|
--- |
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base_model: BAAI/bge-base-en-v1.5 |
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datasets: [] |
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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 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
|
- cosine_precision@5 |
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- cosine_precision@10 |
|
- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_ndcg@100 |
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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:9000 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: ' and Banking and Securities Services, and that helps us in FX, |
|
in commodities and in rates around the world. So, Markets is important both in |
|
terms of its leadership, but also, how it fits into the strengths that we have |
|
from this simpler Citi of those five core interconnected businesses. We''ve demonstrated |
|
solid returns in the past. I think a lot of the actions we''ve been taking will |
|
help drive returns in the future. And you should be getting confidence when you |
|
see the discipline we''re putting on to Copyright 2024 Citigroup Inc. 14 TRANSCRIPT |
|
Citi Fourth Quarter 2023 Earnings Call January 12, 2024 RWA, 5.3, getting close |
|
that target, we said at Investor Day. We''re moving that up to 6. The exits we''ve |
|
got of nonstrategic businesses shows our focus on efficiency. And we''ve also |
|
been doing some good investments in our technology, and that''s getting us into |
|
a good place there. So' |
|
sentences: |
|
- What are the strengths and importance of Markets in terms of leadership and its |
|
role in the interconnected businesses of Citigroup Inc? |
|
- What are the additional resources available to help assess current finances and |
|
plan for the future? |
|
- ¿Puedo cerrar mi cuenta en cualquier momento y sin restricciones? ¿Qué sucede |
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si mi cuenta tiene un saldo de cero durante 90 días consecutivos? ¿Puedo obtener |
|
copias de cheques cancelados o imágenes de los mismos en mi estado de cuenta? |
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¿Debo llamar a CitiPhone Banking para solicitar las imágenes de los cheques? ¿Existen |
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comisiones adicionales o cargos asociados con esto? ¿Puedo acceder a las imágenes |
|
de los cheques en línea y imprimirlos sin ningún costo adicional en citibankonline.com? |
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- source_sentence: ' legal, investment, or financial advice and is not a substitute |
|
for professional advice. It does not indicate the availability of any Citi product |
|
or service. For advice about your specific circumstances, you should consult a |
|
qualified professional. Additional Resources - ! Insights and Tools Utilize these |
|
resources to help you assess your current finances plan for the future. - ! FICO |
|
Score Learn how FICO Scores are determined, why they matter and more. - ! Glossary |
|
Review financial terms definitions to help you better understand credit finances. |
|
!Back to Top Back to Top !Equal housing lender Contact Us - Consumer: 1-800-347-4934 |
|
- Consumer TTY: 711 - Business: 1-866-422-3091 - Business TTY: 711 - LostStolen: |
|
1-800-950-5114 - LostStolen TTY: 711 About Us - Locations - Careers - Site Map |
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Terms Conditions - Card Member Agreement - Security - Privacy - Notice At Collection |
|
-' |
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sentences: |
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- What are the key steps in the tailor consultative process for wealth advisory |
|
services to create a broad plan for the client's future? |
|
- What are the benefits and program details of the American Airlines AAdvantage |
|
MileUp Card? Can I earn AAdvantage miles for flights, upgrades, car rentals, hotel |
|
stays, or vacation packages? How many AAdvantage miles can I earn at grocery stores, |
