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stsb-distilbert-base-ocl

Sentence Similarity
sentence-transformers
Safetensors
English
distilbert
feature-extraction
loss:OnlineContrastiveLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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stsb-distilbert-base-ocl / README.md
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---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- loss:OnlineContrastiveLoss
base_model: sentence-transformers/stsb-distilbert-base
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
- average_precision
- f1
- precision
- recall
- threshold
- 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
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
widget:
- source_sentence: Why did he go MIA?
sentences:
- Why did Yahoo kill Konfabulator?
- Why do people get angry with me?
- What are the best waterproof guns?
- source_sentence: Who is a soulmate?
sentences:
- Is she the “One”?
- Who is Pakistan's biggest enemy?
- Will smoking weed help with my anxiety?
- source_sentence: Is this poem good?
sentences:
- Is my poem any good?
- How can I become a good speaker?
- What is feminism?
- source_sentence: Who invented Yoga?
sentences:
- How was yoga invented?
- Who owns this number 3152150252?
- What is Dynamics CRM Services?
- source_sentence: Is stretching bad?
sentences:
- Is stretching good for you?
- If i=0; what will i=i++ do to i?
- What is the Output of this C program ?
pipeline_tag: sentence-similarity
co2_eq_emissions:
emissions: 15.707175691967695
energy_consumed: 0.040409299905757354
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: 0.202
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: SentenceTransformer based on sentence-transformers/stsb-distilbert-base
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: quora duplicates
type: quora-duplicates
metrics:
- type: cosine_accuracy
value: 0.86
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8104104995727539
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.8250591016548463
name: Cosine F1
- type: cosine_f1_threshold
value: 0.7247534394264221
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.7347368421052631
name: Cosine Precision
- type: cosine_recall
value: 0.9407008086253369
name: Cosine Recall
- type: cosine_ap
value: 0.887247904332921
name: Cosine Ap
- type: dot_accuracy
value: 0.828
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 157.35491943359375
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.7898550724637681
name: Dot F1
- type: dot_f1_threshold
value: 145.7113037109375
name: Dot F1 Threshold
- type: dot_precision
value: 0.7155361050328227
name: Dot Precision
- type: dot_recall
value: 0.8814016172506739
name: Dot Recall
- type: dot_ap
value: 0.8369433397850002
name: Dot Ap
- type: manhattan_accuracy
value: 0.868
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 208.00347900390625
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.8307692307692308
name: Manhattan F1
- type: manhattan_f1_threshold
value: 208.00347900390625
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.7921760391198044
name: Manhattan Precision
- type: manhattan_recall
value: 0.8733153638814016
name: Manhattan Recall
- type: manhattan_ap
value: 0.8868217413983182
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.867
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 9.269388198852539
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.8301404853128991
name: Euclidean F1
- type: euclidean_f1_threshold
value: 9.525729179382324
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.7888349514563107
name: Euclidean Precision
- type: euclidean_recall
value: 0.876010781671159
name: Euclidean Recall
- type: euclidean_ap
value: 0.8884154240019244
name: Euclidean Ap
- type: max_accuracy
value: 0.868
name: Max Accuracy
- type: max_accuracy_threshold
value: 208.00347900390625
name: Max Accuracy Threshold
- type: max_f1
value: 0.8307692307692308
name: Max F1
- type: max_f1_threshold
value: 208.00347900390625
name: Max F1 Threshold
- type: max_precision
value: 0.7921760391198044
name: Max Precision
- type: max_recall
value: 0.9407008086253369
name: Max Recall
- type: max_ap
value: 0.8884154240019244
name: Max Ap
- task:
type: paraphrase-mining
name: Paraphrase Mining
dataset:
name: quora duplicates dev
type: quora-duplicates-dev
metrics:
- type: average_precision
value: 0.534436244125929
name: Average Precision
- type: f1
value: 0.5447997274541295
name: F1
- type: precision
value: 0.5311002514589362
