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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1027471
- loss:CoSENTLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
datasets: []
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: watter dag is gedenkteken dag die jaar
sentences:
- request news
- turn volume down
- request news
- source_sentence: wat is die week se weertoestand
sentences:
- play radio
- make coffee
- traffic query
- source_sentence: skakel aan die roomba
sentences:
- tell joke
- start cleaning
- request datetime
- source_sentence: kan jy my 'n goeie grap vertel
sentences:
- set alarm
- play music
- tell joke
- source_sentence: vertel my die huidige tyd in ottawa
sentences:
- set alarm
- request definition
- query cooking
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: MiniLM dev
type: MiniLM-dev
metrics:
- type: pearson_cosine
value: 0.8464008477003933
name: Pearson Cosine
- type: spearman_cosine
value: 0.8128883563290172
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8204825552661638
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8069612779979122
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8207664286968728
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.806851537985582
name: Spearman Euclidean
- type: pearson_dot
value: 0.7927608791449223
name: Pearson Dot
- type: spearman_dot
value: 0.8078229606916496
name: Spearman Dot
- type: pearson_max
value: 0.8464008477003933
name: Pearson Max
- type: spearman_max
value: 0.8128883563290172
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: MiniLM test
type: MiniLM-test
metrics:
- type: pearson_cosine
value: 0.9079517679775697
name: Pearson Cosine
- type: spearman_cosine
value: 0.842595786650747
name: Spearman Cosine
- type: pearson_manhattan
value: 0.885352838846903
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8389283098138718
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8858228063346806
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8390847286161828
name: Spearman Euclidean
- type: pearson_dot
value: 0.8618645999355777
name: Pearson Dot
- type: spearman_dot
value: 0.8389938584674199
name: Spearman Dot
- type: pearson_max
value: 0.9079517679775697
name: Pearson Max
- type: spearman_max
value: 0.842595786650747
name: Spearman Max
---
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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("philipp-zettl/MiniLM-amazon_massive_intent-similarity")
# Run inference
sentences = [
'vertel my die huidige tyd in ottawa',
'query cooking',
'request definition',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `MiniLM-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8464 |
| **spearman_cosine** | **0.8129** |
| pearson_manhattan | 0.8205 |
| spearman_manhattan | 0.807 |
| pearson_euclidean | 0.8208 |
