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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:23175
- loss:TripletLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: The First Trust Nasdaq Bank ETF (FTXO) seeks to replicate the performance
of the Nasdaq US Smart Banks TM Index by investing at least 90% of its assets
in the index's securities. This fund provides exposure to U.S. banking companies,
selecting the most liquid stocks and ranking/weighting them based on factors including
trailing volatility, value (cash flow to price), and growth (price returns). The
index typically holds around 30 liquid U.S. banking companies across retail banking,
loans, and financial services, with an 8% cap on any single holding. The fund
is non-diversified, and the index undergoes annual reconstitution and quarterly
rebalancing.
sentences:
- The iShares Evolved U.S. Media and Entertainment ETF seeks to invest in U.S. listed
common stocks of large-, mid-, and small-capitalization companies within the media
and entertainment sector. Following an "Evolved" approach, the fund selects companies
belonging to the Media and Entertainment Evolved Sector based on economic characteristics
historically correlated with traditional sector definitions. Under normal circumstances,
it allocates at least 80% of its net assets to these stocks, and the fund is non-diversified.
- The Direxion Daily Healthcare Bull 3X Shares (CURE) is an ETF that seeks daily
investment results, before fees and expenses, of 300% (3X) of the daily performance
of the Health Care Select Sector Index. It invests at least 80% of its net assets
in financial instruments designed to provide this 3X daily leveraged exposure.
The underlying index tracks US listed healthcare companies, including pharmaceuticals,
health care equipment and supplies, providers and services, biotechnology, life
sciences tools, and health care technology, covering major large-cap names. CURE
is non-diversified and intended strictly as a short-term tactical instrument,
as it delivers its stated 3X exposure only for a single day, and returns over
longer periods can significantly differ from three times the index's performance.
- The Xtrackers MSCI Emerging Markets Climate Selection ETF seeks to track an emerging
markets index focused on companies meeting specific climate criteria. Derived
from the MSCI ACWI Select Climate 500 methodology, the underlying index selects
eligible emerging market stocks using an optimization process designed to reduce
greenhouse gas emission intensity (targeting 10% revenue-related and 7% financing-related
reductions) and increase exposure to companies with SBTi-approved targets. The
strategy also excludes controversial companies and evaluates companies based on
broader ESG considerations. The fund is non-diversified and invests at least 80%
of its assets in the component securities of this climate-focused emerging markets
index.
- source_sentence: The iShares S&P Small-Cap 600 Value ETF (IJS) seeks to track the
investment results of the S&P SmallCap 600 Value Index, which consists of U.S.
small-capitalization equities exhibiting value characteristics. This index selects
value stocks from the S&P SmallCap 600 using factors such as book value to price,
earnings to price, and sales to price ratios. The fund generally invests at least
80% of its assets in the component securities of its underlying index and may
invest up to 20% in certain futures, options, swap contracts, cash, and cash equivalents.
The underlying index undergoes annual rebalancing in December.
sentences:
- The Global X S&P 500 Risk Managed Income ETF seeks to track the Cboe S&P 500 Risk
Managed Income Index by investing at least 80% of its assets in index securities.
The index's strategy involves holding the underlying stocks of the S&P 500 Index
while applying an options collar, specifically selling at-the-money covered call
options and buying monthly 5% out-of-the-money put options corresponding to the
portfolio's value. This approach aims to generate income, ideally resulting in
a net credit from the options premiums, and provide risk management, though selling
at-the-money calls inherently caps the fund's potential for upside participation.
- The Amplify International Enhanced Dividend Income ETF (IDVO), an actively managed
fund recently updated to include CWP in its name, seeks to provide current income
primarily and capital appreciation secondarily. The fund invests at least 80%
of its assets in dividend-paying U.S. exchange-traded American depositary receipt
(ADR) securities representing companies located outside the U.S., focusing on
high-quality, large-cap constituents from the MSCI ACWI ex USA Index to offer
international equity exposure in a domestic wrapper. It enhances income generation
by opportunistically utilizing a tactical strategy of writing (selling) short-term,
U.S. exchange-traded covered call option contracts on some or all of its individual
holdings, targeting income from both dividends and option premiums. While aiming
for country and sector diversification by selecting approximately 30-50 stocks,
the fund is classified as non-diversified.
