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---
base_model: nomic-ai/nomic-embed-text-v1.5
datasets: []
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:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Chevron aims to support a diverse and inclusive supply chain that
reflects the communities where they operate, believing that a diverse supply chain
contributes to their success and growth.
sentences:
- What was the renewal rate for Costco memberships in the U.S. and Canada at the
end of 2023?
- What is Chevron's approach towards maintaining a diverse and inclusive supply
chain?
- What percentage growth did LinkedIn revenue experience?
- source_sentence: Visa Direct is part of Visa’s strategy beyond C2B payments and
helps facilitate the delivery of funds to eligible cards, deposit accounts and
digital wallets across more than 190 countries and territories. Visa Direct supports
multiple use cases, such as P2P payments and account-to-account transfers, business
and government payouts to individuals or small businesses, merchant settlements
and refunds.
sentences:
- What type of situations will the company record a liability for legal proceedings?
- What is the purpose of Visa Direct?
- What benefits does Airbnb's AirCover for guests offer?
- source_sentence: As of December 31, 2023, we had $267 million of total unrecognized
compensation cost related to nonvested stock-based compensation awards granted
under our plans.
sentences:
- How much total unrecognized compensation cost related to nonvested stock-based
compensation awards was reported as of December 31, 2023?
- What changes are planned for the company's reporting metrics starting in fiscal
year 202es and how does this affect the treatment of paused subscriptions?
- How much does HP expect to pay for benefit claims for its post-retirement benefit
plans in fiscal year 2024?
- source_sentence: Discrete tax items resulted in a (benefit) provision for income
taxes of $(18.1) million and $(11.9) million for the years ended December 31,
2023 and 2022, respectively.
sentences:
- What was the total cost of TNT Express's business realignment through 2023?
- What is the purpose of adding research and development expenses and general and
administrative expenses to the loss from operations when calculating the contribution
margin?
- What impact did discrete tax items have on the tax provision in 2023 compared
to 2022?
- source_sentence: 'The company may issue debt or equity securities occasionally to
provide additional liquidity or pursue opportunities to enhance its long-term
competitive position while maintaining a strong balance sheet. '
sentences:
- What might the company do to increase liquidity or pursue long-term competitive
advantages while managing a strong balance sheet?
- What types of technologies does the Mortgage Technology segment employ to enhance
operational efficiency?
- Which section of a financial document covers Financial Statements and Supplementary
Data?
model-index:
- name: Nomic Embed 1.5 Financial Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.6928571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8228571428571428
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.87
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9071428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6928571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2742857142857143
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.174
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0907142857142857
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6928571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8228571428571428
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.87
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9071428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8029973671837228
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7692715419501133
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7724352164684344
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.6914285714285714
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8271428571428572
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.87
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9085714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6914285714285714
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2757142857142857
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.174
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09085714285714284
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6914285714285714
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8271428571428572
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.87
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9085714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8029523922190992
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7687732426303853
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7717841390041892
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.6871428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8285714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8728571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8985714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6871428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27619047619047615
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17457142857142854
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08985714285714284
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6871428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8285714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8728571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8985714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7983704009707536
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7655901360544215
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7693376855880492
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6671428571428571
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8185714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8557142857142858
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8957142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6671428571428571
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27285714285714285
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17114285714285712
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08957142857142855
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6671428571428571
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8185714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8557142857142858
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8957142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7849638501826605
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7491031746031743
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.752516331310788
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6528571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7871428571428571
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8271428571428572
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8771428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6528571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2623809523809524
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1654285714285714
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0877142857142857
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6528571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7871428571428571