| spearman_euclidean | 0.8069 |
| pearson_dot | 0.7928 |
| spearman_dot | 0.8078 |
| pearson_max | 0.8464 |
| spearman_max | 0.8129 |
#### Semantic Similarity
* Dataset: `MiniLM-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.908 |
| **spearman_cosine** | **0.8426** |
| pearson_manhattan | 0.8854 |
| spearman_manhattan | 0.8389 |
| pearson_euclidean | 0.8858 |
| spearman_euclidean | 0.8391 |
| pearson_dot | 0.8619 |
| spearman_dot | 0.839 |
| pearson_max | 0.908 |
| spearman_max | 0.8426 |
<!--
## 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 Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: 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`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | loss | MiniLM-dev_spearman_cosine | MiniLM-test_spearman_cosine |
|:------:|:-----:|:-------------:|:------:|:--------------------------:|:---------------------------:|
| 0.0031 | 100 | 7.4879 | - | - | - |
| 0.0062 | 200 | 6.4531 | - | - | - |
| 0.0093 | 300 | 6.4185 | - | - | - |
| 0.0125 | 400 | 4.5043 | - | - | - |
| 0.0156 | 500 | 5.1274 | - | - | - |
| 0.0187 | 600 | 6.0006 | - | - | - |
| 0.0218 | 700 | 4.8066 | - | - | - |
| 0.0249 | 800 | 3.9536 | - | - | - |
| 0.0280 | 900 | 4.7259 | - | - | - |
| 0.0311 | 1000 | 3.7583 | 2.6440 | 0.6640 | - |
| 0.0343 | 1100 | 3.9905 | - | - | - |
| 0.0374 | 1200 | 4.8914 | - | - | - |
| 0.0405 | 1300 | 3.895 | - | - | - |
| 0.0436 | 1400 | 3.1582 | - | - | - |
| 0.0467 | 1500 | 3.7172 | - | - | - |
| 0.0498 | 1600 | 3.6785 | - | - | - |
| 0.0529 | 1700 | 3.9632 | - | - | - |
| 0.0561 | 1800 | 3.9643 | - | - | - |
| 0.0592 | 1900 | 2.829 | - | - | - |
| 0.0623 | 2000 | 2.5923 | 2.3344 | 0.7459 | - |
| 0.0654 | 2100 | 3.1617 | - | - | - |
| 0.0685 | 2200 | 2.6366 | - | - | - |
| 0.0716 | 2300 | 4.3751 | - | - | - |
| 0.0747 | 2400 | 3.4732 | - | - | - |
| 0.0779 | 2500 | 2.5695 | - | - | - |
| 0.0810 | 2600 | 2.7479 | - | - | - |
| 0.0841 | 2700 | 2.5274 | - | - | - |
| 0.0872 | 2800 | 2.4204 | - | - | - |
| 0.0903 | 2900 | 4.1305 | - | - | - |
| 0.0934 | 3000 | 4.091 | 2.0951 | 0.7426 | - |
| 0.0965 | 3100 | 3.7972 | - | - | - |
| 0.0997 | 3200 | 2.6029 | - | - | - |
| 0.1028 | 3300 | 3.2422 | - | - | - |
| 0.1059 | 3400 | 3.3747 | - | - | - |
| 0.1090 | 3500 | 3.3358 | - | - | - |
| 0.1121 | 3600 | 2.8658 | - | - | - |
| 0.1152 | 3700 | 2.6436 | - | - | - |
| 0.1183 | 3800 | 2.2006 | - | - | - |
| 0.1215 | 3900 | 2.0549 | - | - | - |
| 0.1246 | 4000 | 2.4642 | 3.4108 | 0.7236 | - |
| 0.1277 | 4100 | 2.9219 | - | - | - |
| 0.1308 | 4200 | 2.6581 | - | - | - |
| 0.1339 | 4300 | 2.2697 | - | - | - |
| 0.1370 | 4400 | 2.7215 | - | - | - |
| 0.1401 | 4500 | 2.6023 | - | - | - |
| 0.1433 | 4600 | 1.8772 | - | - | - |
| 0.1464 | 4700 | 2.6885 | - | - | - |
| 0.1495 | 4800 | 2.6005 | - | - | - |
| 0.1526 | 4900 | 1.4849 | - | - | - |
| 0.1557 | 5000 | 2.4896 | 3.4860 | 0.7117 | - |
