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---
base_model: Alibaba-NLP/gte-base-en-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:32833
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Anonymity in online interactions can lead to a disinhibition effect,
where individuals feel free to express hostile or aggressive opinions they might
otherwise suppress.
sentences:
- What are the implications of anonymity in online interactions?
- How does creativity function as a form of costly signalling in personal expressions
such as invitations?
- Why is conflict considered essential in a creative organization?
- source_sentence: The author decides to release their novel into the world despite
its imperfections, and finds that this allows them to move on to new projects
and experiences, and to focus on the value of the work itself rather than its
flaws.
sentences:
- How does the author's experience with their novel illustrate the concept of 'embracing
imperfection' in creative work?
- What does the author mean by 'ambitious programmers are better off doing their
own thing'?
- What is the role of 'show me' in the design process?
- source_sentence: Tokens become more valuable as more users adopt them, creating
a positive feedback loop that enhances their utility and encourages further adoption
across various applications.
sentences:
- In what ways do tokens exhibit network effects?
- What can sometimes be found when considering a startup with a lame-sounding idea?
- How do social norms influence decision-making in the context of airport choices?
- source_sentence: Philosophers are often viewed as the guardians of critical thinking;
however, their reliance on bureaucratic structures and abstract discussions can
become problematic. Instead of fostering open-mindedness, they may perpetuate
dogmatic thinking and limit the exploration of diverse perspectives, thereby failing
to fulfill their duty of promoting genuine critical engagement.
sentences:
- In what ways can the role of philosophers be seen as essential or problematic
within the context of critical thinking?
- How does the evolution of pair-bonding facilitate cultural exchange between groups?
- What is the role of autonomy in the success of acquired startups?
- source_sentence: Society tends to admire those who despair when others hope, viewing
them as sages or wise figures.
sentences:
- What is often the societal perception of those who express pessimism about the
future?
- How did the realization about user engagement influence the app development strategy?
- What lessons can be learned from the historical context of employee relations
in large corporations?
model-index:
- name: Alchemy Embedding - Anudit Nagar
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.782012613106663
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8889498217713189
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9248697559638058
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9520153550863724
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.782012613106663
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.29631660725710623
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1849739511927612
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09520153550863725
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.782012613106663
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8889498217713189
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9248697559638058
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9520153550863724
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.867555587052628
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8402608580220322
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8422322227138224
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.780367425281053
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8848368522072937
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9221277762544557
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9514669591445023
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.780367425281053
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2949456174024312
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1844255552508912
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09514669591445023
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.780367425281053
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8848368522072937
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9221277762544557
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9514669591445023
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8661558392165704
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.838656038231032
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8405372438205077
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.7754318618042226
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8804496846723334
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9169180148066904
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9468055936386071
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7754318618042226
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2934832282241111
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18338360296133807
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09468055936386072
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7754318618042226
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8804496846723334
