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clap-htsat-fused-librispeech

Sentence Similarity
sentence-transformers
Safetensors
English
clap
feature-extraction
dense
Generated from Trainer
dataset_size:132553
loss:MultipleNegativesSymmetricRankingLoss
Eval Results
Model card Files
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 ---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:132553
- loss:MultipleNegativesSymmetricRankingLoss
base_model: laion/clap-htsat-fused
widget:
- source_sentence: HE WAS OUT OF HIS MIND WITH SOMETHING HE OVERHEARD ABOUT EATING
PEOPLE'S FLESH AND DRINKING BLOOD WHAT'S THE GOOD OF TALKING LIKE THAT
sentences:
- NESTORIUS WHO DEPENDED ON THE NEAR APPROACH OF HIS EASTERN FRIENDS PERSISTED LIKE
HIS PREDECESSOR CHRYSOSTOM TO DISCLAIM THE JURISDICTION AND TO DISOBEY THE SUMMONS
OF HIS ENEMIES THEY HASTENED HIS TRIAL AND HIS ACCUSER PRESIDED IN THE SEAT OF
JUDGMENT
- THEN BACK I TURNED MY FACE TO THOSE HIGH THINGS WHICH MOVED THEMSELVES TOWARDS
US SO SEDATELY THEY HAD BEEN DISTANCED BY NEW WEDDED BRIDES
- THE PROGRESS OF PRESIDENT DAVIS TO THE NEW CAPITAL SET IN THE VERY FACE OF THE
FOE WAS TO BE ONE HUGE TRIUMPH OF FAITH AND LOYALTY
- source_sentence: I BELIEVE THE SERIOUSNESS OF THE AMERICANS ARISES PARTLY FROM THEIR
PRIDE
sentences:
- YOU HAVE BEEN TO THE HOTEL HE BURST OUT YOU HAVE SEEN CATHERINE
- WHAT DO YOU MEAN SIR
- A HARSH LAUGH FROM COMRADE OSSIPON CUT THE TIRADE DEAD SHORT IN A SUDDEN FALTERING
OF THE TONGUE AND A BEWILDERED UNSTEADINESS OF THE APOSTLE'S MILDLY EXALTED EYES
- source_sentence: BUT YOU OUGHT TO HAVE KNOWN THAT WE ARE ONLY HALF AN HOUR BEHIND
YOU AT SYDENHAM IN THE MATTER OF NEWS
sentences:
- DOWN BELOW IN THE QUIET NARROW STREET MEASURED FOOTSTEPS APPROACHED THE HOUSE
THEN DIED AWAY UNHURRIED AND FIRM AS IF THE PASSER BY HAD STARTED TO PACE OUT
ALL ETERNITY FROM GAS LAMP TO GAS LAMP IN A NIGHT WITHOUT END AND THE DROWSY TICKING
OF THE OLD CLOCK ON THE LANDING BECAME DISTINCTLY AUDIBLE IN THE BEDROOM
- IT WAS A SUMMER NIGHT AND THE GUESTS WERE WANDERING IN AND OUT AT WILL AND THROUGH
HOUSE AND GARDEN AMID LOVELY THINGS OF ALL COLORS AND ODORS
- IF A MAN WERE SLAIN IN BATTLE IT WAS AN OLD CUSTOM TO PLACE HIS BODY AGAINST A
TREE OR ROCK IN A SITTING POSITION ALWAYS FACING THE ENEMY TO INDICATE HIS UNDAUNTED
DEFIANCE AND BRAVERY EVEN IN DEATH
- source_sentence: THE MERCHANT'S DAUGHTER AT FIRST DID NOT ANSWER BUT AS HE KEPT
ON CALLING TO HER SHE FINALLY ASKED HIM WHAT IT WAS THAT HE WANTED
sentences:
- LODGED IN THE BRANCHES OF A PINYON TREE I THINK IT IS BUT HE DOESN'T ANSWER ME
- HOW ASKED TAD
- THE SECOND WAS AS IF HER FLESH AND BONES HAD ALL BEEN FASHIONED OUT OF EMERALD
THE THIRD APPEARED AS SNOW BUT NEWLY FALLEN
- source_sentence: THERE ARE NATURES TOO TO WHOSE SENSE OF JUSTICE THE PRICE EXACTED
LOOMS UP MONSTROUSLY ENORMOUS ODIOUS OPPRESSIVE WORRYING HUMILIATING EXTORTIONATE
INTOLERABLE THOSE ARE THE FANATICS
sentences:
- I SHALL LOCK UP ALL THE DOORS AND WINDOWS IN THE HOUSE AND THEN I SHALL GIVE YOU
MY LATCH KEY AND YOU CAN LET YOURSELF IN AND STAY THE NIGHT HERE THERE IS NO ONE
IN THE HOUSE
- HERE THE HOLY PRELATE OF FERNS MET HIM AND RELATED A VISION IN WHICH HE HAD BEEN
INSTRUCTED TO DEMAND THE ABOLITION OF THE IMPOST
- HE BEGAN TO WISH THAT HE HAD COMPROMISED IN SOME WAY OR OTHER THAT HE HAD SENT
THE MONEY PERHAPS HE COULD DO IT UP HERE
datasets:
- openslr/librispeech_asr
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
co2_eq_emissions:
emissions: 114.78151570511905