- The Strive Emerging Markets Ex-China ETF seeks to track the total return performance
of the Bloomberg Emerging Markets ex China Large & Mid Cap Index. This index comprises
large and mid-capitalization equity securities from 24 emerging market economies,
specifically excluding China. The index is market cap-weighted, includes common
stocks and real estate investment trusts, and is rebalanced quarterly and reconstituted
semi-annually. Under normal circumstances, the fund invests at least 80% of its
assets in these emerging market securities, which may include depositary receipts
representing securities included in the index.
- source_sentence: The Fidelity MSCI Health Care Index ETF (FHLC) seeks to track the
performance of the MSCI USA IMI Health Care 25/50 Index, which represents the
broad U.S. health care sector. The ETF invests at least 80% of its assets in securities
included in this market-cap-weighted index, which captures large, mid, and small-cap
companies across over 10 subsectors. Employing a representative sampling strategy,
the fund aims to correspond to the index's performance. The index incorporates
a 25/50 capping methodology, is rebalanced quarterly, and its broad reach offers
diversification across cap sizes and subsectors, potentially reducing concentration
in dominant large pharma names and increasing exposure to areas like drug retailers
and insurance. The fund is classified as non-diversified.
sentences:
- The SPDR S&P Health Care Equipment ETF (XHE) tracks the equal-weighted S&P Health
Care Equipment Select Industry Index, which is derived from the U.S. total market
and provides exposure to U.S. health care equipment and supplies companies. Employing
a sampling strategy, the fund invests at least 80% of its assets in the index's
securities, which are rebalanced quarterly. While encompassing companies of all
cap sizes, the equal-weight methodology gives XHE a significant small-cap tilt,
offering focused access to this narrow segment as an alternative for investors
seeking to avoid the concentration found in broader, market-cap-weighted healthcare
funds dominated by large pharmaceuticals or service providers.
- The Global X Silver Miners ETF (SIL) seeks to provide investment results that
correspond generally to the price and yield performance of the Solactive Global
Silver Miners Total Return Index. This index is designed to measure the broad-based
equity market performance of global companies primarily involved in the silver
mining industry, including related activities like exploration and refining. The
fund invests at least 80% of its total assets in the securities of this underlying
index and related American and Global Depositary Receipts. The index is market-cap-weighted
and typically comprises 20-40 stocks, while the fund itself is considered non-diversified.
- The Invesco S&P 500 Equal Weight Energy ETF (RSPG) is a large-cap sector fund
tracking an equal-weighted index comprising U.S. energy companies within the S&P
500 Index, classified according to the Global Industry Classification Standard
(GICS). The ETF aims to invest at least 90% of its total assets in securities
from this underlying index, which applies an equal-weighting methodology and rebalances
quarterly. The index also includes a rule to ensure a minimum of 22 constituents,
incorporating the largest energy companies from the S&P MidCap 400 Index if necessary
to meet this count.
- source_sentence: The VictoryShares Top Veteran Employers ETF (VTRN) was designed
to track the Veterans Select Index, focusing on US-listed companies of any market
capitalization that demonstrated support for US military veterans, service members,
and their families primarily through employment opportunities and related policies.
These companies were identified based on various sources like rankings and surveys
and were typically weighted equally in the index. However, this fund is liquidating,
and its last day of trading was October 11, 2021.
sentences:
- The Invesco S&P 500 Equal Weight Industrials ETF (RSPN) tracks an equal-weighted
index of U.S. industrial stocks drawn from the S&P 500 Index, specifically focusing
on companies classified within the industrials sector according to the Global
Industry Classification Standard (GICS). The fund generally invests at least 90%
of its assets in these securities. This equal-weighting scheme offers a non-traditional
approach compared to market-cap weighting, reducing the dominance of large-cap
industrial conglomerates and lowering the portfolio's weighted average market
capitalization. The underlying index is rebalanced on a quarterly basis.
- The SP Funds Dow Jones Global Sukuk ETF (SPSK) is a passively managed fund designed
to track the performance, before fees and expenses, of the Dow Jones Sukuk Total
Return (ex-Reinvestment) Index. This index focuses on U.S. dollar-denominated,
investment-grade sukuk, which are financial certificates similar to bonds, issued
in global markets and structured to comply with Islamic religious law (Sharia)
and its investment principles. Sharia compliance involves screening securities
to exclude businesses such as tobacco, pornography, gambling, and interest-based
finance, and issuers may include international financial institutions and foreign
governments or agencies, including from emerging markets. Under normal circumstances,
the fund attempts to invest substantially all (at least 80%) of its assets in
the index's component securities, which are reconstituted and rebalanced monthly.
The ETF is considered non-diversified.