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9169180148066904
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9468055936386071
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8613819477350178
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8338379881703168
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8360735900013385
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.7617219632574719
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.871675349602413
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9117082533589251
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9418700301617768
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7617219632574719
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2905584498674709
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18234165067178504
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09418700301617768
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7617219632574719
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.871675349602413
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9117082533589251
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9418700301617768
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.851649908463093
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8225671458602635
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8248455884524328
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.7408829174664108
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.853852481491637
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8936111872772141
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9292569234987661
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7408829174664108
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28461749383054563
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17872223745544283
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0929256923498766
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7408829174664108
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.853852481491637
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8936111872772141
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9292569234987661
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8338956659320366
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8033378162525404
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8057702637208689
name: Cosine Map@100
---
# Alchemy Embedding - Anudit Nagar
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) on the json 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:** [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) <!-- at revision a8e4f3e0ee719c75bc30d12b8eae0f8440502718 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
- **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({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("sentence_transformers_model_id")
# Run inference
sentences = [
'Society tends to admire those who despair when others hope, viewing them as sages or wise figures.',
'What is often the societal perception of those who express pessimism about the future?',
'How did the realization about user engagement influence the app development strategy?',
]
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
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.782 |
| cosine_accuracy@3 | 0.8889 |
| cosine_accuracy@5 | 0.9249 |
| cosine_accuracy@10 | 0.952 |
| cosine_precision@1 | 0.782 |
| cosine_precision@3 | 0.2963 |
| cosine_precision@5 | 0.185 |
| cosine_precision@10 | 0.0952 |
| cosine_recall@1 | 0.782 |
| cosine_recall@3 | 0.8889 |
| cosine_recall@5 | 0.9249 |
| cosine_recall@10 | 0.952 |
| cosine_ndcg@10 | 0.8676 |
| cosine_mrr@10 | 0.8403 |
| **cosine_map@100** | **0.8422** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7804 |
| cosine_accuracy@3 | 0.8848 |
| cosine_accuracy@5 | 0.9221 |
| cosine_accuracy@10 | 0.9515 |
| cosine_precision@1 | 0.7804 |
| cosine_precision@3 | 0.2949 |
| cosine_precision@5 | 0.1844 |
| cosine_precision@10 | 0.0951 |
| cosine_recall@1 | 0.7804 |
| cosine_recall@3 | 0.8848 |
| cosine_recall@5 | 0.9221 |
| cosine_recall@10 | 0.9515 |
| cosine_ndcg@10 | 0.8662 |
| cosine_mrr@10 | 0.8387 |
| **cosine_map@100** | **0.8405** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7754 |
| cosine_accuracy@3 | 0.8804 |
| cosine_accuracy@5 | 0.9169 |
| cosine_accuracy@10 | 0.9468 |
| cosine_precision@1 | 0.7754 |
| cosine_precision@3 | 0.2935 |
| cosine_precision@5 | 0.1834 |
| cosine_precision@10 | 0.0947 |
| cosine_recall@1 | 0.7754 |
| cosine_recall@3 | 0.8804 |
| cosine_recall@5 | 0.9169 |
| cosine_recall@10 | 0.9468 |
| cosine_ndcg@10 | 0.8614 |
| cosine_mrr@10 | 0.8338 |
| **cosine_map@100** | **0.8361** |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7617 |
| cosine_accuracy@3 | 0.8717 |
| cosine_accuracy@5 | 0.9117 |
| cosine_accuracy@10 | 0.9419 |
| cosine_precision@1 | 0.7617 |
| cosine_precision@3 | 0.2906 |
| cosine_precision@5 | 0.1823 |
| cosine_precision@10 | 0.0942 |
| cosine_recall@1 | 0.7617 |
| cosine_recall@3 | 0.8717 |
| cosine_recall@5 | 0.9117 |
| cosine_recall@10 | 0.9419 |
| cosine_ndcg@10 | 0.8516 |
| cosine_mrr@10 | 0.8226 |
| **cosine_map@100** | **0.8248** |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7409 |
| cosine_accuracy@3 | 0.8539 |
| cosine_accuracy@5 | 0.8936 |
| cosine_accuracy@10 | 0.9293 |
| cosine_precision@1 | 0.7409 |
| cosine_precision@3 | 0.2846 |
| cosine_precision@5 | 0.1787 |
