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Jaswanth160
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bge-base-financial-matryoshka

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:6300
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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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: Walmart Connect provides house advertising offerings.
sentences:
- What was the fair value per performance-based share granted for the fiscal years
2023, 2022, and 2021?
- What services does Walmart Connect offer?
- By how much did membership fees increase in 2023?
- source_sentence: The total revenue for 2023 was reported as $371,620 million.
sentences:
- What was the percentage increase in Humalog revenue from 2022 to 2023?
- What was the total revenue for the year 2023?
- What were the primary factors influencing profitability in the automotive market
in 2023?
- source_sentence: •LinkedIn revenue increased 10%.
sentences:
- By what percentage did LinkedIn's revenue increase in fiscal year 2023?
- What factors influence the recording of the Company's credit-related contingent
features in financial statements?
- What is the average tenure of associates at the company as of December 31, 2023?
- source_sentence: Cash flows from operating activities in 2023 were primarily generated
from management and franchise fee revenue and operating income from owned and
leased hotels.
sentences:
- What is the significance of the Company’s trademarks to their businesses?
- By what percentage did the S&P 500 Index increase in 2023 compared to the end
of 2022?
- What were the primary sources of operating activities cash flow in 2023?
- source_sentence: The par call date for the 7% Notes due 2029 is August 15, 2025,
allowing for redemption at par from this date onward.
sentences:
- What is the earliest date on which the 7% Notes due 2029 can be redeemed at par?
- What are some of the initiatives managed by Visa for supporting underrepresented
communities?
- Who are the competitors for Microsoft's server applications in PC-based environments?
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: 0.6942857142857143
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8314285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8728571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9071428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6942857142857143
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27714285714285714
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17457142857142854
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09071428571428569
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6942857142857143
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8314285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8728571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9071428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8042383857063928
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7708656462585032
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7746128511093645
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: 0.8371428571428572
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.87
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9114285714285715
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6985714285714286
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27904761904761904
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.174
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09114285714285714
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6985714285714286
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8371428571428572
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.87
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9114285714285715
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8075815858913178
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7741315192743762
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7776656953157759
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.7
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.83
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.86
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9071428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27666666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17199999999999996
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0907142857142857
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.83
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.86
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9071428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8048199967282856
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7720073696145123
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.775510167698765
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.67
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8185714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8571428571428571
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8971428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.67
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27285714285714285
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1714285714285714
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0897142857142857
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.67
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8185714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8571428571428571
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8971428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7867880427582347
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7511031746031744
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7551868866444579
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.65
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7914285714285715
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8385714285714285