- The ALUM ETF, part of the USCF ETF Trust, is an actively managed fund utilizing
a proprietary methodology to seek exposure to the price of aluminum through aluminum-based
derivative investments. It primarily invests in aluminum futures but may also
use cash-settled options, forward contracts, options on futures, and other options
traded on US and non-US exchanges. The fund operates through a wholly owned Cayman
Islands subsidiary to avoid issuing K-1 forms and may hold cash, cash equivalents,
or investment grade fixed-income securities as collateral. This non-diversified
fund is currently being delisted, with its last day of trading on an exchange
scheduled for October 11, 2024.
- source_sentence: 'The Sprott Gold Miners ETF (SGDM) seeks to track the performance
of the Solactive Gold Miners Custom Factors Total Return Index. This index focuses
on gold mining companies based in the U.S. and Canada whose shares trade on the
Toronto Stock Exchange, New York Stock Exchange, or NASDAQ. The index employs
a weighting methodology that begins with market capitalization and then adjusts
based on three fundamental factors: higher revenue growth, lower debt-to-equity,
and higher free cash flow yield. The fund is non-diversified and normally invests
at least 90% of its net assets in securities included in this index.'
sentences:
- 'The Sprott Gold Miners ETF (SGDM) seeks to track the performance of the Solactive
Gold Miners Custom Factors Total Return Index. This index focuses on gold mining
companies based in the U.S. and Canada whose shares trade on the Toronto Stock
Exchange, New York Stock Exchange, or NASDAQ. The index employs a weighting methodology
that begins with market capitalization and then adjusts based on three fundamental
factors: higher revenue growth, lower debt-to-equity, and higher free cash flow
yield. The fund is non-diversified and normally invests at least 90% of its net
assets in securities included in this index.'
- The VanEck Biotech ETF (BBH) seeks to replicate the performance of the MVIS® US
Listed Biotech 25 Index, which provides exposure to approximately 25 of the largest
or leading U.S.-listed companies in the biotechnology industry. The fund normally
invests at least 80% of its assets in securities comprising this market-cap-weighted
index. The underlying index includes common stocks and depositary receipts of
firms involved in the research, development, production, marketing, and sale of
drugs based on genetic analysis and diagnostic equipment. While focusing on U.S.-listed
companies, it may include foreign firms listed domestically, and medium-capitalization
companies can be included. Reflecting the index's concentration, the fund is non-diversified
and may have a top-heavy portfolio. The index is reviewed semi-annually.
- The KraneShares Global Carbon Offset Strategy ETF (KSET) was the first US-listed
ETF providing exposure to the global voluntary carbon market. It achieved this
by investing primarily in liquid carbon offset credit futures, including CME-traded
Global Emissions Offsets (GEOs) and Nature-Based Global Emission Offsets (N-GEOs),
which are designed to help businesses meet greenhouse gas reduction goals. Tracking
an index that weighted eligible futures based on liquidity, the fund sought exposure
to the same carbon offset credit futures, typically those maturing within two
years. The ETF was considered non-diversified and utilized a Cayman Island subsidiary.
However, the fund was delisted, with its last day of trading on an exchange being
March 14, 2024.
