README.md

---
language: []
library_name: sentence-transformers
tags:
- mteb
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:AnglELoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
- source_sentence: 有些人在路上溜达。
  sentences:
  - Folk går
  - Otururken gitar çalan adam.
  - ארה"ב קבעה שסוריה השתמשה בנשק כימי
- source_sentence: 緬甸以前稱為緬甸。
  sentences:
  - 缅甸以前叫缅甸。
  - This is very contradictory.
  - 한 남자가 아기를 안고 의자에 앉아 잠들어 있다.
- source_sentence: אדם כותב.
  sentences:
  - האדם כותב.
  - questa non è una risposta.
  - 7 שוטרים נהרגו ו-4 שוטרים נפצעו.
- source_sentence: הם מפחדים.
  sentences:
  - liên quan đến rủi ro đáng kể;
  - A man is playing a guitar.
  - A man is playing a piano.
- source_sentence: 一个女人正在洗澡。
  sentences:
  - A woman is taking a bath.
  - En jente børster håret sitt
  - אדם מחלק תפוח אדמה.
pipeline_tag: sentence-similarity
model-index:
- name: Gameselo/STS-multilingual-mpnet-base-v2
  results:
  - dataset:
      config: it
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.6847049462613332
    task:
      type: STS
  - dataset:
      config: es
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.6620948502618977
    task:
      type: STS
  - dataset:
      config: fr
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.7875616631597785
    task:
      type: STS
  - dataset:
      config: pl-en
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.7510805416538202
    task:
      type: STS
  - dataset:
      config: ar
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.6265329479575293
    task:
      type: STS
  - dataset:
      config: pl
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.4335552432730643
    task:
      type: STS
  - dataset:
      config: de
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.5774252131250034
    task:
      type: STS
  - dataset:
      config: tr
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.6383757017928495
    task:
      type: STS
  - dataset:
      config: es-it
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.6624635951676386
    task:
      type: STS
  - dataset:
      config: ru
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.5866853707548388
    task:
      type: STS
  - dataset:
      config: en
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.6385354535483773
    task:
      type: STS
  - dataset:
      config: zh-en
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.6537294853166558
    task:
      type: STS
  - dataset:
      config: zh
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.6319430830291571
    task:
      type: STS
  - dataset:
      config: fr-pl
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.8451542547285167
    task:
      type: STS
  - dataset:
      config: de-fr
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.5798716781400349
    task:
      type: STS
  - dataset:
      config: es-en
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.7518021273920814
    task:
      type: STS
  - dataset:
      config: de-en
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.5749790581441845
    task:
      type: STS
  - dataset:
      config: de-pl
      name: MTEB STS22
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.44220332625465214
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STSBenchmark
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
      split: test
      type: mteb/stsbenchmark-sts
    metrics:
      - type: cosine_spearman
        value: 0.9762486352335524
    task:
      type: STS
  - dataset:
      config: en-tr
      name: MTEB STS17
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.7987027653005363
    task:
      type: STS
  - dataset:
      config: ko-ko
      name: MTEB STS17
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.9766336939338607
    task:
      type: STS
  - dataset:
      config: fr-en
      name: MTEB STS17
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.9067607122592818
    task:
      type: STS
  - dataset:
      config: en-ar
      name: MTEB STS17
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.7703365842088069
    task:
      type: STS
  - dataset:
      config: nl-en
      name: MTEB STS17
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.9114826394926738
    task:
      type: STS
  - dataset:
      config: it-en
      name: MTEB STS17
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.9246785886944904
    task:
      type: STS
  - dataset:
      config: ar-ar
      name: MTEB STS17
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.8124393788492182
    task:
      type: STS
  - dataset:
      config: es-es
      name: MTEB STS17
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.872701191632785
    task:
      type: STS
  - dataset:
      config: en-de
      name: MTEB STS17
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.9109414091487618
    task:
      type: STS
  - dataset:
      config: es-en
      name: MTEB STS17
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.8553203530552356
    task:
      type: STS
  - dataset:
      config: en-en
      name: MTEB STS17
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
      - type: cosine_spearman
        value: 0.9378741534997558
    task:
      type: STS
---

## State-of-the-Art Results Comparison (MTEB STS Multilingual Leaderboard)

| Dataset           | State-of-the-art (Multi) | STSb-XLM-RoBERTa-base |    STS Multilingual MPNet base v2    |
|-------------------|--------------------------|-----------------------|--------------------------------------|
| Average           | 73.17                    | 71.68                 | **73.89**                            |
| STS17 (ar-ar)     | **81.87**                | 80.43                 | 81.24                                |
| STS17 (en-ar)     | **81.22**                | 76.3                  | 77.03                                |
| STS17 (en-de)     | 87.3                     | 91.06                 | **91.09**                            |
| STS17 (en-tr)     | 77.18                    | **80.74**             | 79.87                                |
| STS17 (es-en)     | **88.24**                | 83.09                 | 85.53                                |
| STS17 (es-es)     | **88.25**                | 84.16                 | 87.27                                |
| STS17 (fr-en)     | 88.06                    | **91.33**             | 90.68                                |
| STS17 (it-en)     | 89.68                    | **92.87**             | 92.47                                |
| STS17 (ko-ko)     | 83.69                    | **97.67**             | 97.66                                |
| STS17 (nl-en)     | 88.25                    | **92.13**             | 91.15                                |
| STS22 (ar)        | 58.67                    | 58.67                 | **62.66**                            |
| STS22 (de)        | **60.12**                | 52.17                 | 57.74                                |
| STS22 (de-en)     | **60.92**                | 58.5                  | 57.5                                 |
| STS22 (de-fr)     | **67.79**                | 51.28                 | 57.99                                |
| STS22 (de-pl)     | **58.69**                | 44.56                 | 44.22                                |
| STS22 (es)        | **68.57**                | 63.68                 | 66.21                                |
| STS22 (es-en)     | **78.8**                 | 70.65                 | 75.18                                |
| STS22 (es-it)     | **75.04**                | 60.88                 | 66.25                                |
| STS22 (fr)        | **83.75**                | 76.46                 | 78.76                                |
| STS22 (fr-pl)     | 84.52                    | 84.52                 | **84.52**                            |
| STS22 (it)        | **79.28**                | 66.73                 | 68.47                                |
| STS22 (pl)        | 42.08                    | 41.18                 | **43.36**                            |
| STS22 (pl-en)     | **77.5**                 | 64.35                 | 75.11                                |
| STS22 (ru)        | **61.71**                | 58.59                 | 58.67                                |
| STS22 (tr)        | **68.72**                | 57.52                 | 63.84                                |
| STS22 (zh-en)     | **71.88**                | 60.69                 | 65.37                                |
| STSb              | 89.86                    | 95.05                 | **95.15**                            |

