0605 | 0.7228 |
| 30.1715 | 146000 | 0.0032 | 0.0604 | 0.7251 |
| 30.3782 | 147000 | 0.0033 | 0.0603 | 0.7240 |
| 30.5848 | 148000 | 0.0029 | 0.0604 | 0.7242 |
| 30.7915 | 149000 | 0.0032 | 0.0603 | 0.7241 |
| 30.9981 | 150000 | 0.0028 | 0.0602 | 0.7246 |
| 31.2048 | 151000 | 0.0029 | 0.0602 | 0.7261 |
| 31.4114 | 152000 | 0.003 | 0.0602 | 0.7258 |
| 31.6181 | 153000 | 0.0031 | 0.0603 | 0.7253 |
| 31.8248 | 154000 | 0.003 | 0.0602 | 0.7250 |
| 32.0314 | 155000 | 0.0033 | 0.0602 | 0.7248 |
| 32.2381 | 156000 | 0.0031 | 0.0601 | 0.7248 |
| 32.4447 | 157000 | 0.0027 | 0.0602 | 0.7240 |
| 32.6514 | 158000 | 0.0026 | 0.0602 | 0.7243 |
| 32.8580 | 159000 | 0.0028 | 0.0602 | 0.7249 |
| 33.0647 | 160000 | 0.0033 | 0.0602 | 0.7251 |
| 33.2713 | 161000 | 0.0031 | 0.0602 | 0.7252 |
| 33.4780 | 162000 | 0.0027 | 0.0600 | 0.7247 |
| 33.6846 | 163000 | 0.0031 | 0.0601 | 0.7247 |
| 33.8913 | 164000 | 0.0032 | 0.0601 | 0.7251 |
| 34.0980 | 165000 | 0.0026 | 0.0602 | 0.7252 |
| 34.3046 | 166000 | 0.0034 | 0.0602 | 0.7252 |
| 34.5113 | 167000 | 0.0028 | 0.0602 | 0.7250 |
| 34.7179 | 168000 | 0.0029 | 0.0601 | 0.7249 |
| 34.9246 | 169000 | 0.0028 | 0.0602 | 0.7253 |
| 35.1312 | 170000 | 0.0026 | 0.0601 | 0.7249 |
| 35.3379 | 171000 | 0.0027 | 0.0601 | 0.7247 |
| 35.5445 | 172000 | 0.0031 | 0.0601 | 0.7245 |
| 35.7512 | 173000 | 0.003 | 0.0600 | 0.7245 |
| 35.9578 | 174000 | 0.003 | 0.0601 | 0.7250 |
| 36.1645 | 175000 | 0.0027 | 0.0600 | 0.7246 |
| 36.3712 | 176000 | 0.0028 | 0.0601 | 0.7248 |
| 36.5778 | 177000 | 0.0027 | 0.0601 | 0.7250 |
| 36.7845 | 178000 | 0.0028 | 0.0601 | 0.7252 |
| 36.9911 | 179000 | 0.0029 | 0.0601 | 0.7252 |
| 37.1978 | 180000 | 0.0029 | 0.0602 | 0.7251 |
| 37.4044 | 181000 | 0.0025 | 0.0601 | 0.7250 |
| 37.6111 | 182000 | 0.003 | 0.0601 | 0.7250 |
| 37.8177 | 183000 | 0.0028 | 0.0601 | 0.7251 |
| 38.0244 | 184000 | 0.0028 | 0.0601 | 0.7252 |
| 38.2310 | 185000 | 0.0034 | 0.0600 | 0.7251 |
| 38.4377 | 186000 | 0.0028 | 0.0601 | 0.7251 |
| 38.6443 | 187000 | 0.0035 | 0.0601 | 0.7250 |
| 38.8510 | 188000 | 0.003 | 0.0600 | 0.7250 |
| 39.0577 | 189000 | 0.0028 | 0.0601 | 0.7252 |
| 39.2643 | 190000 | 0.0027 | 0.0600 | 0.7250 |
| 39.4710 | 191000 | 0.0026 | 0.0601 | 0.7250 |
| 39.6776 | 192000 | 0.0028 | 0.0600 | 0.7251 |
| 39.8843 | 193000 | 0.0027 | 0.0600 | 0.7251 |
| 40.0909 | 194000 | 0.0031 | 0.0601 | 0.7252 |
| 40.2976 | 195000 | 0.0031 | 0.0600 | 0.7252 |
| 40.5042 | 196000 | 0.0029 | 0.0601 | 0.7251 |
| 40.7109 | 197000 | 0.0032 | 0.0600 | 0.7251 |
| 40.9175 | 198000 | 0.0028 | 0.0600 | 0.7251 |
| 41.1242 | 199000 | 0.0029 | 0.0600 | 0.7252 |
| 41.3309 | 200000 | 0.003 | 0.0600 | 0.7251 |
* 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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