energy_consumed: 0.42889417052827883
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 2.094
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: CLAP model trained on COCO Captions
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: librispeech eval
type: librispeech-eval
metrics:
- type: cosine_accuracy@1
value: 0.108
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.196
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.272
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.438
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.108
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.06533333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.054400000000000004
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0438
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.108
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.196
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.272
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.438
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.24322279069515917
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.18493690476190464
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.20597911270433167
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: librispeech test
type: librispeech-test
metrics:
- type: cosine_accuracy@1
value: 0.151
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.288
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.371
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.518
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.151
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.096
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.0742
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0518
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.151
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.288
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.371
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.518
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.31319206378414244
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.25047857142857116
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2693786295421681
name: Cosine Map@100
---
# CLAP model trained on COCO Captions
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) on the [librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr) dataset. It maps sentences & paragraphs to a None-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:** [laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) <!-- at revision 1d58d5192f5e4f16b57c574c7daf3d941404bd06 -->
- **Maximum Sequence Length:** None tokens
- **Output Dimensionality:** None dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr)
- **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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'get_text_features', 'method_output_name': None}, 'audio': {'method': 'get_audio_features', 'method_output_name': None}}, 'module_output_name': 'sentence_embedding', 'architecture': 'ClapModel'})
)
```
## 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("tomaarsen/clap-htsat-fused-librispeech")
# Run inference
sentences = [
'THERE ARE NATURES TOO TO WHOSE SENSE OF JUSTICE THE PRICE EXACTED LOOMS UP MONSTROUSLY ENORMOUS ODIOUS OPPRESSIVE WORRYING HUMILIATING EXTORTIONATE INTOLERABLE THOSE ARE THE FANATICS',
'HE BEGAN TO WISH THAT HE HAD COMPROMISED IN SOME WAY OR OTHER THAT HE HAD SENT THE MONEY PERHAPS HE COULD DO IT UP HERE',
