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---
base_model: BAAI/bge-base-en-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: A change in key assumptions such as the discount rate or projected
future revenues, expenses and cash flows could materially affect the determination
of fair values.
sentences:
- How many shares of common stock were sold in fiscal 2021 under GameStop Corp.'s
at-the-market equity offering programs?
- How does a change in key assumptions potentially affect the determination of fair
values of assets?
- What is the primary revenue source for Comcast's Theme Parks segment?
- source_sentence: In January 2023, we announced our intention to implement a cost
reduction program to reduce automotive fixed costs by $2.0 billion on an annual
run rate basis by the end of 2024. This goal includes the impact of higher expected
depreciation and amortization expense and inflationary cost increases on fixed
cost but excludes changes in our pension income. In addition to people costs,
we are reducing our marketing and advertising expenses, streamlining our engineering
expense by reducing complexity across the vehicle portfolio, adjusting the cadivers-SafieiaıcıUrbanıcık,
prioritizing growth initiatives, and reducing our overall overhead and discretionary
costs.
sentences:
- What method does AbbVie primarily use to record investments in equity securities
with readily determinable fair values?
- What measures is General Motors taking to reduce costs and streamline operations?
- As of December 31, 2023, what is the total balance of acquisitions, foreign currency
translation and other adjustments?
- source_sentence: AutoZone utilizes a computerized proprietary Point-of-Sale System
including bar code scanning and terminals to enhance customer service by efficiently
processing transactions and assisting in administrative tasks.
sentences:
- How does AutoZone's Point-of-Sale System enhance customer service?
- What unique feature did fiscal year 2021 have compared to 2023 and 2022?
- What was the primary source of the increase in premiums written by Berkshire Hathaway's
Property/Casualty reinsurance in 2023?
- source_sentence: In 2023, capital expenditures for aircraft and related equipment
by FedEx Express saw a decrease of 26% compared to 2022.
sentences:
- What was the increase in earnings from operations for Optum from 2022 to 2023?
- What did the FCA require regarding the continued publication of certain LIBOR
settings after 2021?
- What was the percentage decrease in FedEx's aircraft and related equipment capital
expenditures in 2023 compared to 2022?
- source_sentence: In 1983, Walmart opened its first Sam's Club, and in 1988, it opened
its first supercenter.
sentences:
- When did Walmart open its first Sam's Club and supercenter?
- Which standards and guidelines does the company use for informing its sustainability
disclosures?
- What accounting treatment does the Company apply to refunds issued to customers?
model-index:
- name: BGE base Financial Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.7028571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8371428571428572
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8728571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9185714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7028571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27904761904761904
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17457142857142854
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09185714285714283
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7028571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8371428571428572
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8728571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9185714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.81196519287814
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7777465986394556
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7809887604595412
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.6985714285714286
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8328571428571429
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8642857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9242857142857143
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6985714285714286
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2776190476190476
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17285714285714285
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09242857142857142
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6985714285714286
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8328571428571429
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8642857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9242857142857143
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8104528945408784
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7743191609977326
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7771143041520369
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.6942857142857143
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8271428571428572
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8585714285714285
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9085714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6942857142857143
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2757142857142857
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1717142857142857
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09085714285714284
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6942857142857143
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8271428571428572
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8585714285714285
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9085714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8026074561436641
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7686825396825395
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7726124326414546
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6885714285714286
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8157142857142857
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8571428571428571
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9071428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6885714285714286
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27190476190476187
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1714285714285714
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09071428571428569
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6885714285714286
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8157142857142857
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8571428571428571
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9071428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7972617985734928
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7622108843537415
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.765720886169324
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.66
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7985714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8357142857142857
