|
--- |
|
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** | |
|
|
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#### 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 | |
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| cosine_recall@5 | 0.9948 | |
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| cosine_recall@10 | 1.0 | |
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| cosine_ndcg@10 | 0.946 | |
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| cosine_mrr@10 | 0.9277 | |
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| **cosine_map@100** | **0.9277** | |
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#### Information Retrieval |
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* Dataset: `dim_256` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
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|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.8802 | |
|
| cosine_accuracy@3 | 0.9688 | |
|
| cosine_accuracy@5 | 0.9948 | |
|
| cosine_accuracy@10 | 1.0 | |
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| cosine_precision@1 | 0.8802 | |
|
| cosine_precision@3 | 0.3229 | |
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| cosine_precision@5 | 0.199 | |
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| cosine_precision@10 | 0.1 | |
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| cosine_recall@1 | 0.8802 | |
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| cosine_recall@3 | 0.9688 | |
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| cosine_recall@5 | 0.9948 | |
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| cosine_recall@10 | 1.0 | |
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| cosine_ndcg@10 | 0.9458 | |
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| cosine_mrr@10 | 0.9277 | |
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| **cosine_map@100** | **0.9277** | |
|
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#### Information Retrieval |
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* 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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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
|
|
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## Training Details |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
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- `per_device_eval_batch_size`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 5 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `load_best_model_at_end`: True |
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 8 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 5 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `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 |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
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- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
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|
|
</details> |
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|
|
### 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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*Clearly define terms in order to be accessible across audiences.* |
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## Model Card Authors |
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