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---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
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
widget:
- source_sentence: Item 3—Legal Proceedings See discussion of Legal Proceedings in
Note 10 to the consolidated financial statements included in Item 8 of this Report.
sentences:
- How much did the company's finance lease obligations total as of December 31,
2023?
- What do Note 10 and Item 8 of the report encompass?
- What was the basic earnings per common share attributable to Comcast Corporation
shareholders in 2023?
- source_sentence: Our quarterly Insurance segment earnings and operating cash flows
are impacted by the Medicare Part D benefit Grant program, the changing membership
composition, and the multistage plan period starting annually on January 1. These
plan designs generally result in us sharing a greater portion of the responsibility
for total prescription drug costs in the early stages and less in the latter stages.
sentences:
- What are the two main categories into which Ford Motor Company classifies its
costs and expenses, excluding those related to Ford Credit?
- How does the benefit design of Medicare Part D impact the quarterly insurance
segment earnings and operating cash flows?
- What basis is used to record HTM investment securities in Schwab's financial statements?
- source_sentence: Operating Profit in the Wizards of the Coast and Digital Gaming
segment decreased 2% to $538.3 million.
sentences:
- How much did the Wizards of the Coast and Digital Gaming segment's operating profit
change in 2022?
- What factors are considered in evaluating the lifetime losses for most loans and
receivables?
- How did the loss on certain U.S. affiliates impact the Company's effective tax
rate in the fiscal fourth quarter of 2021?
- source_sentence: In 2023, the net earnings of Johnson & Johnson were $35,153 million.
The company also registered cash dividends paid amounting to $11,770 million for
the year, priced at $4.70 per share.
sentences:
- What was the postpaid churn rate for AT&T Inc. in 2023?
- What was the GAAP net revenue for the fiscal year ended October 31, 2023?
- What were the total net earnings of Johnson & Johnson in the year 2023?
- source_sentence: During fiscal 2022, GameStop Corp increased its valuation allowances
by approximately $70.2 million in various jurisdictions.
sentences:
- How much did GameStop Corp's valuation allowances increase during fiscal 2022?
- How does Gilead ensure an inclusive and diverse workforce?
- What factors are considered in determining the estimated future warranty costs
for connected fitness and Precor branded fitness products?
pipeline_tag: sentence-similarity
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: 0.7185714285714285
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.83
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8714285714285714
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.91
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7185714285714285
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27666666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17428571428571427
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.091
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7185714285714285
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.83
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8714285714285714
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.91
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8137967516958747
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7830442176870747
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7866777593387027
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.7114285714285714
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: 0.9142857142857143
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7114285714285714
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: 0.09142857142857141
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7114285714285714
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: 0.9142857142857143
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8123538841130576
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7798667800453513
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7831580648041446
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: 0.8285714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8614285714285714
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9042857142857142
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2761904761904762
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17228571428571426
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09042857142857143
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8285714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8614285714285714
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9042857142857142
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8043112987059042
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7721706349206346
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7759026470022171
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6857142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8071428571428572
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: 0.6857142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26904761904761904
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: 0.6857142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8071428571428572
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: 0.79087795854059
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7568854875283447
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7608935817550728
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: 0.7757142857142857