| 0.1588 | 5100 | 2.6038 | - | - | - |
| 0.1619 | 5200 | 2.0584 | - | - | - |
| 0.1651 | 5300 | 1.9156 | - | - | - |
| 0.1682 | 5400 | 1.467 | - | - | - |
| 0.1713 | 5500 | 0.5799 | - | - | - |
| 0.1744 | 5600 | 1.617 | - | - | - |
| 0.1775 | 5700 | 1.3764 | - | - | - |
| 0.1806 | 5800 | 3.067 | - | - | - |
| 0.1837 | 5900 | 2.2463 | - | - | - |
| 0.1869 | 6000 | 1.5466 | 2.5326 | 0.7721 | - |
| 0.1900 | 6100 | 1.4097 | - | - | - |
| 0.1931 | 6200 | 1.7852 | - | - | - |
| 0.1962 | 6300 | 1.2715 | - | - | - |
| 0.1993 | 6400 | 2.5585 | - | - | - |
| 0.2024 | 6500 | 2.4665 | - | - | - |
| 0.2055 | 6600 | 1.7246 | - | - | - |
| 0.2087 | 6700 | 1.145 | - | - | - |
| 0.2118 | 6800 | 1.614 | - | - | - |
| 0.2149 | 6900 | 1.7206 | - | - | - |
| 0.2180 | 7000 | 2.6349 | 2.6824 | 0.7652 | - |
| 0.2211 | 7100 | 2.1896 | - | - | - |
| 0.2242 | 7200 | 1.9106 | - | - | - |
| 0.2274 | 7300 | 1.3783 | - | - | - |
| 0.2305 | 7400 | 0.7119 | - | - | - |
| 0.2336 | 7500 | 1.5037 | - | - | - |
| 0.2367 | 7600 | 1.8365 | - | - | - |
| 0.2398 | 7700 | 1.3817 | - | - | - |
| 0.2429 | 7800 | 1.7101 | - | - | - |
| 0.2460 | 7900 | 1.6716 | - | - | - |
| 0.2492 | 8000 | 1.3013 | 3.5864 | 0.7401 | - |
| 0.2523 | 8100 | 1.5131 | - | - | - |
| 0.2554 | 8200 | 2.3699 | - | - | - |
| 0.2585 | 8300 | 1.6179 | - | - | - |
| 0.2616 | 8400 | 1.3 | - | - | - |
| 0.2647 | 8500 | 1.5151 | - | - | - |
| 0.2678 | 8600 | 2.8703 | - | - | - |
| 0.2710 | 8700 | 2.5076 | - | - | - |
| 0.2741 | 8800 | 1.9876 | - | - | - |
| 0.2772 | 8900 | 1.5823 | - | - | - |
| 0.2803 | 9000 | 1.0845 | 2.4197 | 0.7833 | - |
| 0.2834 | 9100 | 1.2871 | - | - | - |
| 0.2865 | 9200 | 1.3901 | - | - | - |
| 0.2896 | 9300 | 1.1607 | - | - | - |
| 0.2928 | 9400 | 2.1171 | - | - | - |
| 0.2959 | 9500 | 1.4335 | - | - | - |
| 0.2990 | 9600 | 0.801 | - | - | - |
| 0.3021 | 9700 | 1.4567 | - | - | - |
| 0.3052 | 9800 | 1.7046 | - | - | - |
| 0.3083 | 9900 | 1.4378 | - | - | - |
| 0.3114 | 10000 | 2.3191 | 2.3063 | 0.7903 | - |
| 0.3146 | 10100 | 1.6518 | - | - | - |
| 0.3177 | 10200 | 0.9857 | - | - | - |
| 0.3208 | 10300 | 2.2052 | - | - | - |
| 0.3239 | 10400 | 2.0443 | - | - | - |
| 0.3270 | 10500 | 2.08 | - | - | - |
| 0.3301 | 10600 | 2.0009 | - | - | - |
| 0.3332 | 10700 | 1.3274 | - | - | - |
| 0.3364 | 10800 | 1.0298 | - | - | - |
| 0.3395 | 10900 | 1.7127 | - | - | - |
| 0.3426 | 11000 | 1.3371 | 4.0607 | 0.7211 | - |
| 0.3457 | 11100 | 2.7555 | - | - | - |
| 0.3488 | 11200 | 4.1792 | - | - | - |
| 0.3519 | 11300 | 2.0931 | - | - | - |
| 0.3550 | 11400 | 2.4591 | - | - | - |
| 0.3582 | 11500 | 3.4962 | - | - | - |
| 0.3613 | 11600 | 1.9228 | - | - | - |
| 0.3644 | 11700 | 2.7295 | - | - | - |
| 0.3675 | 11800 | 1.5425 | - | - | - |
| 0.3706 | 11900 | 1.1586 | - | - | - |
| 0.3737 | 12000 | 1.1336 | 2.2959 | 0.7890 | - |