'HERE THE HOLY PRELATE OF FERNS MET HIM AND RELATED A VISION IN WHICH HE HAD BEEN INSTRUCTED TO DEMAND THE ABOLITION OF THE IMPOST',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, -0.4742, -0.2719],
# [-0.4742, 1.0000, 0.8206],
# [-0.2719, 0.8206, 1.0000]])
```
<!--
### 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>
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `librispeech-eval` and `librispeech-test`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | librispeech-eval | librispeech-test |
|:--------------------|:-----------------|:-----------------|
| cosine_accuracy@1 | 0.108 | 0.151 |
| cosine_accuracy@3 | 0.196 | 0.288 |
| cosine_accuracy@5 | 0.272 | 0.371 |
| cosine_accuracy@10 | 0.438 | 0.518 |
| cosine_precision@1 | 0.108 | 0.151 |
| cosine_precision@3 | 0.0653 | 0.096 |
| cosine_precision@5 | 0.0544 | 0.0742 |
| cosine_precision@10 | 0.0438 | 0.0518 |
| cosine_recall@1 | 0.108 | 0.151 |
| cosine_recall@3 | 0.196 | 0.288 |
| cosine_recall@5 | 0.272 | 0.371 |
| cosine_recall@10 | 0.438 | 0.518 |
| **cosine_ndcg@10** | **0.2432** | **0.3132** |
| cosine_mrr@10 | 0.1849 | 0.2505 |
| cosine_map@100 | 0.206 | 0.2694 |
<!--
## 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
#### librispeech_asr
* Dataset: [librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr) at [71cacbf](https://huggingface.co/datasets/openslr/librispeech_asr/tree/71cacbfb7e2354c4226d01e70d77d5fca3d04ba1)
* Size: 132,553 training samples
* Columns: <code>audio</code> and <code>text</code>
* Approximate statistics based on the first 1000 samples:
| | audio | text |
|:--------|:-------------------|:-------------------------------------------------------------------------------------------------|
| type | dict | string |
| details | <ul><li></li></ul> | <ul><li>min: 20 characters</li><li>mean: 189.15 characters</li><li>max: 294 characters</li></ul> |
* Samples:
| audio | text |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>{'path': '374-180298-0000.flac', 'array': array([ 6.92203816e-04, 8.04404495e-04, 8.03834875e-04, ...,<br> -3.02505396e-05, -6.59527450e-06, 1.11444592e-06]), 'sampling_rate': 48000}</code> | <code>CHAPTER SIXTEEN I MIGHT HAVE TOLD YOU OF THE BEGINNING OF THIS LIAISON IN A FEW LINES BUT I WANTED YOU TO SEE EVERY STEP BY WHICH WE CAME I TO AGREE TO WHATEVER MARGUERITE WISHED</code> |
| <code>{'path': '374-180298-0001.flac', 'array': array([-9.33515839e-05, -1.25754057e-04, -1.44482241e-04, ...,<br> -2.66165182e-04, -2.03228556e-04, -1.03404833e-04]), 'sampling_rate': 48000}</code> | <code>MARGUERITE TO BE UNABLE TO LIVE APART FROM ME IT WAS THE DAY AFTER THE EVENING WHEN SHE CAME TO SEE ME THAT I SENT HER MANON LESCAUT FROM THAT TIME SEEING THAT I COULD NOT CHANGE MY MISTRESS'S LIFE I CHANGED MY OWN</code> |
| <code>{'path': '374-180298-0002.flac', 'array': array([-2.47883319e-04, -2.91854434e-04, -2.82971043e-04, ...,<br> -1.43931946e-04, -1.17829914e-04, -6.32331648e-05]), 'sampling_rate': 48000}</code> | <code>I WISHED ABOVE ALL NOT TO LEAVE MYSELF TIME TO THINK OVER THE POSITION I HAD ACCEPTED FOR IN SPITE OF MYSELF IT WAS A GREAT DISTRESS TO ME THUS MY LIFE GENERALLY SO CALM</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
#### librispeech_asr
* Dataset: [librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr) at [71cacbf](https://huggingface.co/datasets/openslr/librispeech_asr/tree/71cacbfb7e2354c4226d01e70d77d5fca3d04ba1)
* Size: 1,000 evaluation samples
* Columns: <code>audio</code> and <code>text</code>