datasets:
- hobbang/stage1-triplet-dataset
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the [stage1-triplet-dataset](https://huggingface.co/datasets/hobbang/stage1-triplet-dataset) 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 12e86a3c702fc3c50205a8db88f0ec7c0b6b94a0 -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [stage1-triplet-dataset](https://huggingface.co/datasets/hobbang/stage1-triplet-dataset)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("sentence_transformers_model_id")
# Run inference
sentences = [
'The Sprott Gold Miners ETF (SGDM) seeks to track the performance of the Solactive Gold Miners Custom Factors Total Return Index. This index focuses on gold mining companies based in the U.S. and Canada whose shares trade on the Toronto Stock Exchange, New York Stock Exchange, or NASDAQ. The index employs a weighting methodology that begins with market capitalization and then adjusts based on three fundamental factors: higher revenue growth, lower debt-to-equity, and higher free cash flow yield. The fund is non-diversified and normally invests at least 90% of its net assets in securities included in this index.',
'The KraneShares Global Carbon Offset Strategy ETF (KSET) was the first US-listed ETF providing exposure to the global voluntary carbon market. It achieved this by investing primarily in liquid carbon offset credit futures, including CME-traded Global Emissions Offsets (GEOs) and Nature-Based Global Emission Offsets (N-GEOs), which are designed to help businesses meet greenhouse gas reduction goals. Tracking an index that weighted eligible futures based on liquidity, the fund sought exposure to the same carbon offset credit futures, typically those maturing within two years. The ETF was considered non-diversified and utilized a Cayman Island subsidiary. However, the fund was delisted, with its last day of trading on an exchange being March 14, 2024.',
"The VanEck Biotech ETF (BBH) seeks to replicate the performance of the MVIS® US Listed Biotech 25 Index, which provides exposure to approximately 25 of the largest or leading U.S.-listed companies in the biotechnology industry. The fund normally invests at least 80% of its assets in securities comprising this market-cap-weighted index. The underlying index includes common stocks and depositary receipts of firms involved in the research, development, production, marketing, and sale of drugs based on genetic analysis and diagnostic equipment. While focusing on U.S.-listed companies, it may include foreign firms listed domestically, and medium-capitalization companies can be included. Reflecting the index's concentration, the fund is non-diversified and may have a top-heavy portfolio. The index is reviewed semi-annually.",
]
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.*
-->
<!--
## 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
#### stage1-triplet-dataset
* Dataset: [stage1-triplet-dataset](https://huggingface.co/datasets/hobbang/stage1-triplet-dataset) at [a0fb998](https://huggingface.co/datasets/hobbang/stage1-triplet-dataset/tree/a0fb998d4fb2fabe62e38a295f6bbf4a66b70b38)
* Size: 23,175 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 80 tokens</li><li>mean: 148.35 tokens</li><li>max: 211 tokens</li></ul> | <ul><li>min: 80 tokens</li><li>mean: 153.81 tokens</li><li>max: 238 tokens</li></ul> | <ul><li>min: 82 tokens</li><li>mean: 150.74 tokens</li><li>max: 208 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The Invesco Financial Preferred ETF (PGF) seeks to track the ICE Exchange-Listed Fixed Rate Financial Preferred Securities Index, primarily by investing at least 90% of its total assets in the securities comprising the index. The underlying index is market capitalization weighted and designed to track the performance of exchange-listed, fixed rate, U.S. dollar denominated preferred securities, including functionally equivalent instruments, issued by U.S. financial companies. PGF provides a concentrated portfolio exclusively focused on financial-sector preferred securities and is considered non-diversified, holding both investment- and non-investment-grade securities within this focus.</code> | <code>The FlexShares ESG & Climate Investment Grade Corporate Core Index Fund (FEIG) is a passively managed ETF designed to provide broad-market, core exposure to USD-denominated investment-grade corporate bonds. It seeks to track the performance of the Northern Trust ESG & Climate Investment Grade U.S. Corporate Core IndexSM, which selects bonds from a universe of USD-denominated, investment-grade corporate debt with maturities of at least one year. The index employs an optimization process to increase the aggregate ESG score and reduce aggregate climate-related risk among constituent companies, involving ranking firms on material ESG metrics, governance, and carbon risks, while excluding controversial companies and international initiative violators. Weights are also optimized to minimize systematic risk, and the index is rebalanced monthly. Under normal circumstances, the fund invests at least 80% of its assets in the index's securities.</code> | <code>The Pacer Nasdaq-100 Top 50 Cash Cows Growth Leaders ETF (QQQG) seeks to track the Pacer Nasdaq 100 Top 50 Cash Cows Growth Leaders Index, which draws its universe from the Nasdaq-100 Index. Following a rules-based strategy, the fund screens these companies based on average projected free cash flows and earnings over the next two fiscal years, excluding financials, real estate, and those with negative projections. It then ranks identified stocks by their trailing twelve-month free cash flow margins and selects the top 50 names, weighted by price momentum. The portfolio is reconstituted and rebalanced quarterly. Aiming to identify quality growth leaders with strong cash flow generation, the fund seeks to invest at least 80% of assets in growth securities and is non-diversified.</code> |