| 0.3768 | 12100 | 1.572 | - | - | - |
| 0.3800 | 12200 | 1.2827 | - | - | - |
| 0.3831 | 12300 | 1.6352 | - | - | - |
| 0.3862 | 12400 | 1.4708 | - | - | - |
| 0.3893 | 12500 | 1.4719 | - | - | - |
| 0.3924 | 12600 | 1.4136 | - | - | - |
| 0.3955 | 12700 | 1.3969 | - | - | - |
| 0.3986 | 12800 | 1.7228 | - | - | - |
| 0.4018 | 12900 | 4.2842 | - | - | - |
| 0.4049 | 13000 | 3.5861 | 2.1113 | 0.7956 | - |
| 0.4080 | 13100 | 2.9718 | - | - | - |
| 0.4111 | 13200 | 3.1554 | - | - | - |
| 0.4142 | 13300 | 3.1357 | - | - | - |
| 0.4173 | 13400 | 2.8488 | - | - | - |
| 0.4204 | 13500 | 3.7433 | - | - | - |
| 0.4236 | 13600 | 2.4195 | - | - | - |
| 0.4267 | 13700 | 2.1384 | - | - | - |
| 0.4298 | 13800 | 2.7965 | - | - | - |
| 0.4329 | 13900 | 1.7869 | - | - | - |
| 0.4360 | 14000 | 3.0356 | 2.7234 | 0.7697 | - |
| 0.4391 | 14100 | 3.4984 | - | - | - |
| 0.4422 | 14200 | 2.4959 | - | - | - |
| 0.4454 | 14300 | 2.4615 | - | - | - |
| 0.4485 | 14400 | 2.6309 | - | - | - |
| 0.4516 | 14500 | 1.9831 | - | - | - |
| 0.4547 | 14600 | 3.25 | - | - | - |
| 0.4578 | 14700 | 3.3112 | - | - | - |
| 0.4609 | 14800 | 1.9912 | - | - | - |
| 0.4640 | 14900 | 1.9252 | - | - | - |
| 0.4672 | 15000 | 2.4545 | 2.0730 | 0.7972 | - |
| 0.4703 | 15100 | 1.6943 | - | - | - |
| 0.4734 | 15200 | 2.2851 | - | - | - |
| 0.4765 | 15300 | 2.4327 | - | - | - |
| 0.4796 | 15400 | 1.3503 | - | - | - |
| 0.4827 | 15500 | 1.1419 | - | - | - |
| 0.4858 | 15600 | 1.7906 | - | - | - |
| 0.4890 | 15700 | 1.6504 | - | - | - |
| 0.4921 | 15800 | 1.6908 | - | - | - |
| 0.4952 | 15900 | 3.0954 | - | - | - |
| 0.4983 | 16000 | 1.7151 | 2.0042 | 0.8044 | - |
| 0.5014 | 16100 | 1.5165 | - | - | - |
| 0.5045 | 16200 | 2.5573 | - | - | - |
| 0.5076 | 16300 | 1.3401 | - | - | - |
| 0.5108 | 16400 | 2.5464 | - | - | - |
| 0.5139 | 16500 | 2.4564 | - | - | - |
| 0.5170 | 16600 | 2.1667 | - | - | - |
| 0.5201 | 16700 | 1.2402 | - | - | - |
| 0.5232 | 16800 | 1.932 | - | - | - |
| 0.5263 | 16900 | 1.1811 | - | - | - |
| 0.5294 | 17000 | 2.2014 | 2.0475 | 0.8062 | - |
| 0.5326 | 17100 | 2.6535 | - | - | - |
| 0.5357 | 17200 | 1.8715 | - | - | - |
| 0.5388 | 17300 | 1.9385 | - | - | - |
| 0.5419 | 17400 | 2.0398 | - | - | - |
| 0.5450 | 17500 | 1.3436 | - | - | - |
| 0.5481 | 17600 | 2.0687 | - | - | - |
| 0.5512 | 17700 | 1.6224 | - | - | - |
| 0.5544 | 17800 | 1.0539 | - | - | - |
| 0.5575 | 17900 | 1.1162 | - | - | - |
| 0.5606 | 18000 | 1.6334 | 2.4120 | 0.7964 | - |
| 0.5637 | 18100 | 1.247 | - | - | - |
| 0.5668 | 18200 | 2.4652 | - | - | - |
| 0.5699 | 18300 | 1.8593 | - | - | - |
| 0.5730 | 18400 | 1.1875 | - | - | - |
| 0.5762 | 18500 | 2.1173 | - | - | - |
| 0.5793 | 18600 | 1.7473 | - | - | - |
| 0.5824 | 18700 | 2.1865 | - | - | - |
| 0.5855 | 18800 | 1.683 | - | - | - |
| 0.5886 | 18900 | 1.6522 | - | - | - |
| 0.5917 | 19000 | 1.0526 | 2.0743 | 0.8033 | - |
