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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:500
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Non-context LLM: Prompt LLM directly with <atomic-fact> True or
False? without additional context.
Retrieval→LLM: Prompt with $k$ related passages retrieved from the knowledge source
as context.
Nonparametric probability (NP)): Compute the average likelihood of tokens in the
atomic fact by a masked LM and use that to make a prediction.
Retrieval→LLM + NP: Ensemble of two methods.
Some interesting observations on model hallucination behavior:
Error rates are higher for rarer entities in the task of biography generation.
Error rates are higher for facts mentioned later in the generation.
Using retrieval to ground the model generation significantly helps reduce hallucination.'
sentences:
- What is the impact of infrequent entities on the efficacy of language models in
the context of biography generation?
- In what ways does FActScore enhance the assessment of factual accuracy in long-form
content generation when compared to conventional evaluation techniques?
- What approaches does SelfCheckGPT implement when faced with questions it cannot
answer, and how does this influence its overall reliability in delivering accurate
information?
- source_sentence: 'Revision stage: Edit the output to correct content unsupported
by evidence while preserving the original content as much as possible. Initialize
the revised text $y=x$.
(1) Per $(q_i, e_{ij})$, an agreement model (via few-shot prompting + CoT, $(y,
q, e) \to {0,1}$) checks whether the evidence $e_i$ disagrees with the current
revised text $y$.
(2) Only if a disagreement is detect, the edit model (via few-shot prompting +
CoT, $(y, q, e) \to \text{ new }y$) outputs a new version of $y$ that aims to
agree with evidence $e_{ij}$ while otherwise minimally altering $y$.
(3) Finally only a limited number $M=5$ of evidence goes into the attribution
report $A$.
Fig. 12. Illustration of RARR (Retrofit Attribution using Research and Revision).
(Image source: Gao et al. 2022)
When evaluating the revised text $y$, both attribution and preservation metrics
matter.'
sentences:
- What impact does adjusting the sampling temperature have on the calibration of
large language models, and how does this influence the uncertainty of their outputs?
- How do unanswerable questions differ from answerable ones in the context of a
language model's understanding of its own capabilities?
- In what ways does the agreement model evaluate discrepancies between the provided
evidence and the updated text, and how does this evaluation impact the reliability
of AI-generated content modifications?
- source_sentence: 'Non-context LLM: Prompt LLM directly with <atomic-fact> True or
False? without additional context.
Retrieval→LLM: Prompt with $k$ related passages retrieved from the knowledge source
as context.
Nonparametric probability (NP)): Compute the average likelihood of tokens in the
atomic fact by a masked LM and use that to make a prediction.
Retrieval→LLM + NP: Ensemble of two methods.
Some interesting observations on model hallucination behavior:
Error rates are higher for rarer entities in the task of biography generation.
Error rates are higher for facts mentioned later in the generation.
Using retrieval to ground the model generation significantly helps reduce hallucination.'
sentences:
- In what ways can the acknowledgment of uncertainty by large language models (LLMs)
contribute to the mitigation of hallucinations and enhance the overall factual
accuracy of generated content?
- In what ways does the process of retrieving related passages contribute to minimizing
hallucinations in the outputs generated by language models, and how does this
approach differ from the application of nonparametric probability methods?
- How does the triplet structure $(c, y, y^*)$ play a crucial role in the categorization
of errors, and in what ways does it enhance the training process of the editor
model?
- source_sentence: 'Fine-tuning New Knowledge#
Fine-tuning a pre-trained LLM via supervised fine-tuning and RLHF is a common
technique for improving certain capabilities of the model like instruction following.
Introducing new knowledge at the fine-tuning stage is hard to avoid.
Fine-tuning usually consumes much less compute, making it debatable whether the
model can reliably learn new knowledge via small-scale fine-tuning. Gekhman et
al. 2024 studied the research question of whether fine-tuning LLMs on new knowledge
encourages hallucinations. They found that (1) LLMs learn fine-tuning examples
with new knowledge slower than other examples with knowledge consistent with the
pre-existing knowledge of the model; (2) Once the examples with new knowledge
are eventually learned, they increase the model’s tendency to hallucinate.'
sentences:
- How do the intentionally designed questions in TruthfulQA highlight prevalent
misunderstandings regarding AI responses in the healthcare domain?
