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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: Item 8 in IBM's 2023 Annual Report to Stockholders details the
Financial Statements and Supplementary Data, which are included on pages 44 through
121.
sentences:
- What was the amount gained from the disposal of assets in 2022?
- What section of IBM's Annual Report for 2023 contains the Financial Statements
and Supplementary Data?
- What were the cash outflows for capital expenditures in 2023 and 2022 respectively?
- source_sentence: For the fiscal year ended March 31, 2023, Electronic Arts reported
a gross margin of 75.9 percent, an increase of 2.5 percentage points from the
previous year.
sentences:
- How did investment banking revenues at Goldman Sachs change in 2023 compared to
2022, and what factors contributed to this change?
- What was the gross margin percentage for Electronic Arts in the fiscal year ending
March 31, 2023?
- What were the risk-free interest rates for the fiscal years 2021, 2022, and 2023?
- source_sentence: Cash, cash equivalents, and restricted cash at the beginning of
the period totaled $7,013 for a company.
sentences:
- What was the amount of cash, cash equivalents, and restricted cash at the beginning
of the period for the company?
- What is the impact of the new $1.25 price point on Dollar Tree’s sales units and
profitability?
- What was the total amount attributed to Goodwill in the acquisition of Nuance
Communications, Inc. as reported by the company?
- source_sentence: generate our mall revenue primarily from leases with tenants through
base minimum rents, overage rents and reimbursements for common area maintenance
(CAM) and other expenditures.
sentences:
- How does Visa facilitate financial inclusion with their prepaid cards?
- What are the main objectives of the economic sanctions imposed by the United States
and other international bodies?
- What revenue sources does Shoppes at Venetian primarily rely on from its tenants?
- source_sentence: For the fiscal year ended August 26, 2023, we reported net sales
of $17.5 billion compared with $16.3 billion for the year ended August 27, 2022,
a 7.4% increase from fiscal 2022. This growth was driven primarily by a domestic
same store sales increase of 3.4% and net sales of $327.8 million from new domestic
and international stores.
sentences:
- What drove the 7.4% increase in AutoZone's net sales for fiscal 2023 compared
to fiscal 2022?
- What percentage of HP's external U.S. hires in fiscal year 2023 were racially
or ethnically diverse?
- How much did GameStop Corp's valuation allowances increase during fiscal 2022?
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: 0.6985714285714286
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8271428571428572
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8628571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8985714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6985714285714286
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2757142857142857
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17257142857142854
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08985714285714284
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6985714285714286
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8271428571428572
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8628571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8985714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8023663256793517
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7712675736961451
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7758522351159084
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.69
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8271428571428572
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.86
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9028571428571428
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.69
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2757142857142857
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17199999999999996
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09028571428571427
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.69
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8271428571428572
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.86
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9028571428571428
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7998655910794988
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7665912698412698
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7706925401671437
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.6957142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8228571428571428
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.86
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8914285714285715
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6957142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2742857142857143
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17199999999999996
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08914285714285713
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6957142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8228571428571428
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.86
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8914285714285715
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7974564108711016
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7669535147392289
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7718155211819018
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6871428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8128571428571428
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8457142857142858
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8857142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6871428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27095238095238094
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16914285714285712
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08857142857142856
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6871428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8128571428571428
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8457142857142858