| 0.5948 | 19100 | 1.5001 | - | - | - |
| 0.5980 | 19200 | 1.2606 | - | - | - |
| 0.6011 | 19300 | 1.0597 | - | - | - |
| 0.6042 | 19400 | 1.8603 | - | - | - |
| 0.6073 | 19500 | 1.4883 | - | - | - |
| 0.6104 | 19600 | 0.6594 | - | - | - |
| 0.6135 | 19700 | 0.9557 | - | - | - |
| 0.6166 | 19800 | 0.8651 | - | - | - |
| 0.6198 | 19900 | 1.0326 | - | - | - |
| 0.6229 | 20000 | 1.2785 | 2.0868 | 0.8075 | - |
| 0.6260 | 20100 | 1.2881 | - | - | - |
| 0.6291 | 20200 | 0.5919 | - | - | - |
| 0.6322 | 20300 | 1.69 | - | - | - |
| 0.6353 | 20400 | 1.0285 | - | - | - |
| 0.6385 | 20500 | 0.8843 | - | - | - |
| 0.6416 | 20600 | 1.3756 | - | - | - |
| 0.6447 | 20700 | 0.9646 | - | - | - |
| 0.6478 | 20800 | 0.8052 | - | - | - |
| 0.6509 | 20900 | 0.8996 | - | - | - |
| 0.6540 | 21000 | 1.2207 | 2.2881 | 0.8029 | - |
| 0.6571 | 21100 | 1.3225 | - | - | - |
| 0.6603 | 21200 | 1.8101 | - | - | - |
| 0.6634 | 21300 | 0.8756 | - | - | - |
| 0.6665 | 21400 | 0.9877 | - | - | - |
| 0.6696 | 21500 | 1.7329 | - | - | - |
| 0.6727 | 21600 | 1.6885 | - | - | - |
| 0.6758 | 21700 | 1.2132 | - | - | - |
| 0.6789 | 21800 | 1.4888 | - | - | - |
| 0.6821 | 21900 | 1.403 | - | - | - |
| 0.6852 | 22000 | 0.5995 | 2.1952 | 0.8036 | - |
| 0.6883 | 22100 | 0.9658 | - | - | - |
| 0.6914 | 22200 | 1.1485 | - | - | - |
| 0.6945 | 22300 | 1.089 | - | - | - |
| 0.6976 | 22400 | 1.2719 | - | - | - |
| 0.7007 | 22500 | 0.9611 | - | - | - |
| 0.7039 | 22600 | 0.9398 | - | - | - |
| 0.7070 | 22700 | 0.7931 | - | - | - |
| 0.7101 | 22800 | 1.1224 | - | - | - |
| 0.7132 | 22900 | 2.032 | - | - | - |
| 0.7163 | 23000 | 1.3664 | 2.1043 | 0.8075 | - |
| 0.7194 | 23100 | 0.7777 | - | - | - |
| 0.7225 | 23200 | 0.9427 | - | - | - |
| 0.7257 | 23300 | 0.8846 | - | - | - |
| 0.7288 | 23400 | 1.0039 | - | - | - |
| 0.7319 | 23500 | 0.9344 | - | - | - |
| 0.7350 | 23600 | 1.3712 | - | - | - |
| 0.7381 | 23700 | 0.8039 | - | - | - |
| 0.7412 | 23800 | 1.0735 | - | - | - |
| 0.7443 | 23900 | 0.9851 | - | - | - |
| 0.7475 | 24000 | 1.8673 | 2.1547 | 0.8066 | - |
| 0.7506 | 24100 | 5.5805 | - | - | - |
| 0.7537 | 24200 | 4.1286 | - | - | - |
| 0.7568 | 24300 | 2.2206 | - | - | - |
| 0.7599 | 24400 | 3.6468 | - | - | - |
| 0.7630 | 24500 | 2.9307 | - | - | - |
| 0.7661 | 24600 | 3.8745 | - | - | - |
| 0.7693 | 24700 | 2.2125 | - | - | - |
| 0.7724 | 24800 | 2.3844 | - | - | - |
| 0.7755 | 24900 | 1.5081 | - | - | - |
| 0.7786 | 25000 | 1.5982 | 1.8491 | 0.8145 | - |
| 0.7817 | 25100 | 2.1563 | - | - | - |
| 0.7848 | 25200 | 1.8558 | - | - | - |
| 0.7879 | 25300 | 2.2087 | - | - | - |
| 0.7911 | 25400 | 2.3953 | - | - | - |
| 0.7942 | 25500 | 1.4072 | - | - | - |
| 0.7973 | 25600 | 1.4637 | - | - | - |
| 0.8004 | 25700 | 2.2037 | - | - | - |
| 0.8035 | 25800 | 1.6241 | - | - | - |
| 0.8066 | 25900 | 1.4882 | - | - | - |
| 0.8097 | 26000 | 0.9108 | 1.9292 | 0.8115 | - |