- What effect does the slower acquisition of new knowledge compared to established
knowledge have on the effectiveness of large language models in practical scenarios?
- How do the RARR methodology and the FAVA model compare in their approaches to
mitigating hallucination errors in generated outputs, and what key distinctions
can be identified between the two?
- source_sentence: 'Revision stage: Edit the output to correct content unsupported
by evidence while preserving the original content as much as possible. Initialize
the revised text $y=x$.
(1) Per $(q_i, e_{ij})$, an agreement model (via few-shot prompting + CoT, $(y,
q, e) \to {0,1}$) checks whether the evidence $e_i$ disagrees with the current
revised text $y$.
(2) Only if a disagreement is detect, the edit model (via few-shot prompting +
CoT, $(y, q, e) \to \text{ new }y$) outputs a new version of $y$ that aims to
agree with evidence $e_{ij}$ while otherwise minimally altering $y$.
(3) Finally only a limited number $M=5$ of evidence goes into the attribution
report $A$.
Fig. 12. Illustration of RARR (Retrofit Attribution using Research and Revision).
(Image source: Gao et al. 2022)
When evaluating the revised text $y$, both attribution and preservation metrics
matter.'
sentences:
- What mechanisms does the editing algorithm employ to maintain fidelity to the
source material while simultaneously ensuring alignment with the supporting evidence?
- What is the impact of constraining the dataset to a maximum of $M=5$ instances
on the accuracy and reliability of the attribution report $A$ when analyzing AI-generated
content?
- In what ways does the implementation of a query generation model enhance the research
phase when it comes to validating the accuracy of information?
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: 0.8802083333333334
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.96875
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9895833333333334
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8802083333333334
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3229166666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19791666666666666
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8802083333333334
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.96875
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9895833333333334
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9477255159324969
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9301711309523809
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.930171130952381
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.875
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.96875
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9947916666666666
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.875
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3229166666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19895833333333335
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.875
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.96875
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9947916666666666
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9459628876705072
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9277405753968253
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9277405753968253
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.8802083333333334
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.96875
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9947916666666666
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8802083333333334
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3229166666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19895833333333335
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8802083333333334
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.96875
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9947916666666666
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9458393511377685
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9277405753968254
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9277405753968253
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.8697916666666666
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.984375
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9895833333333334
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9947916666666666
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8697916666666666
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.328125
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19791666666666666
