|
--- |
|
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:90 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Ownership of NVIDIA Securities Information regarding ownership |
|
of NVIDIA securities required by this item will be contained in our 2023 Proxy |
|
Statement under the caption “Security Ownership of Certain Beneficial Owners and |
|
Management,” and is hereby incorporated by reference. |
|
sentences: |
|
- What are the two operating segments of NVIDIA as mentioned in the text? |
|
- What major factors contributed to the decrease in cash provided by operating activities |
|
in fiscal year 2023? |
|
- Where can information regarding the ownership of NVIDIA securities be found? |
|
- source_sentence: Development and Retention To support employee development, we provide |
|
opportunities to learn on-the-job through training programs, one on one coaching |
|
and ongoing feedback. We have a library of live and on-demand learning experiences |
|
that include workshops, panel discussions, and speaker forums. We curate learning |
|
paths focused on our most common development needs and constantly upgrade our |
|
offerings to ensure that our employees are exposed to the most current programs |
|
and technologies available. |
|
sentences: |
|
- How much is authorized for the repurchase of additional shares of common stock |
|
as of January 29, 2023? |
|
- What position did Timothy S. Teter acquire at NVIDIA in 2018? |
|
- What types of learning opportunities does the company provide to support employee |
|
development? |
|
- source_sentence: Data Center The NVIDIA computing platform is focused on accelerating |
|
the most compute-intensive workloads, such as AI, data analytics, graphics and |
|
scientific computing, across hyperscale, cloud, enterprise, public sector, and |
|
edge data centers. The platform consists of our energy efficient GPUs, data processing |
|
units, or DPUs, interconnects and systems, our CUDA programming model, and a growing |
|
body of software libraries, software development kits, or SDKs, application frameworks |
|
and services, which are either available as part of the platform or packaged and |
|
sold separately. |
|
sentences: |
|
- What position did Colette M. Kress hold before joining NVIDIA in 2013? |
|
- Where can NVIDIA's financial reports be accessed? |
|
- What are the key components of the NVIDIA computing platform? |
|
- source_sentence: Human Capital Management We believe that our employees are our |
|
greatest assets, and they play a key role in creating long-term value for our |
|
stakeholders. As of the end of fiscal year 2023, we had 26,196 employees in 35 |
|
countries, 19,532 were engaged in research and development and 6,664 were engaged |
|
in sales, marketing, operations, and administrative positions. |
|
sentences: |
|
- What industries use NVIDIA's GPUs and software for automation? |
|
- How many employees did the company have at the end of fiscal year 2023, and in |
|
how many countries were they located? |
|
- How does NVIDIA's platform strategy contribute to the markets it serves? |
|
- source_sentence: Equity Compensation Plan Information Information regarding our |
|
equity compensation plans required by this item will be contained in our 2023 |
|
Proxy Statement under the caption "Equity Compensation Plan Information," and |
|
is hereby incorporated by reference. |
|
sentences: |
|
- What amount is recorded as unrecognized tax benefits at the end of fiscal year |
|
2023? |
|
- What is the total amount authorized for the repurchase of common stock up to December |
|
2023? |
|
- What document contains details about NVIDIA's equity compensation plans? |
|
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: 0.6 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26666666666666666 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.2 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.1 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.81232126232897 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.75 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.75 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26666666666666666 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.2 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.1 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8492282869718244 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26666666666666666 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.2 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.1 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.81232126232897 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.75 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.75 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26666666666666666 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.2 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.1 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8492282869718244 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.5 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.6 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.5 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.19999999999999998 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.18 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.5 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.6879135676952786 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.62 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.6283333333333333 |
|
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("anikulkar/bge-base-financial-matryoshka-nvda") |
|
# Run inference |
|
sentences = [ |
|
'Equity Compensation Plan Information Information regarding our equity compensation plans required by this item will be contained in our 2023 Proxy Statement under the caption "Equity Compensation Plan Information," and is hereby incorporated by reference.', |
|
"What document contains details about NVIDIA's equity compensation plans?", |
|
'What is the total amount authorized for the repurchase of common stock up to December 2023?', |
|
] |
|
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 | 0.6 | |
|
| cosine_accuracy@3 | 0.8 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.6 | |
|
| cosine_precision@3 | 0.2667 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.6 | |
|
| cosine_recall@3 | 0.8 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.8123 | |
|
| cosine_mrr@10 | 0.75 | |
|
| **cosine_map@100** | **0.75** | |
|
|
|
#### 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 | 0.7 | |
|
| cosine_accuracy@3 | 0.8 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.7 | |
|
| cosine_precision@3 | 0.2667 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.7 | |
|
| cosine_recall@3 | 0.8 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.8492 | |
|
