sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:9014210 loss:MSELoss arxiv:1908.10084 arxiv:2004.09813 base_model:sentence-transformers/all-MiniLM-L6-v2 base_model:finetune:sentence-transformers/all-MiniLM-L6-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- negative_mse
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:9014210
- loss:MSELoss
widget:
- source_sentence: At an outdoor event in an Asian-themed area, a crowd congregates
    as one person in a yellow Chinese dragon costume confronts the camera.
  sentences:
  - Boy dressed in blue holds a toy.
  - the animal is running
  - Two young asian men are squatting.
- source_sentence: A man with a shopping cart is studying the shelves in a supermarket
    aisle.
  sentences:
  - The children are watching TV at home.
  - Three young boys one is holding a camera and another is holding a green toy all
    are wearing t-shirt and smiling.
  - A large group of people are gathered outside of a brick building lit with spotlights.
- source_sentence: The door is open.
  sentences:
  - There are three men in this picture, two are on motorbikes, one of the men has
    a large piece of furniture on the back of his bike, the other is about to be handed
    a piece of paper by a man in a white shirt.
  - People are playing music.
  - A girl is using an apple laptop with her headphones in her ears.
- source_sentence: A small group of children are standing in a classroom and one of
    them has a foot in a trashcan, which also has a rope leading out of it.
  sentences:
  - Children are swimming at the beach.
  - Women are celebrating at a bar.
  - Some men with jerseys are in a bar, watching a soccer match.
- source_sentence: A black dog is drinking next to a brown and white dog that is looking
    at an orange ball in the lake, whilst a horse and rider passes behind.
  sentences:
  - There are two people running around a track in lane three and the one wearing
    a blue shirt with a green thing over the eyes is just barely ahead of the guy
    wearing an orange shirt and sunglasses.
  - A girl is sitting
  - the guy is dead
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.8649118460865306
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.864870367786895
      name: Spearman Cosine
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: negative_mse
      value: -0.024522081366740167
      name: Negative Mse
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.8202873084532003
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8190218550432983
      name: Spearman Cosine
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **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': 256, '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})
  (2): Normalize()
)
```

## 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("aleynahukmet/all-MiniLM-L6-v2-8-layers")
# Run inference
sentences = [
    'A black dog is drinking next to a brown and white dog that is looking at an orange ball in the lake, whilst a horse and rider passes behind.',
    'There are two people running around a track in lane three and the one wearing a blue shirt with a green thing over the eyes is just barely ahead of the guy wearing an orange shirt and sunglasses.',
    'the guy is dead',
]
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

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

| Metric              | sts-dev    | sts-test  |
|:--------------------|:-----------|:----------|
| pearson_cosine      | 0.8649     | 0.8203    |
| **spearman_cosine** | **0.8649** | **0.819** |

#### Knowledge Distillation

* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)

| Metric           | Value       |
|:-----------------|:------------|
| **negative_mse** | **-0.0245** |

<!--
## 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: 9,014,210 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence                                                                          | label                                |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------|
  | type    | string                                                                            | list                                 |
  | details | <ul><li>min: 4 tokens</li><li>mean: 12.24 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
* Samples:
  | sentence                                                                   | label                                                                                                                             |
  |:---------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>[-0.009216307662427425, 0.003964003175497055, 0.04029734805226326, 0.0030935262329876423, -0.03516044840216637, ...]</code> |
  | <code>Children smiling and waving at camera</code>                         | <code>[-0.03215238079428673, 0.06086821109056473, 0.013251038268208504, -0.017755677923560143, 0.07927625626325607, ...]</code>   |
  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>[-0.020561737939715385, -0.03641558438539505, -0.039370208978652954, -0.0975518748164177, 0.005307587794959545, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)

### Evaluation Dataset

#### Unnamed Dataset


* Size: 10,000 evaluation samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence                                                                          | label                                |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------|
  | type    | string                                                                            | list                                 |
  | details | <ul><li>min: 5 tokens</li><li>mean: 13.23 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
* Samples:
  | sentence                                                                                                                                                                       | label                                                                                                                              |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>[-0.007923883385956287, -0.024198176339268684, 0.034445445984601974, 0.036053989082574844, -0.06740871071815491, ...]</code> |
  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>[-0.08869566023349762, 0.02789478376507759, 0.060685668140649796, -0.02580258436501026, 0.008359752595424652, ...]</code>    |