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09947916666666667
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8697916666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.984375
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9895833333333334
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9947916666666666
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9440191417149189
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9265252976190478
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.92687251984127
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.8541666666666666
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.984375
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9947916666666666
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9947916666666666
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8541666666666666
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.328125
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19895833333333335
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09947916666666667
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8541666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.984375
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9947916666666666
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9947916666666666
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9380774892768095
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9184027777777778
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9186111111111112
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("joshuapb/fine-tuned-matryoshka-500")
# Run inference
sentences = [
'Revision stage: Edit the output to correct content unsupported by evidence while preserving the original content as much as possible. Initialize the revised text $y=x$.\n\n(1) Per $(q_i, e_{ij})$, an agreement model (via few-shot prompting + CoT, $(y, q, e) \\to {0,1}$) checks whether the evidence $e_i$ disagrees with the current revised text $y$.\n(2) Only if a disagreement is detect, the edit model (via few-shot prompting + CoT, $(y, q, e) \\to \\text{ new }y$) outputs a new version of $y$ that aims to agree with evidence $e_{ij}$ while otherwise minimally altering $y$.\n(3) Finally only a limited number $M=5$ of evidence goes into the attribution report $A$.\n\n\n\n\nFig. 12. Illustration of RARR (Retrofit Attribution using Research and Revision). (Image source: Gao et al. 2022)\nWhen evaluating the revised text $y$, both attribution and preservation metrics matter.',
'What mechanisms does the editing algorithm employ to maintain fidelity to the source material while simultaneously ensuring alignment with the supporting evidence?',
'What is the impact of constraining the dataset to a maximum of $M=5$ instances on the accuracy and reliability of the attribution report $A$ when analyzing AI-generated content?',
]
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 | 0.8802 |
| cosine_accuracy@3 | 0.9688 |
| cosine_accuracy@5 | 0.9896 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.8802 |
| cosine_precision@3 | 0.3229 |
| cosine_precision@5 | 0.1979 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.8802 |
| cosine_recall@3 | 0.9688 |
| cosine_recall@5 | 0.9896 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.9477 |
| cosine_mrr@10 | 0.9302 |
| **cosine_map@100** | **0.9302** |
#### 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 | 0.875 |
| cosine_accuracy@3 | 0.9688 |
| cosine_accuracy@5 | 0.9948 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.875 |
| cosine_precision@3 | 0.3229 |
| cosine_precision@5 | 0.199 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.875 |
| cosine_recall@3 | 0.9688 |
| cosine_recall@5 | 0.9948 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.946 |
| cosine_mrr@10 | 0.9277 |
| **cosine_map@100** | **0.9277** |
#### 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 | 0.8802 |
| cosine_accuracy@3 | 0.9688 |
| cosine_accuracy@5 | 0.9948 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.8802 |
| cosine_precision@3 | 0.3229 |
| cosine_precision@5 | 0.199 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.8802 |
| cosine_recall@3 | 0.9688 |
| cosine_recall@5 | 0.9948 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.9458 |
| cosine_mrr@10 | 0.9277 |
| **cosine_map@100** | **0.9277** |
#### 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 | 0.8698 |
| cosine_accuracy@3 | 0.9844 |
| cosine_accuracy@5 | 0.9896 |
| cosine_accuracy@10 | 0.9948 |
| cosine_precision@1 | 0.8698 |
| cosine_precision@3 | 0.3281 |
| cosine_precision@5 | 0.1979 |
| cosine_precision@10 | 0.0995 |
| cosine_recall@1 | 0.8698 |
