|
--- |
|
base_model: BAAI/bge-base-en-v1.5 |
|
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:6300 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: 'The platform offers a number of free services to its members: |
|
access to their credit scores and reports, credit and identity monitoring, credit |
|
report dispute, tools to help understand net worth and make financial progress, |
|
and personalized recommendations of credit card, loan, and insurance products. |
|
Credit Karma Money offers members online savings and checking accounts through |
|
an FDIC member bank partner. Credit Karma Money also provides tools to help members |
|
improve their credit scores.' |
|
sentences: |
|
- What is the mechanism of action for Veklury? |
|
- What services does Credit Karma offer to its members? |
|
- What was the annual amortization expense forecast for acquisition-related intangible |
|
assets in 2025, according to a specified financial projection? |
|
- source_sentence: Vaccine related exit costs of $0.8 billion were reported in the |
|
2023 annual report. |
|
sentences: |
|
- What factors primarily drove the decrease in Veklury's sales in 2023? |
|
- What were the vaccine related exit costs reported by Johnson & Johnson in their |
|
2023 annual report? |
|
- What was the percentage increase in interest income from 2022 to 2023? |
|
- source_sentence: Broadband revenues increased in 2023 by 8.1% driven by an increase |
|
in fiber customers and higher average revenue per user, partially offset by declines |
|
in copper-based broadband services. |
|
sentences: |
|
- What was the percent change in broadband revenues for AT&T in 2023 compared to |
|
2022? |
|
- What factors primarily drove the increase in net cash provided by operating activities |
|
for fiscal 2023? |
|
- How much interest does Chevron hold in the production sharing contract for deepwater |
|
Block 14? |
|
- source_sentence: SEC regulations require the company to disclose certain information |
|
about proceedings arising under federal, state or local environmental regulations |
|
if they reasonably believe that such proceedings may result in monetary sanctions |
|
exceeding $1 million. |
|
sentences: |
|
- What does the term 'Acquired brands' refer to and how does it affect the reported |
|
volumes? |
|
- How many new medicine candidates are currently in clinical development or under |
|
regulatory review? |
|
- Under what conditions are the Company required to disclose certain proceedings |
|
according to SEC regulations? |
|
- source_sentence: 2023 highlights include net revenues of $5,003.3 million which |
|
decreased 15% from $5,856.7 million in 2022. |
|
sentences: |
|
- How did Hasbro's net revenues in 2023 compare to the previous year? |
|
- How much cash did continuing operating activities provide in 2023? |
|
- Which pages of IBM’s 2023 Annual Report provide information on Financial Statements |
|
and Supplementary Data? |
|
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: 0.68 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.81 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8514285714285714 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8942857142857142 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.68 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.27 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17028571428571426 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08942857142857143 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.68 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.81 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8514285714285714 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8942857142857142 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7882073443841624 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7541315192743764 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7584597649275473 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.68 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8028571428571428 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8457142857142858 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8971428571428571 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.68 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2676190476190476 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16914285714285712 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0897142857142857 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.68 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8028571428571428 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8457142857142858 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8971428571428571 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7870684908640463 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7519659863945578 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7559459500178702 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6714285714285714 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7985714285714286 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8457142857142858 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8842857142857142 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6714285714285714 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2661904761904762 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16914285714285712 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08842857142857141 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6714285714285714 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7985714285714286 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8457142857142858 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8842857142857142 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7799432706618373 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7462352607709751 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7505911400077954 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.66 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7914285714285715 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8285714285714286 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8814285714285715 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.66 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2638095238095238 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1657142857142857 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08814285714285712 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.66 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7914285714285715 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8285714285714286 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8814285714285715 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7707461487192945 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7354421768707481 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7395774801009367 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6271428571428571 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7542857142857143 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8014285714285714 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.86 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6271428571428571 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.25142857142857145 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16028571428571428 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08599999999999998 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6271428571428571 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7542857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8014285714285714 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.86 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7403886246637359 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7025532879818592 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7068862427781479 |