| cosine_precision@10 | 0.0929 |
| cosine_recall@1 | 0.7409 |
| cosine_recall@3 | 0.8539 |
| cosine_recall@5 | 0.8936 |
| cosine_recall@10 | 0.9293 |
| cosine_ndcg@10 | 0.8339 |
| cosine_mrr@10 | 0.8033 |
| **cosine_map@100** | **0.8058** |
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## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 32,833 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 34.54 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 16.78 tokens</li><li>max: 77 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------|
| <code>The author saw taking risks as a necessary part of the creative process, and was willing to take risks in order to explore new ideas and themes.</code> | <code>What was the author's perspective on the importance of taking risks in creative work?</code> |
| <code>Recognizing that older users are less likely to invite new users led to a strategic focus on younger demographics, prompting a shift in development efforts toward creating products that resonate with teens.</code> | <code>How did the realization about user engagement influence the app development strategy?</code> |
| <code>The phrase emphasizes the fragility of Earth and our collective responsibility to protect it and ensure sustainable resource management for future generations.</code> | <code>What is the significance of the phrase 'pale blue dot' in relation to environmental responsibility?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 24
- `per_device_eval_batch_size`: 24
- `gradient_accumulation_steps`: 8
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 24
- `per_device_eval_batch_size`: 24
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: True
- `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`: 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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.0584 | 10 | 0.8567 | - | - | - | - | - |
| 0.1169 | 20 | 0.6549 | - | - | - | - | - |
| 0.1753 | 30 | 0.5407 | - | - | - | - | - |
| 0.2337 | 40 | 0.4586 | - | - | - | - | - |
| 0.2922 | 50 | 0.3914 | - | - | - | - | - |
| 0.3506 | 60 | 0.4104 | - | - | - | - | - |
| 0.4091 | 70 | 0.299 | - | - | - | - | - |
| 0.4675 | 80 | 0.2444 | - | - | - | - | - |
| 0.5259 | 90 | 0.2367 | - | - | - | - | - |
| 0.5844 | 100 | 0.2302 | - | - | - | - | - |
| 0.6428 | 110 | 0.2356 | - | - | - | - | - |
| 0.7012 | 120 | 0.1537 | - | - | - | - | - |
| 0.7597 | 130 | 0.2043 | - | - | - | - | - |
| 0.8181 | 140 | 0.1606 | - | - | - | - | - |
| 0.8766 | 150 | 0.1896 | - | - | - | - | - |
| 0.9350 | 160 | 0.1766 | - | - | - | - | - |
| 0.9934 | 170 | 0.1259 | - | - | - | - | - |
| 0.9993 | 171 | - | 0.8115 | 0.8233 | 0.8321 | 0.7829 | 0.8340 |
| 1.0519 | 180 | 0.1661 | - | - | - | - | - |
| 1.1103 | 190 | 0.1632 | - | - | - | - | - |
| 1.1687 | 200 | 0.1032 | - | - | - | - | - |
| 1.2272 | 210 | 0.1037 | - | - | - | - | - |
| 1.2856 | 220 | 0.0708 | - | - | - | - | - |
| 1.3440 | 230 | 0.0827 | - | - | - | - | - |
| 1.4025 | 240 | 0.0505 | - | - | - | - | - |
| 1.4609 | 250 | 0.0468 | - | - | - | - | - |
| 1.5194 | 260 | 0.0371 | - | - | - | - | - |
| 1.5778 | 270 | 0.049 | - | - | - | - | - |
| 1.6362 | 280 | 0.0527 | - | - | - | - | - |
| 1.6947 | 290 | 0.0316 | - | - | - | - | - |
| 1.7531 | 300 | 0.052 | - | - | - | - | - |
| 1.8115 | 310 | 0.0298 | - | - | - | - | - |
| 1.8700 | 320 | 0.0334 | - | - | - | - | - |
| 1.9284 | 330 | 0.0431 | - | - | - | - | - |
| 1.9869 | 340 | 0.0316 | - | - | - | - | - |
| 1.9985 | 342 | - | 0.8216 | 0.8342 | 0.8397 | 0.8006 | 0.8408 |
| 2.0453 | 350 | 0.0275 | - | - | - | - | - |
| 2.1037 | 360 | 0.0461 | - | - | - | - | - |
| 2.1622 | 370 | 0.0341 | - | - | - | - | - |
| 2.2206 | 380 | 0.0323 | - | - | - | - | - |
| 2.2790 | 390 | 0.0205 | - | - | - | - | - |
| 2.3375 | 400 | 0.0223 | - | - | - | - | - |
| 2.3959 | 410 | 0.0189 | - | - | - | - | - |
| 2.4543 | 420 | 0.0181 | - | - | - | - | - |
| 2.5128 | 430 | 0.0144 | - | - | - | - | - |
| 2.5712 | 440 | 0.0179 | - | - | - | - | - |
| 2.6297 | 450 | 0.0217 | - | - | - | - | - |
| 2.6881 | 460 | 0.016 | - | - | - | - | - |
| 2.7465 | 470 | 0.0143 | - | - | - | - | - |
| 2.8050 | 480 | 0.0193 | - | - | - | - | - |
| 2.8634 | 490 | 0.0183 | - | - | - | - | - |
| 2.9218 | 500 | 0.0171 | - | - | - | - | - |
| 2.9803 | 510 | 0.0195 | - | - | - | - | - |
| 2.9978 | 513 | - | 0.8242 | 0.8350 | 0.8409 | 0.8051 | 0.8413 |
| 3.0387 | 520 | 0.0127 | - | - | - | - | - |
| 3.0972 | 530 | 0.0261 | - | - | - | - | - |
| 3.1556 | 540 | 0.017 | - | - | - | - | - |
| 3.2140 | 550 | 0.0198 | - | - | - | - | - |
| 3.2725 | 560 | 0.0131 | - | - | - | - | - |
| 3.3309 | 570 | 0.0156 | - | - | - | - | - |
| 3.3893 | 580 | 0.0107 | - | - | - | - | - |
| 3.4478 | 590 | 0.0123 | - | - | - | - | - |
| 3.5062 | 600 | 0.0111 | - | - | - | - | - |
| 3.5646 | 610 | 0.0112 | - | - | - | - | - |
| 3.6231 | 620 | 0.0143 | - | - | - | - | - |
| 3.6815 | 630 | 0.013 | - | - | - | - | - |
| 3.7400 | 640 | 0.0105 | - | - | - | - | - |
| 3.7984 | 650 | 0.0126 | - | - | - | - | - |
| 3.8568 | 660 | 0.0118 | - | - | - | - | - |
| 3.9153 | 670 | 0.0163 | - | - | - | - | - |
| 3.9737 | 680 | 0.0187 | - | - | - | - | - |
| **3.9971** | **684** | **-** | **0.8248** | **0.8361** | **0.8405** | **0.8058** | **0.8422** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.5
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1
- Accelerate: 0.33.0
- Datasets: 2.21.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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