| <code>The Invesco Financial Preferred ETF (PGF) seeks to track the ICE Exchange-Listed Fixed Rate Financial Preferred Securities Index, primarily by investing at least 90% of its total assets in the securities comprising the index. The underlying index is market capitalization weighted and designed to track the performance of exchange-listed, fixed rate, U.S. dollar denominated preferred securities, including functionally equivalent instruments, issued by U.S. financial companies. PGF provides a concentrated portfolio exclusively focused on financial-sector preferred securities and is considered non-diversified, holding both investment- and non-investment-grade securities within this focus.</code> | <code>The FlexShares ESG & Climate Investment Grade Corporate Core Index Fund (FEIG) is a passively managed ETF designed to provide broad-market, core exposure to USD-denominated investment-grade corporate bonds. It seeks to track the performance of the Northern Trust ESG & Climate Investment Grade U.S. Corporate Core IndexSM, which selects bonds from a universe of USD-denominated, investment-grade corporate debt with maturities of at least one year. The index employs an optimization process to increase the aggregate ESG score and reduce aggregate climate-related risk among constituent companies, involving ranking firms on material ESG metrics, governance, and carbon risks, while excluding controversial companies and international initiative violators. Weights are also optimized to minimize systematic risk, and the index is rebalanced monthly. Under normal circumstances, the fund invests at least 80% of its assets in the index's securities.</code> | <code>The Nuveen Global Net Zero Transition ETF (NTZG) was an actively managed fund that sought capital appreciation by investing in global equity securities. The fund focused on companies positioned to contribute to the transition to a net zero carbon economy through their current or planned efforts to reduce global greenhouse gas emissions. Utilizing bottom-up, fundamental analysis, NTZG invested in a range of companies, including climate leaders, firms with disruptive climate mitigation technologies, and high carbon emitters working towards real-world emissions decline. The fund aimed to align with the Paris Climate Agreement by seeking to lower portfolio carbon intensity annually towards a 2050 net zero goal and engaging with portfolio companies, while excluding companies involved in weapons and firearms and investing globally across market capitalizations with allocations to non-US and emerging markets. **Please note: The security has been delisted, and the last day of trading on an exc...</code> |
| <code>The Invesco Financial Preferred ETF (PGF) seeks to track the ICE Exchange-Listed Fixed Rate Financial Preferred Securities Index, primarily by investing at least 90% of its total assets in the securities comprising the index. The underlying index is market capitalization weighted and designed to track the performance of exchange-listed, fixed rate, U.S. dollar denominated preferred securities, including functionally equivalent instruments, issued by U.S. financial companies. PGF provides a concentrated portfolio exclusively focused on financial-sector preferred securities and is considered non-diversified, holding both investment- and non-investment-grade securities within this focus.</code> | <code>The FlexShares ESG & Climate Investment Grade Corporate Core Index Fund (FEIG) is a passively managed ETF designed to provide broad-market, core exposure to USD-denominated investment-grade corporate bonds. It seeks to track the performance of the Northern Trust ESG & Climate Investment Grade U.S. Corporate Core IndexSM, which selects bonds from a universe of USD-denominated, investment-grade corporate debt with maturities of at least one year. The index employs an optimization process to increase the aggregate ESG score and reduce aggregate climate-related risk among constituent companies, involving ranking firms on material ESG metrics, governance, and carbon risks, while excluding controversial companies and international initiative violators. Weights are also optimized to minimize systematic risk, and the index is rebalanced monthly. Under normal circumstances, the fund invests at least 80% of its assets in the index's securities.</code> | <code>The First Trust Expanded Technology ETF (XPND) is an actively managed fund seeking long-term capital appreciation by investing primarily in US stocks identified as "Expanded Technology Companies." Defined as companies whose operations are principally derived from or dependent upon technology, these include traditional information technology firms as well as tech-dependent companies in other sectors, such as communication services and consumer discretionary (like internet and direct marketing retail). The fund invests at least 80% of its net assets in common stocks of these companies. While concentrated in the information technology sector and considered non-diversified, XPND aims for expanded exposure through a portfolio of around 50 companies selected using a quantitative model based on factors like return on equity, momentum, and free cash flow growth. Portfolio weights are generally market-cap-based within set ranges, and the fund is reconstituted and rebalanced quarterly.</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.COSINE",
"triplet_margin": 0.05
}
```
### Evaluation Dataset
#### stage1-triplet-dataset
* Dataset: [stage1-triplet-dataset](https://huggingface.co/datasets/hobbang/stage1-triplet-dataset) at [a0fb998](https://huggingface.co/datasets/hobbang/stage1-triplet-dataset/tree/a0fb998d4fb2fabe62e38a295f6bbf4a66b70b38)
