|
--- |
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base_model: Alibaba-NLP/gte-base-en-v1.5 |
|
language: |
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- en |
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library_name: sentence-transformers |
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license: apache-2.0 |
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metrics: |
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- 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 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:32833 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
|
widget: |
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- source_sentence: Anonymity in online interactions can lead to a disinhibition effect, |
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where individuals feel free to express hostile or aggressive opinions they might |
|
otherwise suppress. |
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sentences: |
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- What are the implications of anonymity in online interactions? |
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- How does creativity function as a form of costly signalling in personal expressions |
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such as invitations? |
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- Why is conflict considered essential in a creative organization? |
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- source_sentence: The author decides to release their novel into the world despite |
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its imperfections, and finds that this allows them to move on to new projects |
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and experiences, and to focus on the value of the work itself rather than its |
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flaws. |
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sentences: |
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- How does the author's experience with their novel illustrate the concept of 'embracing |
|
imperfection' in creative work? |
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- What does the author mean by 'ambitious programmers are better off doing their |
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own thing'? |
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- What is the role of 'show me' in the design process? |
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- source_sentence: Tokens become more valuable as more users adopt them, creating |
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a positive feedback loop that enhances their utility and encourages further adoption |
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across various applications. |
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sentences: |
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- In what ways do tokens exhibit network effects? |
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- What can sometimes be found when considering a startup with a lame-sounding idea? |
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- How do social norms influence decision-making in the context of airport choices? |
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- source_sentence: Philosophers are often viewed as the guardians of critical thinking; |
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however, their reliance on bureaucratic structures and abstract discussions can |
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become problematic. Instead of fostering open-mindedness, they may perpetuate |
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dogmatic thinking and limit the exploration of diverse perspectives, thereby failing |
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to fulfill their duty of promoting genuine critical engagement. |
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sentences: |
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- In what ways can the role of philosophers be seen as essential or problematic |
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within the context of critical thinking? |
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- How does the evolution of pair-bonding facilitate cultural exchange between groups? |
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- What is the role of autonomy in the success of acquired startups? |
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- source_sentence: Society tends to admire those who despair when others hope, viewing |
|
them as sages or wise figures. |
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sentences: |
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- What is often the societal perception of those who express pessimism about the |
|
future? |
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- How did the realization about user engagement influence the app development strategy? |
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- What lessons can be learned from the historical context of employee relations |
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in large corporations? |
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model-index: |
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- name: Alchemy Embedding - Anudit Nagar |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
|