* Approximate statistics based on the first 1000 samples:
| | audio | text |
|:--------|:-------------------|:------------------------------------------------------------------------------------------------|
| type | dict | string |
| details | <ul><li></li></ul> | <ul><li>min: 8 characters</li><li>mean: 104.62 characters</li><li>max: 516 characters</li></ul> |
* Samples:
| audio | text |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|
| <code>{'path': '2277-149896-0000.flac', 'array': array([ 0.00179741, 0.00170625, 0.00120927, ..., -0.00144462,<br> -0.00102732, -0.00048062]), 'sampling_rate': 48000}</code> | <code>HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE</code> |
| <code>{'path': '2277-149896-0001.flac', 'array': array([ 0.00111104, 0.00081758, 0.00021103, ..., -0.00138193,<br> -0.0009173 , -0.00041702]), 'sampling_rate': 48000}</code> | <code>HE WOULD HAVE TO PAY HER THE MONEY WHICH SHE WOULD NOW REGULARLY DEMAND OR THERE WOULD BE TROUBLE IT DID NOT MATTER WHAT HE DID</code> |
| <code>{'path': '2277-149896-0002.flac', 'array': array([0.00080266, 0.00088462, 0.00083408, ..., 0.00105488, 0.00083673,<br> 0.00043296]), 'sampling_rate': 48000}</code> | <code>HURSTWOOD WALKED THE FLOOR MENTALLY ARRANGING THE CHIEF POINTS OF HIS SITUATION</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`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: 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`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_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
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `bf16`: True
- `fp16`: False
- `half_precision_backend`: None
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `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_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `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_fused
- `optim_args`: None
- `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`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `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`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: 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 | librispeech-eval_cosine_ndcg@10 | librispeech-test_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:-------------------------------:|
| -1 | -1 | - | - | 0.0114 | - |
| 0.0100 | 83 | 3.5908 | - | - | - |
| 0.0200 | 166 | 2.5371 | - | - | - |
| 0.0301 | 249 | 2.1799 | - | - | - |
| 0.0401 | 332 | 2.0415 | - | - | - |
| 0.0501 | 415 | 1.9394 | - | - | - |
| 0.0601 | 498 | 1.8167 | - | - | - |
| 0.0701 | 581 | 1.7589 | - | - | - |
| 0.0801 | 664 | 1.7262 | - | - | - |
| 0.0902 | 747 | 1.7585 | - | - | - |
| 0.1001 | 829 | - | 1.5991 | 0.0335 | - |
| 0.1002 | 830 | 1.7521 | - | - | - |
| 0.1102 | 913 | 1.6822 | - | - | - |
| 0.1202 | 996 | 1.6176 | - | - | - |
| 0.1302 | 1079 | 1.6391 | - | - | - |
| 0.1403 | 1162 | 1.6931 | - | - | - |
| 0.1503 | 1245 | 1.4626 | - | - | - |
| 0.1603 | 1328 | 1.4305 | - | - | - |
| 0.1703 | 1411 | 1.4998 | - | - | - |
| 0.1803 | 1494 | 1.4073 | - | - | - |
| 0.1903 | 1577 | 1.3843 | - | - | - |
| 0.2001 | 1658 | - | 1.2227 | 0.0925 | - |
| 0.2004 | 1660 | 1.3371 | - | - | - |
| 0.2104 | 1743 | 1.3908 | - | - | - |
| 0.2204 | 1826 | 1.2835 | - | - | - |
| 0.2304 | 1909 | 1.3203 | - | - | - |
| 0.2404 | 1992 | 1.2549 | - | - | - |
| 0.2505 | 2075 | 1.2384 | - | - | - |
| 0.2605 | 2158 | 1.2189 | - | - | - |
| 0.2705 | 2241 | 1.1658 | - | - | - |
| 0.2805 | 2324 | 1.1771 | - | - | - |