  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>[0.027255145832896233, 0.07622072845697403, 0.025504805147647858, -0.0542026124894619, -0.052822694182395935, ...]</code>    |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 0.0001
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True

#### 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`: 64
- `per_device_eval_batch_size`: 64
- `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`: 0.999
- `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`: 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`: 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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch     | Step       | Training Loss | Validation Loss | sts-dev_spearman_cosine | negative_mse | sts-test_spearman_cosine |
|:---------:|:----------:|:-------------:|:---------------:|:-----------------------:|:------------:|:------------------------:|
| 0         | 0          | -             | -               | 0.7048                  | -0.3846      | -                        |
| 0.0071    | 1000       | 0.0032        | -               | -                       | -            | -                        |
| 0.0142    | 2000       | 0.0023        | -               | -                       | -            | -                        |
| 0.0213    | 3000       | 0.0019        | -               | -                       | -            | -                        |
| 0.0284    | 4000       | 0.0017        | -               | -                       | -            | -                        |
| 0.0355    | 5000       | 0.0015        | 0.0013          | 0.8149                  | -0.1309      | -                        |
| 0.0426    | 6000       | 0.0014        | -               | -                       | -            | -                        |
| 0.0497    | 7000       | 0.0012        | -               | -                       | -            | -                        |
| 0.0568    | 8000       | 0.0011        | -               | -                       | -            | -                        |
| 0.0639    | 9000       | 0.001         | -               | -                       | -            | -                        |
| 0.0710    | 10000      | 0.001         | 0.0008          | 0.8495                  | -0.0754      | -                        |
| 0.0781    | 11000      | 0.0009        | -               | -                       | -            | -                        |
| 0.0852    | 12000      | 0.0008        | -               | -                       | -            | -                        |
| 0.0923    | 13000      | 0.0008        | -               | -                       | -            | -                        |
| 0.0994    | 14000      | 0.0007        | -               | -                       | -            | -                        |
| 0.1065    | 15000      | 0.0007        | 0.0005          | 0.8569                  | -0.0528      | -                        |
| 0.1136    | 16000      | 0.0007        | -               | -                       | -            | -                        |
| 0.1207    | 17000      | 0.0007        | -               | -                       | -            | -                        |
| 0.1278    | 18000      | 0.0006        | -               | -                       | -            | -                        |
| 0.1349    | 19000      | 0.0006        | -               | -                       | -            | -                        |
| 0.1420    | 20000      | 0.0006        | 0.0004          | 0.8589                  | -0.0438      | -                        |
| 0.1491    | 21000      | 0.0006        | -               | -                       | -            | -                        |
| 0.1562    | 22000      | 0.0006        | -               | -                       | -            | -                        |
| 0.1633    | 23000      | 0.0006        | -               | -                       | -            | -                        |
| 0.1704    | 24000      | 0.0006        | -               | -                       | -            | -                        |
| 0.1775    | 25000      | 0.0005        | 0.0004          | 0.8608                  | -0.0392      | -                        |
| 0.1846    | 26000      | 0.0005        | -               | -                       | -            | -                        |
| 0.1917    | 27000      | 0.0005        | -               | -                       | -            | -                        |
| 0.1988    | 28000      | 0.0005        | -               | -                       | -            | -                        |
| 0.2059    | 29000      | 0.0005        | -               | -                       | -            | -                        |
| 0.2130    | 30000      | 0.0005        | 0.0004          | 0.8619                  | -0.0363      | -                        |
| 0.2201    | 31000      | 0.0005        | -               | -                       | -            | -                        |
| 0.2272    | 32000      | 0.0005        | -               | -                       | -            | -                        |
| 0.2343    | 33000      | 0.0005        | -               | -                       | -            | -                        |
| 0.2414    | 34000      | 0.0005        | -               | -                       | -            | -                        |
| 0.2485    | 35000      | 0.0005        | 0.0003          | 0.8619                  | -0.0343      | -                        |
| 0.2556    | 36000      | 0.0005        | -               | -                       | -            | -                        |
| 0.2627    | 37000      | 0.0005        | -               | -                       | -            | -                        |
| 0.2698    | 38000      | 0.0005        | -               | -                       | -            | -                        |
| 0.2769    | 39000      | 0.0005        | -               | -                       | -            | -                        |
| 0.2840    | 40000      | 0.0005        | 0.0003          | 0.8613                  | -0.0329      | -                        |
| 0.2911    | 41000      | 0.0005        | -               | -                       | -            | -                        |
| 0.2982    | 42000      | 0.0005        | -               | -                       | -            | -                        |
| 0.3053    | 43000      | 0.0005        | -               | -                       | -            | -                        |