| cosine_recall@3 | 0.9844 |
| cosine_recall@5 | 0.9896 |
| cosine_recall@10 | 0.9948 |
| cosine_ndcg@10 | 0.944 |
| cosine_mrr@10 | 0.9265 |
| **cosine_map@100** | **0.9269** |
#### 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 | 0.8542 |
| cosine_accuracy@3 | 0.9844 |
| cosine_accuracy@5 | 0.9948 |
| cosine_accuracy@10 | 0.9948 |
| cosine_precision@1 | 0.8542 |
| cosine_precision@3 | 0.3281 |
| cosine_precision@5 | 0.199 |
| cosine_precision@10 | 0.0995 |
| cosine_recall@1 | 0.8542 |
| cosine_recall@3 | 0.9844 |
| cosine_recall@5 | 0.9948 |
| cosine_recall@10 | 0.9948 |
| cosine_ndcg@10 | 0.9381 |
| cosine_mrr@10 | 0.9184 |
| **cosine_map@100** | **0.9186** |
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## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
#### 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`: 8
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `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`: 5
- `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`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `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
- `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`: batch_sampler
- `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 |
|:-------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.0794 | 5 | 5.4149 | - | - | - | - | - |
| 0.1587 | 10 | 4.8587 | - | - | - | - | - |
| 0.2381 | 15 | 3.9711 | - | - | - | - | - |
| 0.3175 | 20 | 3.4853 | - | - | - | - | - |
| 0.3968 | 25 | 3.6227 | - | - | - | - | - |
| 0.4762 | 30 | 3.3359 | - | - | - | - | - |
| 0.5556 | 35 | 2.0868 | - | - | - | - | - |
| 0.6349 | 40 | 2.256 | - | - | - | - | - |
| 0.7143 | 45 | 2.2958 | - | - | - | - | - |
| 0.7937 | 50 | 1.7128 | - | - | - | - | - |
| 0.8730 | 55 | 2.029 | - | - | - | - | - |
| 0.9524 | 60 | 1.9104 | - | - | - | - | - |
| 1.0 | 63 | - | 0.8950 | 0.9042 | 0.9039 | 0.8640 | 0.8989 |
| 1.0317 | 65 | 2.5929 | - | - | - | - | - |
| 1.1111 | 70 | 1.4257 | - | - | - | - | - |
| 1.1905 | 75 | 1.9956 | - | - | - | - | - |
| 1.2698 | 80 | 1.5845 | - | - | - | - | - |
| 1.3492 | 85 | 1.7383 | - | - | - | - | - |
| 1.4286 | 90 | 1.4657 | - | - | - | - | - |
| 1.5079 | 95 | 1.8461 | - | - | - | - | - |
| 1.5873 | 100 | 1.8531 | - | - | - | - | - |
| 1.6667 | 105 | 1.6504 | - | - | - | - | - |
| 1.7460 | 110 | 2.7636 | - | - | - | - | - |
| 1.8254 | 115 | 0.7195 | - | - | - | - | - |
| 1.9048 | 120 | 1.2494 | - | - | - | - | - |
| 1.9841 | 125 | 1.7331 | - | - | - | - | - |
| 2.0 | 126 | - | 0.9170 | 0.9340 | 0.9167 | 0.9013 | 0.9179 |
| 2.0635 | 130 | 1.1102 | - | - | - | - | - |
| 2.1429 | 135 | 1.8586 | - | - | - | - | - |
| 2.2222 | 140 | 1.4211 | - | - | - | - | - |
| 2.3016 | 145 | 1.9531 | - | - | - | - | - |
| 2.3810 | 150 | 1.9516 | - | - | - | - | - |
| 2.4603 | 155 | 2.1174 | - | - | - | - | - |
| 2.5397 | 160 | 1.7883 | - | - | - | - | - |
| 2.6190 | 165 | 1.4537 | - | - | - | - | - |
| 2.6984 | 170 | 1.3927 | - | - | - | - | - |
| 2.7778 | 175 | 1.2559 | - | - | - | - | - |
| 2.8571 | 180 | 1.8748 | - | - | - | - | - |
| 2.9365 | 185 | 0.7509 | - | - | - | - | - |
| 3.0 | 189 | - | 0.9312 | 0.9244 | 0.9241 | 0.9199 | 0.9349 |
| 3.0159 | 190 | 0.947 | - | - | - | - | - |
| 3.0952 | 195 | 1.9463 | - | - | - | - | - |
| 3.1746 | 200 | 1.2077 | - | - | - | - | - |
| 3.2540 | 205 | 0.7721 | - | - | - | - | - |
| 3.3333 | 210 | 1.5633 | - | - | - | - | - |
| 3.4127 | 215 | 1.5042 | - | - | - | - | - |
| 3.4921 | 220 | 1.1531 | - | - | - | - | - |
| 3.5714 | 225 | 1.2408 | - | - | - | - | - |
| 3.6508 | 230 | 0.8085 | - | - | - | - | - |
| 3.7302 | 235 | 1.1195 | - | - | - | - | - |
| 3.8095 | 240 | 1.1843 | - | - | - | - | - |
| 3.8889 | 245 | 0.7176 | - | - | - | - | - |
| 3.9683 | 250 | 1.1715 | - | - | - | - | - |
| 4.0 | 252 | - | 0.9244 | 0.9287 | 0.9251 | 0.9199 | 0.9300 |
| 4.0476 | 255 | 1.3187 | - | - | - | - | - |
| 4.1270 | 260 | 0.2891 | - | - | - | - | - |
| 4.2063 | 265 | 1.5887 | - | - | - | - | - |
| 4.2857 | 270 | 1.1227 | - | - | - | - | - |
| 4.3651 | 275 | 1.5385 | - | - | - | - | - |
| 4.4444 | 280 | 0.4732 | - | - | - | - | - |
| 4.5238 | 285 | 1.2039 | - | - | - | - | - |
| 4.6032 | 290 | 1.0755 | - | - | - | - | - |
| 4.6825 | 295 | 1.5345 | - | - | - | - | - |
| 4.7619 | 300 | 1.4255 | - | - | - | - | - |
| 4.8413 | 305 | 1.7436 | - | - | - | - | - |
| 4.9206 | 310 | 0.9408 | - | - | - | - | - |
| **5.0** | **315** | **0.7724** | **0.9269** | **0.9277** | **0.9277** | **0.9186** | **0.9302** |
* 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: 2.21.0
- 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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