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8785714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.65
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26380952380952377
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16771428571428568
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08785714285714286
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.65
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7914285714285715
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8385714285714285
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8785714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7645553995345995
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.727849206349206
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.73258711812532
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("Jaswanth160/bge-base-financial-matryoshka")
# Run inference
sentences = [
'The par call date for the 7% Notes due 2029 is August 15, 2025, allowing for redemption at par from this date onward.',
'What is the earliest date on which the 7% Notes due 2029 can be redeemed at par?',
'What are some of the initiatives managed by Visa for supporting underrepresented communities?',
]
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 | 0.6943 |
| cosine_accuracy@3 | 0.8314 |
| cosine_accuracy@5 | 0.8729 |
| cosine_accuracy@10 | 0.9071 |
| cosine_precision@1 | 0.6943 |
| cosine_precision@3 | 0.2771 |
| cosine_precision@5 | 0.1746 |
| cosine_precision@10 | 0.0907 |
| cosine_recall@1 | 0.6943 |
| cosine_recall@3 | 0.8314 |
| cosine_recall@5 | 0.8729 |
| cosine_recall@10 | 0.9071 |
| cosine_ndcg@10 | 0.8042 |
| cosine_mrr@10 | 0.7709 |
| **cosine_map@100** | **0.7746** |
#### 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 | 0.8371 |
| cosine_accuracy@5 | 0.87 |
| cosine_accuracy@10 | 0.9114 |
| cosine_precision@1 | 0.6986 |
| cosine_precision@3 | 0.279 |
| cosine_precision@5 | 0.174 |
| cosine_precision@10 | 0.0911 |
| cosine_recall@1 | 0.6986 |
| cosine_recall@3 | 0.8371 |
| cosine_recall@5 | 0.87 |
| cosine_recall@10 | 0.9114 |
| cosine_ndcg@10 | 0.8076 |
| cosine_mrr@10 | 0.7741 |
| **cosine_map@100** | **0.7777** |
#### 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 | 0.7 |
| cosine_accuracy@3 | 0.83 |
| cosine_accuracy@5 | 0.86 |
| cosine_accuracy@10 | 0.9071 |
| cosine_precision@1 | 0.7 |
| cosine_precision@3 | 0.2767 |
| cosine_precision@5 | 0.172 |
| cosine_precision@10 | 0.0907 |
| cosine_recall@1 | 0.7 |
| cosine_recall@3 | 0.83 |
| cosine_recall@5 | 0.86 |
| cosine_recall@10 | 0.9071 |
| cosine_ndcg@10 | 0.8048 |
| cosine_mrr@10 | 0.772 |
| **cosine_map@100** | **0.7755** |
#### 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 | 0.67 |
| cosine_accuracy@3 | 0.8186 |
| cosine_accuracy@5 | 0.8571 |
| cosine_accuracy@10 | 0.8971 |
| cosine_precision@1 | 0.67 |
| cosine_precision@3 | 0.2729 |
| cosine_precision@5 | 0.1714 |
| cosine_precision@10 | 0.0897 |
| cosine_recall@1 | 0.67 |
| cosine_recall@3 | 0.8186 |
| cosine_recall@5 | 0.8571 |
| cosine_recall@10 | 0.8971 |
| cosine_ndcg@10 | 0.7868 |
| cosine_mrr@10 | 0.7511 |
| **cosine_map@100** | **0.7552** |
#### 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 | 0.65 |
| cosine_accuracy@3 | 0.7914 |
| cosine_accuracy@5 | 0.8386 |
| cosine_accuracy@10 | 0.8786 |
| cosine_precision@1 | 0.65 |
| cosine_precision@3 | 0.2638 |
| cosine_precision@5 | 0.1677 |
| cosine_precision@10 | 0.0879 |
| cosine_recall@1 | 0.65 |
| cosine_recall@3 | 0.7914 |
| cosine_recall@5 | 0.8386 |
| cosine_recall@10 | 0.8786 |
| cosine_ndcg@10 | 0.7646 |
| cosine_mrr@10 | 0.7278 |
| **cosine_map@100** | **0.7326** |
<!--
## 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: 6 tokens</li><li>mean: 47.11 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.36 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
| positive | anchor |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------|
| <code>For some of our medical membership, we share risk with providers under capitation contracts where physicians and hospitals accept varying levels of financial risk for a defined set of membership, primarily HMO membership.</code> | <code>What is the primary type of membership for which risk is shared with providers under capitation contracts?</code> |
| <code>Revenue for Comcast's Theme Parks segment is primarily derived from guest spending at the theme parks, including ticket sales and in-park spending on food, beverages, and merchandise.</code> | <code>What is the primary revenue source for Comcast's Theme Parks segment?</code> |
| <code>In August 2022, the Board of Directors authorized a program to repurchase up to $10.0 billion of the Company’s common stock, referred to as the "Share Repurchase Program". In February 2023, the Board of Directors authorized an additional $10.0 billion in repurchases under the Share Repurchase Program, bringing the aggregate total authorized to $20.0 billion.</code> | <code>What was the total authorization amount for the Share Repurchase Program of the Company as of February 2023?</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
- `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`: 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`: 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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8122 | 10 | 1.5811 | - | - | - | - | - |
| 0.9746 | 12 | - | 0.7341 | 0.7568 | 0.7632 | 0.7056 | 0.7660 |
| 1.6244 | 20 | 0.6854 | - | - | - | - | - |
| 1.9492 | 24 | - | 0.7516 | 0.7705 | 0.7722 | 0.7263 | 0.7702 |
| 2.4365 | 30 | 0.4874 | - | - | - | - | - |
| **2.9239** | **36** | **-** | **0.755** | **0.7747** | **0.7756** | **0.7321** | **0.7739** |
| 3.2487 | 40 | 0.3876 | - | - | - | - | - |
| 3.8985 | 48 | - | 0.7552 | 0.7755 | 0.7777 | 0.7326 | 0.7746 |
* 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: 0.33.0
- 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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