| 0.3124    | 44000      | 0.0005        | -               | -                       | -            | -                        |
| 0.3195    | 45000      | 0.0005        | 0.0003          | 0.8633                  | -0.0316      | -                        |
| 0.3266    | 46000      | 0.0005        | -               | -                       | -            | -                        |
| 0.3337    | 47000      | 0.0005        | -               | -                       | -            | -                        |
| 0.3408    | 48000      | 0.0005        | -               | -                       | -            | -                        |
| 0.3479    | 49000      | 0.0004        | -               | -                       | -            | -                        |
| 0.3550    | 50000      | 0.0004        | 0.0003          | 0.8631                  | -0.0306      | -                        |
| 0.3621    | 51000      | 0.0004        | -               | -                       | -            | -                        |
| 0.3692    | 52000      | 0.0004        | -               | -                       | -            | -                        |
| 0.3763    | 53000      | 0.0004        | -               | -                       | -            | -                        |
| 0.3834    | 54000      | 0.0004        | -               | -                       | -            | -                        |
| 0.3905    | 55000      | 0.0004        | 0.0003          | 0.8635                  | -0.0297      | -                        |
| 0.3976    | 56000      | 0.0004        | -               | -                       | -            | -                        |
| 0.4047    | 57000      | 0.0004        | -               | -                       | -            | -                        |
| 0.4118    | 58000      | 0.0004        | -               | -                       | -            | -                        |
| 0.4189    | 59000      | 0.0004        | -               | -                       | -            | -                        |
| 0.4260    | 60000      | 0.0004        | 0.0003          | 0.8640                  | -0.0290      | -                        |
| 0.4331    | 61000      | 0.0004        | -               | -                       | -            | -                        |
| 0.4402    | 62000      | 0.0004        | -               | -                       | -            | -                        |
| 0.4473    | 63000      | 0.0004        | -               | -                       | -            | -                        |
| 0.4544    | 64000      | 0.0004        | -               | -                       | -            | -                        |
| 0.4615    | 65000      | 0.0004        | 0.0003          | 0.8644                  | -0.0285      | -                        |
| 0.4686    | 66000      | 0.0004        | -               | -                       | -            | -                        |
| 0.4757    | 67000      | 0.0004        | -               | -                       | -            | -                        |
| 0.4828    | 68000      | 0.0004        | -               | -                       | -            | -                        |
| 0.4899    | 69000      | 0.0004        | -               | -                       | -            | -                        |
| 0.4970    | 70000      | 0.0004        | 0.0003          | 0.8641                  | -0.0280      | -                        |
| 0.5041    | 71000      | 0.0004        | -               | -                       | -            | -                        |
| 0.5112    | 72000      | 0.0004        | -               | -                       | -            | -                        |
| 0.5183    | 73000      | 0.0004        | -               | -                       | -            | -                        |
| 0.5254    | 74000      | 0.0004        | -               | -                       | -            | -                        |
| 0.5325    | 75000      | 0.0004        | 0.0003          | 0.8648                  | -0.0276      | -                        |
| 0.5396    | 76000      | 0.0004        | -               | -                       | -            | -                        |
| 0.5467    | 77000      | 0.0004        | -               | -                       | -            | -                        |
| 0.5538    | 78000      | 0.0004        | -               | -                       | -            | -                        |
| 0.5609    | 79000      | 0.0004        | -               | -                       | -            | -                        |
| 0.5680    | 80000      | 0.0004        | 0.0003          | 0.8644                  | -0.0271      | -                        |
| 0.5751    | 81000      | 0.0004        | -               | -                       | -            | -                        |
| 0.5822    | 82000      | 0.0004        | -               | -                       | -            | -                        |
| 0.5893    | 83000      | 0.0004        | -               | -                       | -            | -                        |
| 0.5964    | 84000      | 0.0004        | -               | -                       | -            | -                        |
| 0.6035    | 85000      | 0.0004        | 0.0003          | 0.8648                  | -0.0267      | -                        |
| 0.6106    | 86000      | 0.0004        | -               | -                       | -            | -                        |
| 0.6177    | 87000      | 0.0004        | -               | -                       | -            | -                        |
| 0.6248    | 88000      | 0.0004        | -               | -                       | -            | -                        |
| 0.6319    | 89000      | 0.0004        | -               | -                       | -            | -                        |
| 0.6390    | 90000      | 0.0004        | 0.0003          | 0.8645                  | -0.0264      | -                        |
| 0.6461    | 91000      | 0.0004        | -               | -                       | -            | -                        |
| 0.6532    | 92000      | 0.0004        | -               | -                       | -            | -                        |
| 0.6603    | 93000      | 0.0004        | -               | -                       | -            | -                        |