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8828571428571429
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.66
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2661904761904762
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1671428571428571
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08828571428571427
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.66
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7985714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8357142857142857
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8828571428571429
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7715751288332002
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7360753968253966
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7400601081956545
name: Cosine Map@100
---
# BGE base Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-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:** [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': True}) with Transformer model: BertModel
(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})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("anishareddyalla/bge-base-financial-matryoshka-anisha")
# Run inference
sentences = [
"In 1983, Walmart opened its first Sam's Club, and in 1988, it opened its first supercenter.",
"When did Walmart open its first Sam's Club and supercenter?",
'Which standards and guidelines does the company use for informing its sustainability disclosures?',
]
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 | 0.7029 |
| cosine_accuracy@3 | 0.8371 |
| cosine_accuracy@5 | 0.8729 |
| cosine_accuracy@10 | 0.9186 |
| cosine_precision@1 | 0.7029 |
| cosine_precision@3 | 0.279 |
| cosine_precision@5 | 0.1746 |
| cosine_precision@10 | 0.0919 |
| cosine_recall@1 | 0.7029 |
| cosine_recall@3 | 0.8371 |
| cosine_recall@5 | 0.8729 |
| cosine_recall@10 | 0.9186 |
| cosine_ndcg@10 | 0.812 |
| cosine_mrr@10 | 0.7777 |
| **cosine_map@100** | **0.781** |
#### 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 | 0.6986 |
| cosine_accuracy@3 | 0.8329 |
| cosine_accuracy@5 | 0.8643 |
| cosine_accuracy@10 | 0.9243 |
| cosine_precision@1 | 0.6986 |
| cosine_precision@3 | 0.2776 |
| cosine_precision@5 | 0.1729 |
| cosine_precision@10 | 0.0924 |
| cosine_recall@1 | 0.6986 |
| cosine_recall@3 | 0.8329 |
| cosine_recall@5 | 0.8643 |
| cosine_recall@10 | 0.9243 |
| cosine_ndcg@10 | 0.8105 |
| cosine_mrr@10 | 0.7743 |
| **cosine_map@100** | **0.7771** |
#### 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 | 0.6943 |
| cosine_accuracy@3 | 0.8271 |
| cosine_accuracy@5 | 0.8586 |
| cosine_accuracy@10 | 0.9086 |
| cosine_precision@1 | 0.6943 |
| cosine_precision@3 | 0.2757 |
| cosine_precision@5 | 0.1717 |
| cosine_precision@10 | 0.0909 |
| cosine_recall@1 | 0.6943 |
| cosine_recall@3 | 0.8271 |
| cosine_recall@5 | 0.8586 |
| cosine_recall@10 | 0.9086 |
| cosine_ndcg@10 | 0.8026 |
| cosine_mrr@10 | 0.7687 |
| **cosine_map@100** | **0.7726** |
#### 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 | 0.6886 |
| cosine_accuracy@3 | 0.8157 |
| cosine_accuracy@5 | 0.8571 |
| cosine_accuracy@10 | 0.9071 |
| cosine_precision@1 | 0.6886 |
| cosine_precision@3 | 0.2719 |
| cosine_precision@5 | 0.1714 |
| cosine_precision@10 | 0.0907 |
| cosine_recall@1 | 0.6886 |
| cosine_recall@3 | 0.8157 |
| cosine_recall@5 | 0.8571 |
| cosine_recall@10 | 0.9071 |
| cosine_ndcg@10 | 0.7973 |
| cosine_mrr@10 | 0.7622 |
| **cosine_map@100** | **0.7657** |
#### 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 | 0.66 |
| cosine_accuracy@3 | 0.7986 |
| cosine_accuracy@5 | 0.8357 |
| cosine_accuracy@10 | 0.8829 |
| cosine_precision@1 | 0.66 |
| cosine_precision@3 | 0.2662 |
| cosine_precision@5 | 0.1671 |
| cosine_precision@10 | 0.0883 |
| cosine_recall@1 | 0.66 |
| cosine_recall@3 | 0.7986 |
| cosine_recall@5 | 0.8357 |
| cosine_recall@10 | 0.8829 |
| cosine_ndcg@10 | 0.7716 |
| cosine_mrr@10 | 0.7361 |
| **cosine_map@100** | **0.7401** |
<!--
## 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: 46.43 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.76 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The Company’s human capital management strategy is built on three fundamental focus areas: Attracting and recruiting the best talent, Developing and retaining talent, Empowering and inspiring talent.</code> | <code>What strategies are outlined in the Company's human capital management?</code> |
| <code>Opinion on the Consolidated Financial Statements We have audited the accompanying consolidated balance sheets of Costco Wholesale Corporation and subsidiaries (the Company) as of September 3, 2023, and August 28, 2022, the related consolidated statements of income, comprehensive income, equity, and cash flows for the 53-week period ended September 3, 2023, and the 52-week periods ended August 28, 2022, and August 29, 2021, and the related notes (collectively, the consolidated financial statements). In our opinion, the consolidated financial statements present fairly, in all material respects, the financial position of the Company as of September 3, 2023, and August 28, 2022, and the results of its operations and its cash flows for each of the 53-week period ended September 3, 2023, and the 52-week periods ended August 28, 2022, and August 29, 2021, in conformity with U.S. generally accepted accounting principles.</code> | <code>What was the opinion of the independent registered public accounting firm on Costco Wholesale Corporation's consolidated financial statements for the year ended September 3, 2023?</code> |
| <code>Nonperforming loans and leases are generally those that have been placed on nonaccrual status, such as when they are 90 days past due or have confirmed cases of fraud or bankruptcy. Additionally, specific types of loans like consumer real estate-secured loans are classified as nonperforming at 90 days past due unless they are fully insured, and commercial loans and leases are classified as nonperforming when past due 90 days or more unless well-secured and in the process of collection.</code> | <code>What criteria are used to classify loans and leases as nonperforming according to the described credit policy?</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`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `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`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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
- `eval_on_start`: 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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8122 | 10 | 1.5488 | - | - | - | - | - |
| 0.9746 | 12 | - | 0.7540 | 0.7565 | 0.7660 | 0.7176 | 0.7693 |
| 1.6244 | 20 | 0.674 | - | - | - | - | - |
| 1.9492 | 24 | - | 0.7622 | 0.7715 | 0.7781 | 0.7352 | 0.7790 |
| 2.4365 | 30 | 0.4592 | - | - | - | - | - |
| **2.9239** | **36** | **-** | **0.7648** | **0.7729** | **0.7778** | **0.7384** | **0.7799** |
| 3.2487 | 40 | 0.4113 | - | - | - | - | - |
| 3.8985 | 48 | - | 0.7657 | 0.7726 | 0.7771 | 0.7401 | 0.7810 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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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