name: Precision
- type: recall
value: 0.5592246590398161
name: Recall
- type: threshold
value: 0.8626040816307068
name: Threshold
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.928
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9712
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9782
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9874
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.928
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4151333333333334
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.26656
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.14166
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7993523853760618
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9341884771405065
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9560896250710075
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9766088525134997
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9516150309696244
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9509392857142857
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9390263696194139
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.8926
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.9518
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9658
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9768
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.8926
name: Dot Precision@1
- type: dot_precision@3
value: 0.40273333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.26076
name: Dot Precision@5
- type: dot_precision@10
value: 0.13882
name: Dot Precision@10
- type: dot_recall@1
value: 0.7679620996617761
name: Dot Recall@1
- type: dot_recall@3
value: 0.9105756956997251
name: Dot Recall@3
- type: dot_recall@5
value: 0.9402185219519044
name: Dot Recall@5
- type: dot_recall@10
value: 0.9623418143294613
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9263520741106431
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9243020634920638
name: Dot Mrr@10
- type: dot_map@100
value: 0.9094019438194247
name: Dot Map@100
---
# SentenceTransformer based on sentence-transformers/stsb-distilbert-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) on the [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) dataset. 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/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) <!-- at revision 82ad392c08f81be9be9bf065339670b23f2e1493 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
- **Language:** en
<!-- - **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: DistilBertModel
(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("tomaarsen/stsb-distilbert-base-ocl")
# Run inference
sentences = [
'Is stretching bad?',
'Is stretching good for you?',
'If i=0; what will i=i++ do to i?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(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
#### Binary Classification
* Dataset: `quora-duplicates`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.86 |
| cosine_accuracy_threshold | 0.8104 |
| cosine_f1 | 0.8251 |
| cosine_f1_threshold | 0.7248 |
| cosine_precision | 0.7347 |
| cosine_recall | 0.9407 |
| cosine_ap | 0.8872 |
| dot_accuracy | 0.828 |
| dot_accuracy_threshold | 157.3549 |
| dot_f1 | 0.7899 |
| dot_f1_threshold | 145.7113 |
| dot_precision | 0.7155 |
| dot_recall | 0.8814 |
| dot_ap | 0.8369 |
| manhattan_accuracy | 0.868 |
| manhattan_accuracy_threshold | 208.0035 |
| manhattan_f1 | 0.8308 |
| manhattan_f1_threshold | 208.0035 |
| manhattan_precision | 0.7922 |
| manhattan_recall | 0.8733 |
| manhattan_ap | 0.8868 |
| euclidean_accuracy | 0.867 |
| euclidean_accuracy_threshold | 9.2694 |
| euclidean_f1 | 0.8301 |
| euclidean_f1_threshold | 9.5257 |
| euclidean_precision | 0.7888 |
| euclidean_recall | 0.876 |
| euclidean_ap | 0.8884 |
| max_accuracy | 0.868 |
| max_accuracy_threshold | 208.0035 |
| max_f1 | 0.8308 |
| max_f1_threshold | 208.0035 |
| max_precision | 0.7922 |
| max_recall | 0.9407 |
| **max_ap** | **0.8884** |
#### Paraphrase Mining
* Dataset: `quora-duplicates-dev`
* Evaluated with [<code>ParaphraseMiningEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator)
| Metric | Value |
|:----------------------|:-----------|
| **average_precision** | **0.5344** |
| f1 | 0.5448 |
| precision | 0.5311 |
| recall | 0.5592 |
| threshold | 0.8626 |
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:----------|
| cosine_accuracy@1 | 0.928 |
| cosine_accuracy@3 | 0.9712 |
| cosine_accuracy@5 | 0.9782 |
| cosine_accuracy@10 | 0.9874 |