| cosine_mrr@10 | 0.8 | |
|
| **cosine_map@100** | **0.8** | |
|
|
|
#### 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 | 0.6 | |
|
| cosine_accuracy@3 | 0.8 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.6 | |
|
| cosine_precision@3 | 0.2667 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.6 | |
|
| cosine_recall@3 | 0.8 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.8123 | |
|
| cosine_mrr@10 | 0.75 | |
|
| **cosine_map@100** | **0.75** | |
|
|
|
#### 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 | 0.7 | |
|
| cosine_accuracy@3 | 0.8 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.7 | |
|
| cosine_precision@3 | 0.2667 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.7 | |
|
| cosine_recall@3 | 0.8 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.8492 | |
|
| cosine_mrr@10 | 0.8 | |
|
| **cosine_map@100** | **0.8** | |
|
|
|
#### 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 | 0.5 | |
|
| cosine_accuracy@3 | 0.6 | |
|
| cosine_accuracy@5 | 0.9 | |
|
| cosine_accuracy@10 | 0.9 | |
|
| cosine_precision@1 | 0.5 | |
|
| cosine_precision@3 | 0.2 | |
|
| cosine_precision@5 | 0.18 | |
|
| cosine_precision@10 | 0.09 | |
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| cosine_recall@1 | 0.5 | |
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| cosine_recall@3 | 0.6 | |
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| cosine_recall@5 | 0.9 | |
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| cosine_recall@10 | 0.9 | |
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| cosine_ndcg@10 | 0.6879 | |
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| cosine_mrr@10 | 0.62 | |
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| **cosine_map@100** | **0.6283** | |
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|
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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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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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|
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### Training Dataset |
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|
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#### Unnamed Dataset |
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* Size: 90 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: 22 tokens</li><li>mean: 56.66 tokens</li><li>max: 142 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 19.33 tokens</li><li>max: 32 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>We also offer the NVIDIA GPU Cloud registry, or NGC, a comprehensive catalog of easy-to-use, optimized software stacks across a range of domains including scientific computing, deep learning, and machine learning. With NGC, AI developers, researchers and data scientists can get started with the development of AI and HPC applications and deploy them on DGX systems, NVIDIA-Certified systems from our partners, or with NVIDIA’s cloud partners.</code> | <code>What does the NVIDIA GPU Cloud registry offer?</code> | |
|
| <code>To the extent realization of the deferred tax assets becomes more-likely-than-not, we would recognize such deferred tax assets as income tax benefits during the period.</code> | <code>What will be recognized as income tax benefits if the realization of deferred tax assets becomes more-likely-than-not?</code> | |
|
| <code>Fueled by the sustained demand for exceptional 3D graphics and the scale of the gaming market, NVIDIA has leveraged its GPU architecture to create platforms for scientific computing, AI, data science, AV, robotics, metaverse and 3D internet applications.</code> | <code>How did NVIDIA pivot its GPU architecture usage beyond PC graphics?</code> | |
|
* 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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### 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`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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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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- `tf32`: False |
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- `load_best_model_at_end`: True |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<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`: 32 |
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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`: 16 |
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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`: 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`: 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`: False |
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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 |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
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- `torchdynamo`: None |
|
- `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 |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
|
- `batch_eval_metrics`: 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 | 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 | |
|
|:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 1.0 | 1 | 0.6952 | 0.6617 | 0.725 | 0.5966 | 0.7167 | |
|
| 2.0 | 2 | 0.7060 | 0.75 | 0.8 | 0.6086 | 0.8 | |
|
| 3.0 | 3 | 0.72 | 0.75 | 0.8 | 0.6277 | 0.75 | |
|
| **4.0** | **4** | **0.8** | **0.75** | **0.8** | **0.6283** | **0.75** | |
|
|
|
* The bold row denotes the saved checkpoint. |
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|
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.3.0+cu121 |
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- Accelerate: 0.32.1 |
|
- Datasets: 2.20.0 |
|
- Tokenizers: 0.19.1 |
|
|
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## Citation |
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|
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### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
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@misc{kusupati2024matryoshka, |
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title={Matryoshka Representation Learning}, |
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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}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
|
``` |
|
|
|
#### 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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} |
|
``` |
|
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## Glossary |
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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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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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