|
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) 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:** [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:** |
|
- 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': 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("amichelini/bge-base-financial-matryoshka") |
|
# Run inference |
|
sentences = [ |
|
'2023 highlights include net revenues of $5,003.3 million which decreased 15% from $5,856.7 million in 2022.', |
|
"How did Hasbro's net revenues in 2023 compare to the previous year?", |
|
'How much cash did continuing operating activities provide in 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 | 0.68 | |
|
| cosine_accuracy@3 | 0.81 | |
|
| cosine_accuracy@5 | 0.8514 | |
|
| cosine_accuracy@10 | 0.8943 | |
|
| cosine_precision@1 | 0.68 | |
|
| cosine_precision@3 | 0.27 | |
|
| cosine_precision@5 | 0.1703 | |
|
| cosine_precision@10 | 0.0894 | |
|
| cosine_recall@1 | 0.68 | |
|
| cosine_recall@3 | 0.81 | |
|
| cosine_recall@5 | 0.8514 | |
|
| cosine_recall@10 | 0.8943 | |
|
| cosine_ndcg@10 | 0.7882 | |
|
| cosine_mrr@10 | 0.7541 | |
|
| **cosine_map@100** | **0.7585** | |
|
|
|
#### 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 | 0.68 | |
|
| cosine_accuracy@3 | 0.8029 | |
|
| cosine_accuracy@5 | 0.8457 | |
|
| cosine_accuracy@10 | 0.8971 | |
|
| cosine_precision@1 | 0.68 | |
|
| cosine_precision@3 | 0.2676 | |
|
| cosine_precision@5 | 0.1691 | |
|
| cosine_precision@10 | 0.0897 | |
|
| cosine_recall@1 | 0.68 | |
|
| cosine_recall@3 | 0.8029 | |
|
| cosine_recall@5 | 0.8457 | |
|
| cosine_recall@10 | 0.8971 | |
|
| cosine_ndcg@10 | 0.7871 | |
|
| cosine_mrr@10 | 0.752 | |
|
| **cosine_map@100** | **0.7559** | |
|
|
|
#### 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 | 0.6714 | |
|
| cosine_accuracy@3 | 0.7986 | |
|
| cosine_accuracy@5 | 0.8457 | |
|
| cosine_accuracy@10 | 0.8843 | |
|
| cosine_precision@1 | 0.6714 | |
|
| cosine_precision@3 | 0.2662 | |
|
| cosine_precision@5 | 0.1691 | |
|
| cosine_precision@10 | 0.0884 | |
|
| cosine_recall@1 | 0.6714 | |
|
| cosine_recall@3 | 0.7986 | |
|
| cosine_recall@5 | 0.8457 | |
|
| cosine_recall@10 | 0.8843 | |
|
| cosine_ndcg@10 | 0.7799 | |
|
| cosine_mrr@10 | 0.7462 | |
|
| **cosine_map@100** | **0.7506** | |
|
|
|
#### 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 | 0.66 | |
|
| cosine_accuracy@3 | 0.7914 | |
|
| cosine_accuracy@5 | 0.8286 | |
|
| cosine_accuracy@10 | 0.8814 | |
|
| cosine_precision@1 | 0.66 | |
|
| cosine_precision@3 | 0.2638 | |
|
| cosine_precision@5 | 0.1657 | |
|
| cosine_precision@10 | 0.0881 | |
|
| cosine_recall@1 | 0.66 | |
|
| cosine_recall@3 | 0.7914 | |
|
| cosine_recall@5 | 0.8286 | |
|
| cosine_recall@10 | 0.8814 | |
|
| cosine_ndcg@10 | 0.7707 | |
|
| cosine_mrr@10 | 0.7354 | |
|
| **cosine_map@100** | **0.7396** | |
|
|
|
#### 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 | 0.6271 | |
|
| cosine_accuracy@3 | 0.7543 | |
|
| cosine_accuracy@5 | 0.8014 | |
|
| cosine_accuracy@10 | 0.86 | |
|
| cosine_precision@1 | 0.6271 | |
|
| cosine_precision@3 | 0.2514 | |
|
| cosine_precision@5 | 0.1603 | |
|
| cosine_precision@10 | 0.086 | |
|
| cosine_recall@1 | 0.6271 | |
|
| cosine_recall@3 | 0.7543 | |
|
| cosine_recall@5 | 0.8014 | |
|
| cosine_recall@10 | 0.86 | |
|
| cosine_ndcg@10 | 0.7404 | |
|
| cosine_mrr@10 | 0.7026 | |
|
| **cosine_map@100** | **0.7069** | |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### json |
|
|
|
* Dataset: json |
|
* Size: 6,300 training samples |
|
* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 4 tokens</li><li>mean: 46.33 tokens</li><li>max: 326 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.38 tokens</li><li>max: 43 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>The data includes transaction and integration costs listed as follows for each year: $0, $0, $59, $0, $0, $0, $269, $91, $39, $269, $91, $98.</code> | <code>What were the values of transaction and integration costs for each of the years provided in the data?</code> | |
|
| <code>In 2023, Delta Air Lines announced an increase in remuneration from their partnership with American Express to $6.8 billion, with expected growth of 10% in 2024.</code> | <code>What was the remuneration from Delta Air Lines' partnership with American Express in 2023, and what is the growth expectation for 2024?</code> | |
|
| <code>On December 1, 2023, we advanced $10.0 billion under the ASR program and received approximately 215 million shares of common stock with a value of $6.8 billion, which were immediately retired.</code> | <code>What significant financial activity occurred on December 1, 2023, under the ASR program?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512, |
|
256, |
|
128, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `gradient_accumulation_steps`: 16 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 4 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `bf16`: True |
|
- `tf32`: True |
|
- `load_best_model_at_end`: True |
|
- `optim`: adamw_torch_fused |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 16 |
|
- `eval_accumulation_steps`: None |
|
- `torch_empty_cache_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 4 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `warmup_steps`: 0 |
|
- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
|
- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: True |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: True |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: True |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `eval_use_gather_object`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0 | 0 | - | 0.6648 | 0.6922 | 0.6982 | 0.6028 | 0.7029 | |
|
| 0.8122 | 10 | 1.5362 | - | - | - | - | - | |
|
| 0.9746 | 12 | - | 0.7259 | 0.7402 | 0.7481 | 0.6913 | 0.7510 | |
|
| 1.6244 | 20 | 0.6012 | - | - | - | - | - | |
|
| 1.9492 | 24 | - | 0.7341 | 0.7503 | 0.7554 | 0.7051 | 0.7576 | |
|
| 2.4365 | 30 | 0.4225 | - | - | - | - | - | |
|
| 2.9239 | 36 | - | 0.7383 | 0.7522 | 0.7569 | 0.7063 | 0.7570 | |
|
| 3.2487 | 40 | 0.358 | - | - | - | - | - | |
|
| **3.8985** | **48** | **-** | **0.7396** | **0.7506** | **0.7559** | **0.7069** | **0.7585** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.1.1 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.1+cu121 |
|
- Accelerate: 0.34.2 |
|
- Datasets: 3.0.0 |
|
- 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