| cosine_precision@1 | 0.928 |
| cosine_precision@3 | 0.4151 |
| cosine_precision@5 | 0.2666 |
| cosine_precision@10 | 0.1417 |
| cosine_recall@1 | 0.7994 |
| cosine_recall@3 | 0.9342 |
| cosine_recall@5 | 0.9561 |
| cosine_recall@10 | 0.9766 |
| cosine_ndcg@10 | 0.9516 |
| cosine_mrr@10 | 0.9509 |
| **cosine_map@100** | **0.939** |
| dot_accuracy@1 | 0.8926 |
| dot_accuracy@3 | 0.9518 |
| dot_accuracy@5 | 0.9658 |
| dot_accuracy@10 | 0.9768 |
| dot_precision@1 | 0.8926 |
| dot_precision@3 | 0.4027 |
| dot_precision@5 | 0.2608 |
| dot_precision@10 | 0.1388 |
| dot_recall@1 | 0.768 |
| dot_recall@3 | 0.9106 |
| dot_recall@5 | 0.9402 |
| dot_recall@10 | 0.9623 |
| dot_ndcg@10 | 0.9264 |
| dot_mrr@10 | 0.9243 |
| dot_map@100 | 0.9094 |
<!--
## 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
#### sentence-transformers/quora-duplicates
* Dataset: [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 100,000 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.5 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.46 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>0: ~64.10%</li><li>1: ~35.90%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------|
| <code>What are the best ecommerce blogs to do guest posts on about SEO to gain new clients?</code> | <code>Interested in being a guest blogger for an ecommerce marketing blog?</code> | <code>0</code> |
| <code>How do I learn Informatica online training?</code> | <code>What is Informatica online training?</code> | <code>0</code> |
| <code>What effects does marijuana use have on the flu?</code> | <code>What effects does Marijuana use have on the common cold?</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/losses.html#onlinecontrastiveloss)
### Evaluation Dataset
#### sentence-transformers/quora-duplicates
* Dataset: [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 1,000 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.82 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.91 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~62.90%</li><li>1: ~37.10%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:------------------------------------------------------|:---------------------------------------------------|:---------------|
| <code>How should I prepare for JEE Mains 2017?</code> | <code>How do I prepare for the JEE 2016?</code> | <code>0</code> |
| <code>What is the gate exam?</code> | <code>What is the GATE exam in engineering?</code> | <code>0</code> |
| <code>Where do IRS officers get posted?</code> | <code>Does IRS Officers get posted abroad?</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/losses.html#onlinecontrastiveloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `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`: False
- `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
- `learning_rate`: 5e-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
- `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`: None
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | cosine_map@100 | quora-duplicates-dev_average_precision | quora-duplicates_max_ap |
|:------:|:----:|:-------------:|:------:|:--------------:|:--------------------------------------:|:-----------------------:|
| 0 | 0 | - | - | 0.9235 | 0.4200 | 0.7276 |
| 0.0640 | 100 | 2.5123 | - | - | - | - |
| 0.1280 | 200 | 2.0534 | - | - | - | - |
| 0.1599 | 250 | - | 1.7914 | 0.9127 | 0.4082 | 0.8301 |
| 0.1919 | 300 | 1.9505 | - | - | - | - |
| 0.2559 | 400 | 1.9836 | - | - | - | - |
| 0.3199 | 500 | 1.8462 | 1.5923 | 0.9190 | 0.4445 | 0.8688 |
| 0.3839 | 600 | 1.7734 | - | - | - | - |
| 0.4479 | 700 | 1.7918 | - | - | - | - |
| 0.4798 | 750 | - | 1.5461 | 0.9291 | 0.4943 | 0.8707 |
| 0.5118 | 800 | 1.6157 | - | - | - | - |
| 0.5758 | 900 | 1.7244 | - | - | - | - |
| 0.6398 | 1000 | 1.7322 | 1.5294 | 0.9309 | 0.5048 | 0.8808 |
| 0.7038 | 1100 | 1.6825 | - | - | - | - |
| 0.7678 | 1200 | 1.6823 | - | - | - | - |
| 0.7997 | 1250 | - | 1.4812 | 0.9351 | 0.5126 | 0.8865 |
| 0.8317 | 1300 | 1.5707 | - | - | - | - |
| 0.8957 | 1400 | 1.6145 | - | - | - | - |
| 0.9597 | 1500 | 1.5795 | 1.4705 | 0.9390 | 0.5344 | 0.8884 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.040 kWh
- **Carbon Emitted**: 0.016 kg of CO2
- **Hours Used**: 0.202 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: 3.0.0.dev0
- Transformers: 4.41.0.dev0
- PyTorch: 2.3.0+cu121
- Accelerate: 0.26.1
- Datasets: 2.18.0
- 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",
}
```
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