|
including grocery delivery services? How many AAdvantage miles can I earn on eligible |
|
American Airlines purchases? How many AAdvantage miles can I earn on all other |
|
purchases? Can I earn loyalty points for eligible mile purchases? How many loyalty |
|
points can I earn? |
|
- What resources are available to help assess current finances and plan for the |
|
future? |
|
- source_sentence: ' Watchlist Alerts . 17 Delivery Settings and Hold Alerts for Brokerage |
|
Alerts . 18 5. Electronic Delivery . 19 Add E-mail Addresses . 19 Set Up e-Delivery |
|
for an Individual Account . 20 3 Set Up e-Delivery for Multiple Accounts using |
|
Quick Enroll. 20 View Statements Reports. 21 View Trade Confirmations. 21 View |
|
Tax Documents . 22 View Notifications . 22 6. Account Portfolio . 24 Overview |
|
. 24 Portfolio Changes . 24 Quick Links . 25 Composition of Holdings . 25 Quick |
|
Trade . 25 Open Orders Executed Trades . 25 Strong Weak Performers . 26 Portfolio |
|
History . 26 News. 27 Balances . 28 Holdings . 29 Non Dollar transactions on Non |
|
US exchanges valued at foreign currency of the exchange . 30 Realized GainLoss |
|
. 32 History . 34 Projected Cash Flow. 35 7. Transact . 36 Trade Equities . ' |
|
sentences: |
|
- What is the track record of the company in managing the risks associated with |
|
its global network and what is its business model focused on? |
|
- What are the watchlist alerts for brokerage alerts and how can electronic delivery |
|
be set up for an individual account and multiple accounts using quick enroll? |
|
How can statements reports, trade confirmations, tax documents, and notifications |
|
be viewed? What is the overview of the account portfolio and how can portfolio |
|
changes, quick links, composition of holdings, quick trades, open orders executed |
|
trades, strong weak performers, portfolio history, news, balances, holdings, non-dollar |
|
transactions on non-US exchanges valued at foreign currency of the exchange, realized |
|
gain/loss, history, and projected cash flow be accessed? How can equities be traded? |
|
- What does the EMV chip do and how does it work? |
|
- source_sentence: . Los productos y servicios mencionados en este documento no se |
|
ofrecen a individuos que residen en la Unin Europea, el Espacio Econmico Europeo, |
|
Suiza, Guernsey, Jersey, Mnaco, Isla de Man, San Marino y el Vaticano. Su elegibilidad |
|
para productos y servicios en particular est sujeta a una decisin definitiva de |
|
nuestra parte. Este documento no es ni debe interpretarse como si fuera una oferta, |
|
invitacin o solicitud para comprar o vender alguno de los productos y servicios |
|
mencionados aqu a tales personas. 2020 Citibank, N.A., Miembro FDIC. Citi, Citi |
|
con el Diseo del Arco y las otras marcas usadas en el presente documento son marcas |
|
de servicio de Citigroup Inc. o sus filiales, usadas y registradas en todo el |
|
mundo. Todos los derechos reservados. IFCBRO-0320SP Treasury |
|
sentences: |
|
- exime Citibank este cargo para cuentas Citigold cheques de diseo estndar para |
|
todas Pedidos de chequeras, cheques oficiales, entrega rpida en el pas de tarjetas |
|
de dbito de reemplazo, giro para clientes, cargos por investigacin y proceso de |
|
verificacin consular o carta de referencia, cumplimiento de proceso legal y servicios |
|
de cobranza. También exime Citibank este cargo para cuentas Citigold en el caso |
|
de canje de cupones de bonos. |
|
- What are the products and services mentioned in this document and where are they |
|
offered? Can individuals residing in the European Union, the European Economic |
|
Area, Switzerland, Guernsey, Jersey, Monaco, Isle of Man, San Marino, and the |
|
Vatican avail these products and services? Is this document an offer, invitation, |
|
or solicitation to buy or sell any of the mentioned products and services to such |
|
individuals? Which organization owns the trademarks and service marks used in |
|
this document? |
|
- How can credit card points be redeemed for cash and what can the cash be used |
|
for? |
|
- source_sentence: ' Drive, Attn: Arbitration Opt Out, San Antonio, TX 78245. Your |