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8271428571428572
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8771428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7639694587103518
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7279750566893419
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7317631790989764
name: Cosine Map@100
---
# Nomic Embed 1.5 Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5). 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:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) <!-- at revision b0753ae76394dd36bcfb912a46018088bca48be0 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **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: NomicBertModel
(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("venkateshmurugadas/nomic-v1.5-financial-matryoshka")
# Run inference
sentences = [
'The company may issue debt or equity securities occasionally to provide additional liquidity or pursue opportunities to enhance its long-term competitive position while maintaining a strong balance sheet. ',
'What might the company do to increase liquidity or pursue long-term competitive advantages while managing a strong balance sheet?',
'What types of technologies does the Mortgage Technology segment employ to enhance operational efficiency?',
]
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 | 0.6929 |
| cosine_accuracy@3 | 0.8229 |
| cosine_accuracy@5 | 0.87 |
| cosine_accuracy@10 | 0.9071 |
| cosine_precision@1 | 0.6929 |
| cosine_precision@3 | 0.2743 |
| cosine_precision@5 | 0.174 |
| cosine_precision@10 | 0.0907 |
| cosine_recall@1 | 0.6929 |
| cosine_recall@3 | 0.8229 |
| cosine_recall@5 | 0.87 |
| cosine_recall@10 | 0.9071 |
| cosine_ndcg@10 | 0.803 |
| cosine_mrr@10 | 0.7693 |
| **cosine_map@100** | **0.7724** |
#### 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 | 0.6914 |
| cosine_accuracy@3 | 0.8271 |
| cosine_accuracy@5 | 0.87 |
| cosine_accuracy@10 | 0.9086 |
| cosine_precision@1 | 0.6914 |
| cosine_precision@3 | 0.2757 |
| cosine_precision@5 | 0.174 |
| cosine_precision@10 | 0.0909 |
| cosine_recall@1 | 0.6914 |
| cosine_recall@3 | 0.8271 |
| cosine_recall@5 | 0.87 |
| cosine_recall@10 | 0.9086 |
| cosine_ndcg@10 | 0.803 |
| cosine_mrr@10 | 0.7688 |
| **cosine_map@100** | **0.7718** |
#### 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 | 0.6871 |
| cosine_accuracy@3 | 0.8286 |
| cosine_accuracy@5 | 0.8729 |
| cosine_accuracy@10 | 0.8986 |
| cosine_precision@1 | 0.6871 |
| cosine_precision@3 | 0.2762 |
| cosine_precision@5 | 0.1746 |
| cosine_precision@10 | 0.0899 |
| cosine_recall@1 | 0.6871 |
| cosine_recall@3 | 0.8286 |
| cosine_recall@5 | 0.8729 |
| cosine_recall@10 | 0.8986 |
| cosine_ndcg@10 | 0.7984 |
| cosine_mrr@10 | 0.7656 |
| **cosine_map@100** | **0.7693** |
#### 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 | 0.6671 |
| cosine_accuracy@3 | 0.8186 |
| cosine_accuracy@5 | 0.8557 |
| cosine_accuracy@10 | 0.8957 |
| cosine_precision@1 | 0.6671 |
| cosine_precision@3 | 0.2729 |
| cosine_precision@5 | 0.1711 |
| cosine_precision@10 | 0.0896 |
| cosine_recall@1 | 0.6671 |
| cosine_recall@3 | 0.8186 |
| cosine_recall@5 | 0.8557 |
| cosine_recall@10 | 0.8957 |
| cosine_ndcg@10 | 0.785 |
| cosine_mrr@10 | 0.7491 |
| **cosine_map@100** | **0.7525** |
#### 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 | 0.6529 |
| cosine_accuracy@3 | 0.7871 |
| cosine_accuracy@5 | 0.8271 |
| cosine_accuracy@10 | 0.8771 |
| cosine_precision@1 | 0.6529 |
| cosine_precision@3 | 0.2624 |
| cosine_precision@5 | 0.1654 |
| cosine_precision@10 | 0.0877 |
| cosine_recall@1 | 0.6529 |
| cosine_recall@3 | 0.7871 |
| cosine_recall@5 | 0.8271 |
| cosine_recall@10 | 0.8771 |
| cosine_ndcg@10 | 0.764 |
| cosine_mrr@10 | 0.728 |
| **cosine_map@100** | **0.7318** |
<!--
## 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: 6,300 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: 8 tokens</li><li>mean: 46.46 tokens</li><li>max: 371 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.45 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|
| <code>We evaluate uncertain tax positions periodically, considering changes in facts and circumstances, such as new regulations or recent judicial opinions, as well as the status of audit activities by taxing authorities.</code> | <code>How are changes to a company's uncertain tax positions evaluated?</code> |
| <code>During 2022 and 2023, our operating margin was impacted by increased wage rates. During 2022, our gross margin was impacted by higher air freight costs as a result of global supply chain disruption.</code> | <code>What effects did inflation have on the company's operating results during 2022 and 2023?</code> |
| <code>To mitigate these developments, we are continually working to evolve our advertising systems to improve the performance of our ad products. We are developing privacy enhancing technologies to deliver relevant ads and measurement capabilities while reducing the amount of personal information we process, including by relying more on anonymized or aggregated third-party data. In addition, we are developing tools that enable marketers to share their data into our systems, as well as ad products that generate more valuable signals within our apps. More broadly, we also continue to innovate our advertising tools to help marketers prepare campaigns and connect with consumers, including developing growing formats such as Reels ads and our business messaging ad products. Across all of these efforts, we are making significant investments in artificial intelligence (AI), including generative AI, to improve our delivery, targeting, and measurement capabilities. Further, we are focused on driving onsite conversions in our business messaging ad products by developing new features and scaling existing features.</code> | <code>What technological solutions is the company developing to improve ad delivery?</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`: 4
- `per_device_eval_batch_size`: 4
- `gradient_accumulation_steps`: 64
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `fp16`: True
- `tf32`: False
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `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`: 4
- `per_device_eval_batch_size`: 4
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 64
- `eval_accumulation_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`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: False
- `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_fused
- `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
- `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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.4063 | 10 | 0.1329 | - | - | - | - | - |
| 0.8127 | 20 | 0.0567 | - | - | - | - | - |
| 0.9752 | 24 | - | 0.7416 | 0.7604 | 0.7678 | 0.7249 | 0.7758 |
| 1.2190 | 30 | 0.0415 | - | - | - | - | - |
| 1.6254 | 40 | 0.0043 | - | - | - | - | - |
| 1.9911 | 49 | - | 0.7491 | 0.7648 | 0.7700 | 0.7315 | 0.7731 |
| 2.0317 | 50 | 0.0059 | - | - | - | - | - |
| 2.4381 | 60 | 0.0045 | - | - | - | - | - |
| 2.8444 | 70 | 0.0013 | - | - | - | - | - |
| **2.9663** | **73** | **-** | **0.7531** | **0.7703** | **0.7712** | **0.7327** | **0.7738** |
| 3.2508 | 80 | 0.0031 | - | - | - | - | - |
| 3.6571 | 90 | 0.0009 | - | - | - | - | - |
| 3.9010 | 96 | - | 0.7525 | 0.7693 | 0.7718 | 0.7318 | 0.7724 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- 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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