value: 0.782012613106663 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8889498217713189 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9248697559638058 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9520153550863724 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.782012613106663 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.29631660725710623 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1849739511927612 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09520153550863725 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.782012613106663 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8889498217713189 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9248697559638058 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9520153550863724 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.867555587052628 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8402608580220322 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8422322227138224 |
|
name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
|
name: Information Retrieval |
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dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.780367425281053 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8848368522072937 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9221277762544557 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9514669591445023 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.780367425281053 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2949456174024312 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1844255552508912 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09514669591445023 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.780367425281053 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8848368522072937 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9221277762544557 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9514669591445023 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8661558392165704 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.838656038231032 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8405372438205077 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7754318618042226 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8804496846723334 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9169180148066904 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9468055936386071 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7754318618042226 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2934832282241111 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.18338360296133807 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09468055936386072 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7754318618042226 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8804496846723334 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9169180148066904 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9468055936386071 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8613819477350178 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8338379881703168 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8360735900013385 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7617219632574719 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.871675349602413 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9117082533589251 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9418700301617768 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7617219632574719 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2905584498674709 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.18234165067178504 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09418700301617768 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7617219632574719 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.871675349602413 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9117082533589251 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9418700301617768 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.851649908463093 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8225671458602635 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8248455884524328 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7408829174664108 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.853852481491637 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8936111872772141 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9292569234987661 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7408829174664108 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.28461749383054563 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17872223745544283 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0929256923498766 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7408829174664108 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.853852481491637 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8936111872772141 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9292569234987661 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8338956659320366 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8033378162525404 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8057702637208689 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# Alchemy Embedding - Anudit Nagar |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) on the json dataset. 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:** [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) <!-- at revision a8e4f3e0ee719c75bc30d12b8eae0f8440502718 --> |
|
- **Maximum Sequence Length:** 8192 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
|
- json |
|
- **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': 8192, 'do_lower_case': False}) with Transformer model: NewModel |
|
(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}) |
|
) |
|
``` |
|
|
|
## 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("sentence_transformers_model_id") |
|
# Run inference |
|
sentences = [ |
|
'Society tends to admire those who despair when others hope, viewing them as sages or wise figures.', |
|
'What is often the societal perception of those who express pessimism about the future?', |