| 0.2905 | 2407 | 1.2068 | - | - | - |
| 0.3002 | 2487 | - | 1.0471 | 0.1318 | - |
| 0.3005 | 2490 | 1.1708 | - | - | - |
| 0.3106 | 2573 | 1.1389 | - | - | - |
| 0.3206 | 2656 | 1.0786 | - | - | - |
| 0.3306 | 2739 | 1.0792 | - | - | - |
| 0.3406 | 2822 | 1.0562 | - | - | - |
| 0.3506 | 2905 | 0.98 | - | - | - |
| 0.3607 | 2988 | 1.1153 | - | - | - |
| 0.3707 | 3071 | 0.9987 | - | - | - |
| 0.3807 | 3154 | 1.0002 | - | - | - |
| 0.3907 | 3237 | 1.0017 | - | - | - |
| 0.4002 | 3316 | - | 0.8901 | 0.1589 | - |
| 0.4007 | 3320 | 0.9364 | - | - | - |
| 0.4107 | 3403 | 0.9394 | - | - | - |
| 0.4208 | 3486 | 0.9459 | - | - | - |
| 0.4308 | 3569 | 0.9604 | - | - | - |
| 0.4408 | 3652 | 0.9491 | - | - | - |
| 0.4508 | 3735 | 0.9295 | - | - | - |
| 0.4608 | 3818 | 0.9508 | - | - | - |
| 0.4709 | 3901 | 0.9122 | - | - | - |
| 0.4809 | 3984 | 0.8483 | - | - | - |
| 0.4909 | 4067 | 0.8443 | - | - | - |
| 0.5003 | 4145 | - | 0.7955 | 0.1908 | - |
| 0.5009 | 4150 | 0.8838 | - | - | - |
| 0.5109 | 4233 | 0.8367 | - | - | - |
| 0.5209 | 4316 | 0.8516 | - | - | - |
| 0.5310 | 4399 | 0.8112 | - | - | - |
| 0.5410 | 4482 | 0.8368 | - | - | - |
| 0.5510 | 4565 | 0.873 | - | - | - |
| 0.5610 | 4648 | 0.8156 | - | - | - |
| 0.5710 | 4731 | 0.8864 | - | - | - |
| 0.5811 | 4814 | 0.8278 | - | - | - |
| 0.5911 | 4897 | 0.8006 | - | - | - |
| 0.6004 | 4974 | - | 0.7649 | 0.1874 | - |
| 0.6011 | 4980 | 0.8199 | - | - | - |
| 0.6111 | 5063 | 0.7475 | - | - | - |
| 0.6211 | 5146 | 0.7345 | - | - | - |
| 0.6311 | 5229 | 0.7301 | - | - | - |
| 0.6412 | 5312 | 0.774 | - | - | - |
| 0.6512 | 5395 | 0.7391 | - | - | - |
| 0.6612 | 5478 | 0.6929 | - | - | - |
| 0.6712 | 5561 | 0.7218 | - | - | - |
| 0.6812 | 5644 | 0.7071 | - | - | - |
| 0.6912 | 5727 | 0.7024 | - | - | - |
| 0.7004 | 5803 | - | 0.6712 | 0.2419 | - |
| 0.7013 | 5810 | 0.6428 | - | - | - |
| 0.7113 | 5893 | 0.6719 | - | - | - |
| 0.7213 | 5976 | 0.6972 | - | - | - |
| 0.7313 | 6059 | 0.7043 | - | - | - |
| 0.7413 | 6142 | 0.663 | - | - | - |
| 0.7514 | 6225 | 0.6963 | - | - | - |
| 0.7614 | 6308 | 0.6591 | - | - | - |
| 0.7714 | 6391 | 0.6736 | - | - | - |
| 0.7814 | 6474 | 0.7033 | - | - | - |
| 0.7914 | 6557 | 0.6314 | - | - | - |
| 0.8005 | 6632 | - | 0.6806 | 0.2319 | - |
| 0.8014 | 6640 | 0.6508 | - | - | - |
| 0.8115 | 6723 | 0.6532 | - | - | - |
| 0.8215 | 6806 | 0.6788 | - | - | - |
| 0.8315 | 6889 | 0.6038 | - | - | - |
| 0.8415 | 6972 | 0.658 | - | - | - |
| 0.8515 | 7055 | 0.656 | - | - | - |
| 0.8616 | 7138 | 0.6533 | - | - | - |
| 0.8716 | 7221 | 0.601 | - | - | - |
| 0.8816 | 7304 | 0.6243 | - | - | - |
| 0.8916 | 7387 | 0.6315 | - | - | - |
| 0.9005 | 7461 | - | 0.6526 | 0.2432 | - |
| 0.9016 | 7470 | 0.5707 | - | - | - |
| 0.9116 | 7553 | 0.5778 | - | - | - |
| 0.9217 | 7636 | 0.5736 | - | - | - |
| 0.9317 | 7719 | 0.615 | - | - | - |
| 0.9417 | 7802 | 0.5756 | - | - | - |
| 0.9517 | 7885 | 0.5724 | - | - | - |
| 0.9617 | 7968 | 0.5678 | - | - | - |
| 0.9718 | 8051 | 0.5661 | - | - | - |
| 0.9818 | 8134 | 0.6162 | - | - | - |
| 0.9918 | 8217 | 0.5766 | - | - | - |
| -1 | -1 | - | - | - | 0.3132 |
</details>
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.429 kWh
- **Carbon Emitted**: 0.115 kg of CO2
- **Hours Used**: 2.094 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 5.2.0.dev0
- Transformers: 4.57.0.dev0
- PyTorch: 2.8.0+cu128
- Accelerate: 1.6.0
- Datasets: 3.6.0
- Tokenizers: 0.22.1
## 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",
}
```
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