**Bold** indicates the best result in each row.

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). It maps sentences & paragraphs to a 768-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 79f2382ceacceacdf38563d7c5d16b9ff8d725d6 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 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: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Gameselo/STS-multilingual-mpnet-base-v2")
# Run inference
sentences = [
    '一个女人正在洗澡。',
    'A woman is taking a bath.',
    'En jente børster håret sitt',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.9551     |
| **spearman_cosine** | **0.9593** |
| pearson_manhattan   | 0.927      |
| spearman_manhattan  | 0.9383     |
| pearson_euclidean   | 0.9278     |
| spearman_euclidean  | 0.9394     |
| pearson_dot         | 0.876      |
| spearman_dot        | 0.8865     |
| pearson_max         | 0.9551     |
| spearman_max        | 0.9593     |

#### Evalutation results vs SOTA results
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.948      |
| **spearman_cosine** | **0.9515** |
| pearson_manhattan   | 0.9252     |
| spearman_manhattan  | 0.9352     |
| pearson_euclidean   | 0.9258     |
| spearman_euclidean  | 0.9364     |
| pearson_dot         | 0.8443     |
| spearman_dot        | 0.8435     |
| pearson_max         | 0.948      |
| spearman_max        | 0.9515     |

<!--
## 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

#### Unnamed Dataset


* Size: 226,547 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                         | sentence_1                                                                         | label                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | float                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 20.05 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.94 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 1.92</li><li>max: 398.6</li></ul> |
* Samples:
  | sentence_0                                                         | sentence_1                                                      | label                            |
  |:-------------------------------------------------------------------|:----------------------------------------------------------------|:---------------------------------|
  | <code>Bir kadın makineye dikiş dikiyor.</code>                     | <code>Bir kadın biraz et ekiyor.</code>                         | <code>0.12</code>                |
  | <code>Snowden 'gegeven vluchtelingendocument door Ecuador'.</code> | <code>Snowden staat op het punt om uit Moskou te vliegen</code> | <code>0.24000000953674316</code> |
  | <code>Czarny pies idzie mostem przez wodę</code>                   | <code>Czarny pies nie idzie mostem przez wodę</code>            | <code>0.74000000954</code>       |
* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_angle_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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
- `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`: 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, '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_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:-----------------------:|:------------------------:|
| 0.5650 | 500  | 10.9426       | -                       | -                        |
| 1.0    | 885  | -             | 0.9202                  | -                        |
| 1.1299 | 1000 | 9.7184        | -                       | -                        |
| 1.6949 | 1500 | 9.5348        | -                       | -                        |
| 2.0    | 1770 | -             | 0.9400                  | -                        |
| 2.2599 | 2000 | 9.4412        | -                       | -                        |
| 2.8249 | 2500 | 9.3097        | -                       | -                        |
| 3.0    | 2655 | -             | 0.9489                  | -                        |
| 3.3898 | 3000 | 9.2357        | -                       | -                        |
| 3.9548 | 3500 | 9.1594        | -                       | -                        |
| 4.0    | 3540 | -             | 0.9528                  | -                        |
| 4.5198 | 4000 | 9.0963        | -                       | -                        |
| 5.0    | 4425 | -             | 0.9553                  | -                        |
| 5.0847 | 4500 | 9.0382        | -                       | -                        |
| 5.6497 | 5000 | 8.9837        | -                       | -                        |
| 6.0    | 5310 | -             | 0.9567                  | -                        |
| 6.2147 | 5500 | 8.9403        | -                       | -                        |
| 6.7797 | 6000 | 8.8841        | -                       | -                        |
| 7.0    | 6195 | -             | 0.9581                  | -                        |
| 7.3446 | 6500 | 8.8513        | -                       | -                        |
| 7.9096 | 7000 | 8.81          | -                       | -                        |
| 8.0    | 7080 | -             | 0.9582                  | -                        |
| 8.4746 | 7500 | 8.8069        | -                       | -                        |
| 9.0    | 7965 | -             | 0.9589                  | -                        |
| 9.0395 | 8000 | 8.7616        | -                       | -                        |
| 9.6045 | 8500 | 8.7521        | -                       | -                        |
| 10.0   | 8850 | -             | 0.9593                  | 0.6266                   |


### Framework Versions
- Python: 3.9.7
- Sentence Transformers: 3.0.0
- Transformers: 4.40.1
- PyTorch: 2.3.0+cu121
- Accelerate: 0.29.3
- Datasets: 2.19.0
- 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",
}
```

#### AnglELoss
```bibtex
@misc{li2023angleoptimized,
    title={AnglE-optimized Text Embeddings}, 
    author={Xianming Li and Jing Li},
    year={2023},
    eprint={2309.12871},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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