| 0.8129 | 26100 | 0.9198 | - | - | - |
| 0.8160 | 26200 | 1.2981 | - | - | - |
| 0.8191 | 26300 | 1.0513 | - | - | - |
| 0.8222 | 26400 | 1.389 | - | - | - |
| 0.8253 | 26500 | 5.8539 | - | - | - |
| 0.8284 | 26600 | 3.547 | - | - | - |
| 0.8315 | 26700 | 2.3285 | - | - | - |
| 0.8347 | 26800 | 2.8112 | - | - | - |
| 0.8378 | 26900 | 3.3717 | - | - | - |
| 0.8409 | 27000 | 2.5921 | 1.9430 | 0.8108 | - |
| 0.8440 | 27100 | 1.5048 | - | - | - |
| 0.8471 | 27200 | 1.5 | - | - | - |
| 0.8502 | 27300 | 0.778 | - | - | - |
| 0.8533 | 27400 | 0.9557 | - | - | - |
| 0.8565 | 27500 | 1.347 | - | - | - |
| 0.8596 | 27600 | 1.5882 | - | - | - |
| 0.8627 | 27700 | 1.7333 | - | - | - |
| 0.8658 | 27800 | 1.5683 | - | - | - |
| 0.8689 | 27900 | 0.7698 | - | - | - |
| 0.8720 | 28000 | 1.2758 | 1.9704 | 0.8127 | - |
| 0.8751 | 28100 | 1.3248 | - | - | - |
| 0.8783 | 28200 | 1.041 | - | - | - |
| 0.8814 | 28300 | 1.6066 | - | - | - |
| 0.8845 | 28400 | 1.9033 | - | - | - |
| 0.8876 | 28500 | 0.8781 | - | - | - |
| 0.8907 | 28600 | 0.9345 | - | - | - |
| 0.8938 | 28700 | 0.9209 | - | - | - |
| 0.8969 | 28800 | 1.1443 | - | - | - |
| 0.9001 | 28900 | 0.9522 | - | - | - |
| 0.9032 | 29000 | 1.4295 | 2.0572 | 0.8111 | - |
| 0.9063 | 29100 | 0.9005 | - | - | - |
| 0.9094 | 29200 | 1.0024 | - | - | - |
| 0.9125 | 29300 | 1.3573 | - | - | - |
| 0.9156 | 29400 | 1.0805 | - | - | - |
| 0.9187 | 29500 | 1.3308 | - | - | - |
| 0.9219 | 29600 | 1.4853 | - | - | - |
| 0.9250 | 29700 | 2.0785 | - | - | - |
| 0.9281 | 29800 | 0.9283 | - | - | - |
| 0.9312 | 29900 | 0.8081 | - | - | - |
| 0.9343 | 30000 | 0.4223 | 2.0404 | 0.8115 | - |
| 0.9374 | 30100 | 0.8565 | - | - | - |
| 0.9405 | 30200 | 0.6674 | - | - | - |
| 0.9437 | 30300 | 0.5499 | - | - | - |
| 0.9468 | 30400 | 0.3212 | - | - | - |
| 0.9499 | 30500 | 0.166 | - | - | - |
| 0.9530 | 30600 | 0.1096 | - | - | - |
| 0.9561 | 30700 | 0.0382 | - | - | - |
| 0.9592 | 30800 | 0.2927 | - | - | - |
| 0.9623 | 30900 | 0.4097 | - | - | - |
| 0.9655 | 31000 | 0.5554 | 2.0068 | 0.8130 | - |
| 0.9686 | 31100 | 0.5783 | - | - | - |
| 0.9717 | 31200 | 0.376 | - | - | - |
| 0.9748 | 31300 | 0.3469 | - | - | - |
| 0.9779 | 31400 | 0.3043 | - | - | - |
| 0.9810 | 31500 | 0.4023 | - | - | - |
| 0.9841 | 31600 | 0.1876 | - | - | - |
| 0.9873 | 31700 | 0.4473 | - | - | - |
| 0.9904 | 31800 | 0.3256 | - | - | - |
| 0.9935 | 31900 | 0.4875 | - | - | - |
| 0.9966 | 32000 | 0.1807 | 2.0122 | 0.8129 | - |
| 0.9997 | 32100 | 0.3249 | - | - | - |
| 1.0 | 32109 | - | - | - | 0.8426 |
</details>
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.1+cu121
- Accelerate: 0.33.0
- Datasets: 2.21.0
- Tokenizers: 0.19.1
## 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",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
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