| 0.6674    | 94000      | 0.0004        | -               | -                       | -            | -                        |
| 0.6745    | 95000      | 0.0004        | 0.0003          | 0.8643                  | -0.0261      | -                        |
| 0.6816    | 96000      | 0.0004        | -               | -                       | -            | -                        |
| 0.6887    | 97000      | 0.0004        | -               | -                       | -            | -                        |
| 0.6958    | 98000      | 0.0004        | -               | -                       | -            | -                        |
| 0.7029    | 99000      | 0.0004        | -               | -                       | -            | -                        |
| 0.7100    | 100000     | 0.0004        | 0.0003          | 0.8643                  | -0.0259      | -                        |
| 0.7171    | 101000     | 0.0004        | -               | -                       | -            | -                        |
| 0.7242    | 102000     | 0.0004        | -               | -                       | -            | -                        |
| 0.7313    | 103000     | 0.0004        | -               | -                       | -            | -                        |
| 0.7384    | 104000     | 0.0004        | -               | -                       | -            | -                        |
| 0.7455    | 105000     | 0.0004        | 0.0003          | 0.8646                  | -0.0257      | -                        |
| 0.7526    | 106000     | 0.0004        | -               | -                       | -            | -                        |
| 0.7597    | 107000     | 0.0004        | -               | -                       | -            | -                        |
| 0.7668    | 108000     | 0.0004        | -               | -                       | -            | -                        |
| 0.7739    | 109000     | 0.0004        | -               | -                       | -            | -                        |
| 0.7810    | 110000     | 0.0004        | 0.0003          | 0.8637                  | -0.0254      | -                        |
| 0.7881    | 111000     | 0.0004        | -               | -                       | -            | -                        |
| 0.7952    | 112000     | 0.0004        | -               | -                       | -            | -                        |
| 0.8023    | 113000     | 0.0004        | -               | -                       | -            | -                        |
| 0.8094    | 114000     | 0.0004        | -               | -                       | -            | -                        |
| 0.8165    | 115000     | 0.0004        | 0.0003          | 0.8643                  | -0.0252      | -                        |
| 0.8236    | 116000     | 0.0004        | -               | -                       | -            | -                        |
| 0.8307    | 117000     | 0.0004        | -               | -                       | -            | -                        |
| 0.8378    | 118000     | 0.0004        | -               | -                       | -            | -                        |
| 0.8449    | 119000     | 0.0004        | -               | -                       | -            | -                        |
| 0.8520    | 120000     | 0.0004        | 0.0003          | 0.8645                  | -0.0250      | -                        |
| 0.8591    | 121000     | 0.0004        | -               | -                       | -            | -                        |
| 0.8662    | 122000     | 0.0004        | -               | -                       | -            | -                        |
| 0.8733    | 123000     | 0.0004        | -               | -                       | -            | -                        |
| 0.8804    | 124000     | 0.0004        | -               | -                       | -            | -                        |
| 0.8875    | 125000     | 0.0004        | 0.0002          | 0.8646                  | -0.0248      | -                        |
| 0.8946    | 126000     | 0.0004        | -               | -                       | -            | -                        |
| 0.9017    | 127000     | 0.0004        | -               | -                       | -            | -                        |
| 0.9088    | 128000     | 0.0004        | -               | -                       | -            | -                        |
| 0.9159    | 129000     | 0.0004        | -               | -                       | -            | -                        |
| 0.9230    | 130000     | 0.0004        | 0.0002          | 0.8647                  | -0.0247      | -                        |
| 0.9301    | 131000     | 0.0004        | -               | -                       | -            | -                        |
| 0.9372    | 132000     | 0.0004        | -               | -                       | -            | -                        |
| 0.9443    | 133000     | 0.0004        | -               | -                       | -            | -                        |
| 0.9514    | 134000     | 0.0004        | -               | -                       | -            | -                        |
| 0.9585    | 135000     | 0.0004        | 0.0002          | 0.8646                  | -0.0246      | -                        |
| 0.9656    | 136000     | 0.0004        | -               | -                       | -            | -                        |
| 0.9727    | 137000     | 0.0004        | -               | -                       | -            | -                        |
| 0.9798    | 138000     | 0.0004        | -               | -                       | -            | -                        |
| 0.9869    | 139000     | 0.0004        | -               | -                       | -            | -                        |
| **0.994** | **140000** | **0.0004**    | **0.0002**      | **0.8649**              | **-0.0245**  | **-**                    |
| 1.0       | 140848     | -             | -               | -                       | -            | 0.8190                   |

* The bold row denotes the saved checkpoint.
</details>

### Framework Versions
- Python: 3.12.4
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 1.0.1
- 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",
}
```

#### MSELoss
```bibtex
@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}
```

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