|
'How did the realization about user engagement influence the app development strategy?', |
|
] |
|
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> |
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|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
|
|
|
You can finetune this model on your own dataset. |
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|
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<details><summary>Click to expand</summary> |
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|
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</details> |
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--> |
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|
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<!-- |
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### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## 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 | 0.782 | |
|
| cosine_accuracy@3 | 0.8889 | |
|
| cosine_accuracy@5 | 0.9249 | |
|
| cosine_accuracy@10 | 0.952 | |
|
| cosine_precision@1 | 0.782 | |
|
| cosine_precision@3 | 0.2963 | |
|
| cosine_precision@5 | 0.185 | |
|
| cosine_precision@10 | 0.0952 | |
|
| cosine_recall@1 | 0.782 | |
|
| cosine_recall@3 | 0.8889 | |
|
| cosine_recall@5 | 0.9249 | |
|
| cosine_recall@10 | 0.952 | |
|
| cosine_ndcg@10 | 0.8676 | |
|
| cosine_mrr@10 | 0.8403 | |
|
| **cosine_map@100** | **0.8422** | |
|
|
|
#### 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 | 0.7804 | |
|
| cosine_accuracy@3 | 0.8848 | |
|
| cosine_accuracy@5 | 0.9221 | |
|
| cosine_accuracy@10 | 0.9515 | |
|
| cosine_precision@1 | 0.7804 | |
|
| cosine_precision@3 | 0.2949 | |
|
| cosine_precision@5 | 0.1844 | |
|
| cosine_precision@10 | 0.0951 | |
|
| cosine_recall@1 | 0.7804 | |
|
| cosine_recall@3 | 0.8848 | |
|
| cosine_recall@5 | 0.9221 | |
|
| cosine_recall@10 | 0.9515 | |
|
| cosine_ndcg@10 | 0.8662 | |
|
| cosine_mrr@10 | 0.8387 | |
|
| **cosine_map@100** | **0.8405** | |
|
|
|
#### 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 | 0.7754 | |
|
| cosine_accuracy@3 | 0.8804 | |
|
| cosine_accuracy@5 | 0.9169 | |
|
| cosine_accuracy@10 | 0.9468 | |
|
| cosine_precision@1 | 0.7754 | |
|
| cosine_precision@3 | 0.2935 | |
|
| cosine_precision@5 | 0.1834 | |
|
| cosine_precision@10 | 0.0947 | |
|
| cosine_recall@1 | 0.7754 | |
|
| cosine_recall@3 | 0.8804 | |
|
| cosine_recall@5 | 0.9169 | |
|
| cosine_recall@10 | 0.9468 | |
|
| cosine_ndcg@10 | 0.8614 | |
|
| cosine_mrr@10 | 0.8338 | |
|
| **cosine_map@100** | **0.8361** | |
|
|
|
#### 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 | 0.7617 | |
|
| cosine_accuracy@3 | 0.8717 | |
|
| cosine_accuracy@5 | 0.9117 | |
|
| cosine_accuracy@10 | 0.9419 | |
|
| cosine_precision@1 | 0.7617 | |
|
| cosine_precision@3 | 0.2906 | |
|
| cosine_precision@5 | 0.1823 | |
|
| cosine_precision@10 | 0.0942 | |
|
| cosine_recall@1 | 0.7617 | |
|
| cosine_recall@3 | 0.8717 | |
|
| cosine_recall@5 | 0.9117 | |
|
| cosine_recall@10 | 0.9419 | |
|
| cosine_ndcg@10 | 0.8516 | |
|
| cosine_mrr@10 | 0.8226 | |
|
| **cosine_map@100** | **0.8248** | |
|
|
|
#### 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 | 0.7409 | |
|
| cosine_accuracy@3 | 0.8539 | |
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| cosine_accuracy@5 | 0.8936 | |
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| cosine_accuracy@10 | 0.9293 | |
|
| cosine_precision@1 | 0.7409 | |
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| cosine_precision@3 | 0.2846 | |
|
| cosine_precision@5 | 0.1787 | |
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| cosine_precision@10 | 0.0929 | |
|
| cosine_recall@1 | 0.7409 | |
|
| cosine_recall@3 | 0.8539 | |
|
| cosine_recall@5 | 0.8936 | |
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| cosine_recall@10 | 0.9293 | |
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| cosine_ndcg@10 | 0.8339 | |
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| cosine_mrr@10 | 0.8033 | |
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| **cosine_map@100** | **0.8058** | |
|
|
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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 |
|
|
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### Training Dataset |
|
|
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#### json |
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|
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* Dataset: json |
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* Size: 32,833 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 34.54 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 16.78 tokens</li><li>max: 77 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------| |
|
| <code>The author saw taking risks as a necessary part of the creative process, and was willing to take risks in order to explore new ideas and themes.</code> | <code>What was the author's perspective on the importance of taking risks in creative work?</code> | |
|
| <code>Recognizing that older users are less likely to invite new users led to a strategic focus on younger demographics, prompting a shift in development efforts toward creating products that resonate with teens.</code> | <code>How did the realization about user engagement influence the app development strategy?</code> | |
|
| <code>The phrase emphasizes the fragility of Earth and our collective responsibility to protect it and ensure sustainable resource management for future generations.</code> | <code>What is the significance of the phrase 'pale blue dot' in relation to environmental responsibility?</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
|
- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 24 |
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- `per_device_eval_batch_size`: 24 |