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8857142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.787697533881839
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.756192743764172
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7610331995977764
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6328571428571429
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7771428571428571
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8171428571428572
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8571428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6328571428571429
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.259047619047619
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16342857142857142
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08571428571428569
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6328571428571429
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7771428571428571
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8171428571428572
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8571428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7482728321357093
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7131224489795914
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7189753431460272
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("NickyNicky/bge-base-financial-matryoshka")
# Run inference
sentences = [
'For the fiscal year ended August 26, 2023, we reported net sales of $17.5 billion compared with $16.3 billion for the year ended August 27, 2022, a 7.4% increase from fiscal 2022. This growth was driven primarily by a domestic same store sales increase of 3.4% and net sales of $327.8 million from new domestic and international stores.',
"What drove the 7.4% increase in AutoZone's net sales for fiscal 2023 compared to fiscal 2022?",
"What percentage of HP's external U.S. hires in fiscal year 2023 were racially or ethnically diverse?",
]
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 | 0.6986 |
| cosine_accuracy@3 | 0.8271 |
| cosine_accuracy@5 | 0.8629 |
| cosine_accuracy@10 | 0.8986 |
| cosine_precision@1 | 0.6986 |
| cosine_precision@3 | 0.2757 |
| cosine_precision@5 | 0.1726 |
| cosine_precision@10 | 0.0899 |
| cosine_recall@1 | 0.6986 |
| cosine_recall@3 | 0.8271 |
| cosine_recall@5 | 0.8629 |
| cosine_recall@10 | 0.8986 |
| cosine_ndcg@10 | 0.8024 |
| cosine_mrr@10 | 0.7713 |
| **cosine_map@100** | **0.7759** |
#### 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 | 0.69 |
| cosine_accuracy@3 | 0.8271 |
| cosine_accuracy@5 | 0.86 |
| cosine_accuracy@10 | 0.9029 |
| cosine_precision@1 | 0.69 |
| cosine_precision@3 | 0.2757 |
| cosine_precision@5 | 0.172 |
| cosine_precision@10 | 0.0903 |
| cosine_recall@1 | 0.69 |
| cosine_recall@3 | 0.8271 |
| cosine_recall@5 | 0.86 |
| cosine_recall@10 | 0.9029 |
| cosine_ndcg@10 | 0.7999 |
| cosine_mrr@10 | 0.7666 |
| **cosine_map@100** | **0.7707** |
#### 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 | 0.6957 |
| cosine_accuracy@3 | 0.8229 |
| cosine_accuracy@5 | 0.86 |
| cosine_accuracy@10 | 0.8914 |
| cosine_precision@1 | 0.6957 |
| cosine_precision@3 | 0.2743 |
| cosine_precision@5 | 0.172 |
| cosine_precision@10 | 0.0891 |
| cosine_recall@1 | 0.6957 |
| cosine_recall@3 | 0.8229 |
| cosine_recall@5 | 0.86 |
| cosine_recall@10 | 0.8914 |
| cosine_ndcg@10 | 0.7975 |
| cosine_mrr@10 | 0.767 |
| **cosine_map@100** | **0.7718** |
#### 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 | 0.6871 |
| cosine_accuracy@3 | 0.8129 |
| cosine_accuracy@5 | 0.8457 |
| cosine_accuracy@10 | 0.8857 |
| cosine_precision@1 | 0.6871 |
| cosine_precision@3 | 0.271 |
| cosine_precision@5 | 0.1691 |
| cosine_precision@10 | 0.0886 |
| cosine_recall@1 | 0.6871 |
| cosine_recall@3 | 0.8129 |
| cosine_recall@5 | 0.8457 |
| cosine_recall@10 | 0.8857 |
| cosine_ndcg@10 | 0.7877 |
| cosine_mrr@10 | 0.7562 |
| **cosine_map@100** | **0.761** |
#### 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 | 0.6329 |
| cosine_accuracy@3 | 0.7771 |
| cosine_accuracy@5 | 0.8171 |
| cosine_accuracy@10 | 0.8571 |
| cosine_precision@1 | 0.6329 |
| cosine_precision@3 | 0.259 |
| cosine_precision@5 | 0.1634 |
| cosine_precision@10 | 0.0857 |
| cosine_recall@1 | 0.6329 |
| cosine_recall@3 | 0.7771 |
| cosine_recall@5 | 0.8171 |
| cosine_recall@10 | 0.8571 |
| cosine_ndcg@10 | 0.7483 |
| cosine_mrr@10 | 0.7131 |
| **cosine_map@100** | **0.719** |
<!--
## 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: 2 tokens</li><li>mean: 46.19 tokens</li><li>max: 371 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.39 tokens</li><li>max: 46 tokens</li></ul> |
* Samples:
| positive | anchor |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|
| <code>Cash used in financing activities in fiscal 2022 was primarily attributable to settlement of stock-based awards.</code> | <code>Why was there a net outflow of cash in financing activities in fiscal 2022?</code> |
| <code>Certain vendors have been impacted by volatility in the supply chain financing market.</code> | <code>How have certain vendors been impacted in the supply chain financing market?</code> |
| <code>In the consolidated financial statements for Visa, the net cash provided by operating activities amounted to 20,755 units in the most recent period, 18,849 units in the previous period, and 15,227 units in the period before that.</code> | <code>How much net cash did Visa's operating activities generate in the most recent period according to the financial statements?</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
- `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`: 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_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 |
|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8122 | 10 | 1.5643 | - | - | - | - | - |
| 0.9746 | 12 | - | 0.7349 | 0.7494 | 0.7524 | 0.6987 | 0.7569 |
| 1.6244 | 20 | 0.6756 | - | - | - | - | - |
| 1.9492 | 24 | - | 0.7555 | 0.7659 | 0.7683 | 0.7190 | 0.7700 |
| 2.4365 | 30 | 0.4561 | - | - | - | - | - |
| 2.9239 | 36 | - | 0.7592 | 0.7698 | 0.7698 | 0.7184 | 0.7741 |
| 3.2487 | 40 | 0.3645 | - | - | - | - | - |
| 3.8985 | 48 | - | 0.7610 | 0.7718 | 0.7707 | 0.7190 | 0.7759 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.2.0+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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