|
rejection notice must be mailed within 45 days of account opening. Your rejection |
|
notice must state that you reject the arbitration provision and include your name, |
|
address, account number and personal signature. No one else may sign the rejection |
|
notice. Your rejection notice will not apply to the arbitration provision governing |
|
any other account that you have or had with us. Rejection of this arbitration |
|
provision wont affect your other rights or responsibilities under this Agreement, |
|
including use of the account. 68 Appendix 1: Fee Schedule The following Checkbook |
|
Order Fee, Safe Deposit Fee, Fee Chart, and Wire Transfer Fee Chart are known |
|
as the Fee Schedule. Unless otherwise stated, all fees described in the Fee Schedule |
|
are charged to the account associated with the product or service. Checkbook Orders. |
|
Fees will be charged for standard and Non-Standard checkbook orders. Non-Standard |
|
Checkbook Orders include non-standard design, non-standard lettering' |
|
sentences: |
|
- How can I start building credit? |
|
- What is the Annual Percentage Yield for the Citigold Private Client Pendant Exclusive |
|
24K Gold Rabbit on the Moon or IL in the states of NY, CT, MD, VA, DC, CA, NV, |
|
NJ and select markets in FL? |
|
- What is the process for rejecting the arbitration provision and what information |
|
should be included in the rejection notice? |
|
model-index: |
|
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5 |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.524 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.718 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7826666666666666 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.848 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.524 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.23933333333333334 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1565333333333333 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08479999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.524 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.718 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7826666666666666 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.848 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.6849393771058847 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_ndcg@100 |
|
value: 0.7108472738066071 |
|
name: Cosine Ndcg@100 |
|
- type: cosine_mrr@10 |
|
value: 0.6327346560846572 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.638367026629088 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on BAAI/bge-base-en-v1.5 |
|
|
|
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("MugheesAwan11/bge-base-citi-dataset-detailed-9k-1_5k-e1") |
|
# Run inference |
|
sentences = [ |
|
' Drive, Attn: Arbitration Opt Out, San Antonio, TX 78245. Your rejection notice must be mailed within 45 days of account opening. Your rejection notice must state that you reject the arbitration provision and include your name, address, account number and personal signature. No one else may sign the rejection notice. Your rejection notice will not apply to the arbitration provision governing any other account that you have or had with us. Rejection of this arbitration provision wont affect your other rights or responsibilities under this Agreement, including use of the account. 68 Appendix 1: Fee Schedule The following Checkbook Order Fee, Safe Deposit Fee, Fee Chart, and Wire Transfer Fee Chart are known as the Fee Schedule. Unless otherwise stated, all fees described in the Fee Schedule are charged to the account associated with the product or service. Checkbook Orders. Fees will be charged for standard and Non-Standard checkbook orders. Non-Standard Checkbook Orders include non-standard design, non-standard lettering', |
|
'What is the process for rejecting the arbitration provision and what information should be included in the rejection notice?', |
|
'What is the Annual Percentage Yield for the Citigold Private Client Pendant Exclusive 24K Gold Rabbit on the Moon or IL in the states of NY, CT, MD, VA, DC, CA, NV, NJ and select markets in FL?', |