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- `gradient_accumulation_steps`: 8 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `load_best_model_at_end`: True |
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- `batch_sampler`: no_duplicates |
|
|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `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`: 24 |
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- `per_device_eval_batch_size`: 24 |
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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`: 8 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_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`: 4 |
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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`: True |
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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 |
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- `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 |
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- `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 |
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- `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 |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `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 |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `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 |
|
- `eval_on_start`: False |
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- `eval_use_gather_object`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
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|
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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 | |
|
|:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.0584 | 10 | 0.8567 | - | - | - | - | - | |
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| 0.1169 | 20 | 0.6549 | - | - | - | - | - | |
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| 0.1753 | 30 | 0.5407 | - | - | - | - | - | |
|
| 0.2337 | 40 | 0.4586 | - | - | - | - | - | |
|
| 0.2922 | 50 | 0.3914 | - | - | - | - | - | |
|
| 0.3506 | 60 | 0.4104 | - | - | - | - | - | |
|
| 0.4091 | 70 | 0.299 | - | - | - | - | - | |
|
| 0.4675 | 80 | 0.2444 | - | - | - | - | - | |
|
| 0.5259 | 90 | 0.2367 | - | - | - | - | - | |
|
| 0.5844 | 100 | 0.2302 | - | - | - | - | - | |
|
| 0.6428 | 110 | 0.2356 | - | - | - | - | - | |
|
| 0.7012 | 120 | 0.1537 | - | - | - | - | - | |
|
| 0.7597 | 130 | 0.2043 | - | - | - | - | - | |
|
| 0.8181 | 140 | 0.1606 | - | - | - | - | - | |
|
| 0.8766 | 150 | 0.1896 | - | - | - | - | - | |
|
| 0.9350 | 160 | 0.1766 | - | - | - | - | - | |
|
| 0.9934 | 170 | 0.1259 | - | - | - | - | - | |
|
| 0.9993 | 171 | - | 0.8115 | 0.8233 | 0.8321 | 0.7829 | 0.8340 | |
|
| 1.0519 | 180 | 0.1661 | - | - | - | - | - | |
|
| 1.1103 | 190 | 0.1632 | - | - | - | - | - | |
|
| 1.1687 | 200 | 0.1032 | - | - | - | - | - | |
|
| 1.2272 | 210 | 0.1037 | - | - | - | - | - | |
|
| 1.2856 | 220 | 0.0708 | - | - | - | - | - | |
|
| 1.3440 | 230 | 0.0827 | - | - | - | - | - | |
|
| 1.4025 | 240 | 0.0505 | - | - | - | - | - | |
|
| 1.4609 | 250 | 0.0468 | - | - | - | - | - | |
|
| 1.5194 | 260 | 0.0371 | - | - | - | - | - | |
|
| 1.5778 | 270 | 0.049 | - | - | - | - | - | |
|
| 1.6362 | 280 | 0.0527 | - | - | - | - | - | |
|
| 1.6947 | 290 | 0.0316 | - | - | - | - | - | |
|
| 1.7531 | 300 | 0.052 | - | - | - | - | - | |
|
| 1.8115 | 310 | 0.0298 | - | - | - | - | - | |
|
| 1.8700 | 320 | 0.0334 | - | - | - | - | - | |
|
| 1.9284 | 330 | 0.0431 | - | - | - | - | - | |
|
| 1.9869 | 340 | 0.0316 | - | - | - | - | - | |
|
| 1.9985 | 342 | - | 0.8216 | 0.8342 | 0.8397 | 0.8006 | 0.8408 | |
|
| 2.0453 | 350 | 0.0275 | - | - | - | - | - | |
|
| 2.1037 | 360 | 0.0461 | - | - | - | - | - | |
|
| 2.1622 | 370 | 0.0341 | - | - | - | - | - | |
|
| 2.2206 | 380 | 0.0323 | - | - | - | - | - | |
|
| 2.2790 | 390 | 0.0205 | - | - | - | - | - | |
|
| 2.3375 | 400 | 0.0223 | - | - | - | - | - | |
|
| 2.3959 | 410 | 0.0189 | - | - | - | - | - | |
|
| 2.4543 | 420 | 0.0181 | - | - | - | - | - | |
|
| 2.5128 | 430 | 0.0144 | - | - | - | - | - | |
|
| 2.5712 | 440 | 0.0179 | - | - | - | - | - | |
|
| 2.6297 | 450 | 0.0217 | - | - | - | - | - | |
|
| 2.6881 | 460 | 0.016 | - | - | - | - | - | |
|
| 2.7465 | 470 | 0.0143 | - | - | - | - | - | |
|
| 2.8050 | 480 | 0.0193 | - | - | - | - | - | |
|
| 2.8634 | 490 | 0.0183 | - | - | - | - | - | |
|
| 2.9218 | 500 | 0.0171 | - | - | - | - | - | |
|
| 2.9803 | 510 | 0.0195 | - | - | - | - | - | |
|
| 2.9978 | 513 | - | 0.8242 | 0.8350 | 0.8409 | 0.8051 | 0.8413 | |
|
| 3.0387 | 520 | 0.0127 | - | - | - | - | - | |
|
| 3.0972 | 530 | 0.0261 | - | - | - | - | - | |
|
| 3.1556 | 540 | 0.017 | - | - | - | - | - | |
|
| 3.2140 | 550 | 0.0198 | - | - | - | - | - | |
|
| 3.2725 | 560 | 0.0131 | - | - | - | - | - | |
|
| 3.3309 | 570 | 0.0156 | - | - | - | - | - | |
|
| 3.3893 | 580 | 0.0107 | - | - | - | - | - | |
|
| 3.4478 | 590 | 0.0123 | - | - | - | - | - | |
|
| 3.5062 | 600 | 0.0111 | - | - | - | - | - | |
|
| 3.5646 | 610 | 0.0112 | - | - | - | - | - | |
|
| 3.6231 | 620 | 0.0143 | - | - | - | - | - | |
|
| 3.6815 | 630 | 0.013 | - | - | - | - | - | |
|
| 3.7400 | 640 | 0.0105 | - | - | - | - | - | |
|
| 3.7984 | 650 | 0.0126 | - | - | - | - | - | |
|
| 3.8568 | 660 | 0.0118 | - | - | - | - | - | |
|
| 3.9153 | 670 | 0.0163 | - | - | - | - | - | |
|
| 3.9737 | 680 | 0.0187 | - | - | - | - | - | |
|
| **3.9971** | **684** | **-** | **0.8248** | **0.8361** | **0.8405** | **0.8058** | **0.8422** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.12.5 |
|
- Sentence Transformers: 3.1.1 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.1 |
|
- Accelerate: 0.33.0 |
|
- 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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