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8128571428571428
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8671428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.66
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.25857142857142856
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16257142857142853
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0867142857142857
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.66
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7757142857142857
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8128571428571428
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8671428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7616045249840884
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7281247165532877
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7330922421864847
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("cristuf/bge-base-financial-matryoshka")
# Run inference
sentences = [
'During fiscal 2022, GameStop Corp increased its valuation allowances by approximately $70.2 million in various jurisdictions.',
"How much did GameStop Corp's valuation allowances increase during fiscal 2022?",
'How does Gilead ensure an inclusive and diverse workforce?',
]
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 | 0.7186 |
| cosine_accuracy@3 | 0.83 |
| cosine_accuracy@5 | 0.8714 |
| cosine_accuracy@10 | 0.91 |
| cosine_precision@1 | 0.7186 |
| cosine_precision@3 | 0.2767 |
| cosine_precision@5 | 0.1743 |
| cosine_precision@10 | 0.091 |
| cosine_recall@1 | 0.7186 |
| cosine_recall@3 | 0.83 |
| cosine_recall@5 | 0.8714 |
| cosine_recall@10 | 0.91 |
| cosine_ndcg@10 | 0.8138 |
| cosine_mrr@10 | 0.783 |
| **cosine_map@100** | **0.7867** |
#### 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 | 0.7114 |
| cosine_accuracy@3 | 0.8314 |
| cosine_accuracy@5 | 0.8729 |
| cosine_accuracy@10 | 0.9143 |
| cosine_precision@1 | 0.7114 |
| cosine_precision@3 | 0.2771 |
| cosine_precision@5 | 0.1746 |
| cosine_precision@10 | 0.0914 |
| cosine_recall@1 | 0.7114 |
| cosine_recall@3 | 0.8314 |
| cosine_recall@5 | 0.8729 |
| cosine_recall@10 | 0.9143 |
| cosine_ndcg@10 | 0.8124 |
| cosine_mrr@10 | 0.7799 |
| **cosine_map@100** | **0.7832** |
#### 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 | 0.8286 |
| cosine_accuracy@5 | 0.8614 |
| cosine_accuracy@10 | 0.9043 |
| cosine_precision@1 | 0.7 |
| cosine_precision@3 | 0.2762 |
| cosine_precision@5 | 0.1723 |
| cosine_precision@10 | 0.0904 |
| cosine_recall@1 | 0.7 |
| cosine_recall@3 | 0.8286 |
| cosine_recall@5 | 0.8614 |
| cosine_recall@10 | 0.9043 |
| cosine_ndcg@10 | 0.8043 |
| cosine_mrr@10 | 0.7722 |
| **cosine_map@100** | **0.7759** |
#### 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 | 0.6857 |
| cosine_accuracy@3 | 0.8071 |
| cosine_accuracy@5 | 0.8571 |
| cosine_accuracy@10 | 0.8971 |
| cosine_precision@1 | 0.6857 |
| cosine_precision@3 | 0.269 |
| cosine_precision@5 | 0.1714 |
| cosine_precision@10 | 0.0897 |
| cosine_recall@1 | 0.6857 |
| cosine_recall@3 | 0.8071 |
| cosine_recall@5 | 0.8571 |
| cosine_recall@10 | 0.8971 |
| cosine_ndcg@10 | 0.7909 |
| cosine_mrr@10 | 0.7569 |
| **cosine_map@100** | **0.7609** |
#### 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 | 0.7757 |
| cosine_accuracy@5 | 0.8129 |
| cosine_accuracy@10 | 0.8671 |
| cosine_precision@1 | 0.66 |
| cosine_precision@3 | 0.2586 |
| cosine_precision@5 | 0.1626 |
| cosine_precision@10 | 0.0867 |
| cosine_recall@1 | 0.66 |
| cosine_recall@3 | 0.7757 |
| cosine_recall@5 | 0.8129 |
| cosine_recall@10 | 0.8671 |
| cosine_ndcg@10 | 0.7616 |
| cosine_mrr@10 | 0.7281 |
| **cosine_map@100** | **0.7331** |
<!--
## 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: 46.36 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.41 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------|
| <code>Japan's revenue for the year 2023 reached 2,367.0 million.</code> | <code>What was the revenue attributed to Japan in the year 2023?</code> |
| <code>Our four reportable segments are: •the Data Center segment, which primarily includes server CPUs, GPUs, APUs, DPUs, FPGAs, SmartNICs, AI accelerators and Adaptive SoC products for data centers; •the Client segment, which primarily includes CPUs, APUs, and chipsets for desktop, notebook and handheld personal computers; •the Gaming segment, which primarily includes discrete GPUs, semi-custom SoC products and development services; and •the Embedded segment, which primarily includes embedded CPUs, GPUs, APUs, FPGAs, SOMs, and Adaptive SoC products.</code> | <code>What are the different segments that AMD reports financially?</code> |
| <code>For detailed information about the company's legal proceedings, see Note 4 to the consolidated financial statements, included under the caption 'Contingencies' in the Annual Report on Form 10-K.</code> | <code>Where can detailed information about the company's legal proceedings be found in its 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
- `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
- `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 | 1.5267 | - | - | - | - | - |
| 0.9746 | 12 | - | 0.7446 | 0.7639 | 0.7765 | 0.7039 | 0.7725 |
| 1.6244 | 20 | 0.6742 | - | - | - | - | - |
| 1.9492 | 24 | - | 0.7606 | 0.7795 | 0.7828 | 0.7297 | 0.7839 |
| 2.4365 | 30 | 0.4469 | - | - | - | - | - |
| **2.9239** | **36** | **-** | **0.7643** | **0.7758** | **0.7834** | **0.7332** | **0.7845** |
| 3.2487 | 40 | 0.3712 | - | - | - | - | - |
| 3.8985 | 48 | - | 0.7609 | 0.7759 | 0.7832 | 0.7331 | 0.7867 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.8
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.1+cu121
- Accelerate: 0.30.1
- 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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