|
] |
|
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 | 0.524 | |
|
| cosine_accuracy@3 | 0.718 | |
|
| cosine_accuracy@5 | 0.7827 | |
|
| cosine_accuracy@10 | 0.848 | |
|
| cosine_precision@1 | 0.524 | |
|
| cosine_precision@3 | 0.2393 | |
|
| cosine_precision@5 | 0.1565 | |
|
| cosine_precision@10 | 0.0848 | |
|
| cosine_recall@1 | 0.524 | |
|
| cosine_recall@3 | 0.718 | |
|
| cosine_recall@5 | 0.7827 | |
|
| cosine_recall@10 | 0.848 | |
|
| cosine_ndcg@10 | 0.6849 | |
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| cosine_ndcg@100 | 0.7108 | |
|
| cosine_mrr@10 | 0.6327 | |
|
| **cosine_map@100** | **0.6384** | |
|
|
|
<!-- |
|
## 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 |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 9,000 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: 152 tokens</li><li>mean: 206.96 tokens</li><li>max: 299 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 52.08 tokens</li><li>max: 281 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code> adverse effect on the value of any Index Linked Product. 15 Citi Investment Strategies Citi Flexible Allocation 6 Excess Return Index Index General Conditions Section D: Definitions 16 Citi Investment Strategies Citi Flexible Allocation 6 Excess Return Index Index General Conditions Definitions References to the "applicable Index Methodology" are references to the Index Methodology relating to the relevant Index which together with these Index General Conditions completes the Index Conditions for such Index. References to a "Section" shall be references to a section of these Index General Conditions. References to a "Part" shall be references to a part of the applicable Index Methodology. "Adjustment Event" shall, in respect of a Constituent, have the meaning given to it in the Constituent Schedule applicable to such Constituent. "Affected Constituent" shall have the meaning given to it in Section B . "Affiliate" shall mean, in respect of a person, any entity controlled by such person, any entity which controls</code> | <code>What is the meaning of "applicable Index Methodology" in the Index General Conditions? What does "Section" refer to in the Index General Conditions? How is "Part" defined in the applicable Index Methodology? What is the definition of "Adjustment Event" in relation to a Constituent? How is an "Affected Constituent" defined in Section B? What is the definition of "Affiliate" in relation to a person?</code> | |
|
| <code> that the Depositary andor the Custodian may in the future identify from the balance of Shares on deposit in the DR program as belonging to the holders of DRs in the DR Balance on the basis of a full or partial reconciliation of the Share-to-DR imbalance created by the Automatic Conversions and Forced Conversions. The is no guarantee that any such reconciliation will be successful or that any such Shares will be available any time in the near or distant future, and as a result there is no indication that the DRs credited to the DR balance have, or will in the future have, any value. The creation of the DR Balance and any credit of DRs in the DR balance to a Beneficial Owner is purely an accommodation to the Beneficial Owner and does not represent any undertaking of any value or service. Neither the Depositary nor the Custodian undertake in any way to take any action on behalf of the holders of DRs credited to the DR balance to retrieve any Shares from third parties</code> | <code>What is the likelihood of the Depositary and/or the Custodian successfully reconciling the Share-to-DR imbalance in the DR program and identifying Shares belonging to DR holders in the DR Balance? Is there any guarantee of the availability or future value of these Shares? Are the DRs credited to the DR balance of any value? Does the creation of the DR Balance and credit of DRs to Beneficial Owners represent any commitment of value or service? Do the Depositary and the Custodian have any responsibility to retrieve Shares from third parties on behalf of DR holders credited to the DR balance?</code> | |
|
| <code> of ways to save money while shopping online. Thats why a browser extension like Citi Shop can be a great addition to your online shopping experience. Lets look at how the Citi Shop extension works. Contact helpdeskciti.com What is the Citi Shop Browser Extension? Citi Shop is a free desktop browser extension you can download through the Chrome, Edge or Safari app stores. Once installed, enroll your eligible Citi credit card and let the Citi Shop program automatically search for available offers at more than 5,000 online merchants across the internet. How to Install the Citi Shop Browser Extension First, download the Citi Shop browser extension from the Chrome, Edge or Safari app store for your desktop browser. Once downloaded, you will be required to enroll your eligible Citi credit card. Contact helpdeskciti.com How to Use the Citi Shop Browser Extension Simply shop at your favorite online merchants. The Citi Shop program automatically searches behind the scenes to find money-saving offers percent</code> | <code>What is the Citi Shop Browser Extension and how does it work? How can I install the Citi Shop Browser Extension for my desktop browser? How do I use the Citi Shop Browser Extension to save money while shopping online? Who can I contact for help with the Citi Shop Browser Extension?</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 |
|
], |
|
"matryoshka_weights": [ |
|
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 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 2 |
|
- `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`: 1 |
|
- `eval_accumulation_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 2 |
|
- `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 |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_768_cosine_map@100 | |
|
|:-------:|:-------:|:-------------:|:----------------------:| |
|
| 0.0355 | 10 | 0.7377 | - | |
|
| 0.0709 | 20 | 0.5614 | - | |
|
| 0.1064 | 30 | 0.4571 | - | |
|
| 0.1418 | 40 | 0.2944 | - | |
|
| 0.1773 | 50 | 0.2584 | - | |
|
| 0.2128 | 60 | 0.1855 | - | |
|
| 0.2482 | 70 | 0.1699 | - | |
|
| 0.2837 | 80 | 0.2212 | - | |
|
| 0.3191 | 90 | 0.1827 | - | |
|
| 0.3546 | 100 | 0.1801 | - | |
|
| 0.3901 | 110 | 0.1836 | - | |
|
| 0.4255 | 120 | 0.1112 | - | |
|
| 0.4610 | 130 | 0.1638 | - | |
|
| 0.4965 | 140 | 0.1355 | - | |
|
| 0.5319 | 150 | 0.0873 | - | |
|
| 0.5674 | 160 | 0.1852 | - | |
|
| 0.6028 | 170 | 0.1424 | - | |
|
| 0.6383 | 180 | 0.1467 | - | |
|
| 0.6738 | 190 | 0.1879 | - | |
|
| 0.7092 | 200 | 0.1382 | - | |
|
| 0.7447 | 210 | 0.1358 | - | |
|
| 0.7801 | 220 | 0.0906 | - | |
|
| 0.8156 | 230 | 0.1173 | - | |
|
| 0.8511 | 240 | 0.1196 | - | |
|
| 0.8865 | 250 | 0.1251 | - | |
|
| 0.9220 | 260 | 0.0922 | - | |
|
| 0.9574 | 270 | 0.1344 | - | |
|
| 0.9929 | 280 | 0.0751 | - | |
|
| **1.0** | **282** | **-** | **0.6395** | |
|
| 1.0284 | 290 | 0.166 | - | |
|
| 1.0638 | 300 | 0.0842 | - | |
|
| 1.0993 | 310 | 0.098 | - | |
|
| 1.1348 | 320 | 0.0674 | - | |
|
| 1.1702 | 330 | 0.071 | - | |
|
| 1.2057 | 340 | 0.0527 | - | |
|
| 1.2411 | 350 | 0.0401 | - | |
|
| 1.2766 | 360 | 0.0575 | - | |
|
| 1.3121 | 370 | 0.0418 | - | |
|
| 1.3475 | 380 | 0.054 | - | |
|
| 1.3830 | 390 | 0.0495 | - | |
|
| 1.4184 | 400 | 0.0355 | - | |
|
| 1.4539 | 410 | 0.0449 | - | |
|
| 1.4894 | 420 | 0.0509 | - | |
|
| 1.5248 | 430 | 0.0196 | - | |
|
| 1.5603 | 440 | 0.0634 | - | |
|
| 1.5957 | 450 | 0.0522 | - | |
|
| 1.6312 | 460 | 0.0477 | - | |
|
| 1.6667 | 470 | 0.0583 | - | |
|
| 1.7021 | 480 | 0.0584 | - | |
|
| 1.7376 | 490 | 0.0553 | - | |
|
| 1.7730 | 500 | 0.0358 | - | |
|
| 1.8085 | 510 | 0.0253 | - | |
|
| 1.8440 | 520 | 0.0541 | - | |
|
| 1.8794 | 530 | 0.0488 | - | |
|
| 1.9149 | 540 | 0.0528 | - | |
|
| 1.9504 | 550 | 0.0474 | - | |
|
| 1.9858 | 560 | 0.0311 | - | |
|
| 2.0 | 564 | - | 0.6384 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.14 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 0.32.1 |
|
- Datasets: 2.19.1 |
|
- 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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