* Size: 3,010 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 84 tokens</li><li>mean: 152.57 tokens</li><li>max: 214 tokens</li></ul> | <ul><li>min: 70 tokens</li><li>mean: 154.43 tokens</li><li>max: 224 tokens</li></ul> | <ul><li>min: 70 tokens</li><li>mean: 150.04 tokens</li><li>max: 204 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The Global X S&P 500 Risk Managed Income ETF seeks to track the Cboe S&P 500 Risk Managed Income Index by investing at least 80% of its assets in index securities. The index's strategy involves holding the underlying stocks of the S&P 500 Index while applying an options collar, specifically selling at-the-money covered call options and buying monthly 5% out-of-the-money put options corresponding to the portfolio's value. This approach aims to generate income, ideally resulting in a net credit from the options premiums, and provide risk management, though selling at-the-money calls inherently caps the fund's potential for upside participation.</code> | <code>The U.S. Global Technology and Aerospace & Defense ETF is an actively managed ETF seeking capital appreciation by investing in equity securities of companies expected to benefit from national defense efforts. These efforts include technological innovations and the development of products and services related to aerospace, physical, and cybersecurity defense, often in preparation for or in response to domestic, regional, or global conflicts. The fund is non-diversified.</code> | <code>The BlackRock Future Climate and Sustainable Economy ETF (BECO) is an actively managed equity fund focused on the transition to a lower carbon economy and future climate themes. It seeks a relatively concentrated, non-diversified portfolio of globally-listed companies of any market capitalization, investing across multiple subthemes such as sustainable energy, resource efficiency, future transport, sustainable nutrition, and biodiversity. The fund utilizes proprietary environmental criteria, including carbon metrics, and aims to align with the Paris Climate Agreement goals for net-zero emissions by 2050, while excluding certain high-emission industries and companies violating the UN Global Compact. It also attempts to achieve a better aggregate environmental and ESG score than its benchmark, the MSCI ACWI Multiple Industries Select Index. Note that BECO is being delisted, with its last day of trading on an exchange scheduled for August 12, 2024.</code> |
| <code>The Global X S&P 500 Risk Managed Income ETF seeks to track the Cboe S&P 500 Risk Managed Income Index by investing at least 80% of its assets in index securities. The index's strategy involves holding the underlying stocks of the S&P 500 Index while applying an options collar, specifically selling at-the-money covered call options and buying monthly 5% out-of-the-money put options corresponding to the portfolio's value. This approach aims to generate income, ideally resulting in a net credit from the options premiums, and provide risk management, though selling at-the-money calls inherently caps the fund's potential for upside participation.</code> | <code>The U.S. Global Technology and Aerospace & Defense ETF is an actively managed ETF seeking capital appreciation by investing in equity securities of companies expected to benefit from national defense efforts. These efforts include technological innovations and the development of products and services related to aerospace, physical, and cybersecurity defense, often in preparation for or in response to domestic, regional, or global conflicts. The fund is non-diversified.</code> | <code>The iShares Energy Storage & Materials ETF (IBAT) seeks to track the STOXX Global Energy Storage and Materials Index, which measures the performance of equity securities of global companies involved in energy storage solutions, including hydrogen, fuel cells, and batteries, aiming to support the transition to a low carbon economy. Determined by STOXX Ltd., the index selects companies based on their exposure to the theme through revenue analysis and patent assessment, while also applying exclusionary ESG screens. The index is price-weighted, based on market capitalization with capping rules. The fund generally invests at least 90% of its assets in the component securities of its underlying index or substantially identical investments and is considered non-diversified.</code> |
| <code>The Global X S&P 500 Risk Managed Income ETF seeks to track the Cboe S&P 500 Risk Managed Income Index by investing at least 80% of its assets in index securities. The index's strategy involves holding the underlying stocks of the S&P 500 Index while applying an options collar, specifically selling at-the-money covered call options and buying monthly 5% out-of-the-money put options corresponding to the portfolio's value. This approach aims to generate income, ideally resulting in a net credit from the options premiums, and provide risk management, though selling at-the-money calls inherently caps the fund's potential for upside participation.</code> | <code>The U.S. Global Technology and Aerospace & Defense ETF is an actively managed ETF seeking capital appreciation by investing in equity securities of companies expected to benefit from national defense efforts. These efforts include technological innovations and the development of products and services related to aerospace, physical, and cybersecurity defense, often in preparation for or in response to domestic, regional, or global conflicts. The fund is non-diversified.</code> | <code>The Sprott Gold Miners ETF (SGDM) seeks to track the performance of the Solactive Gold Miners Custom Factors Total Return Index. This index focuses on gold mining companies based in the U.S. and Canada whose shares trade on the Toronto Stock Exchange, New York Stock Exchange, or NASDAQ. The index employs a weighting methodology that begins with market capitalization and then adjusts based on three fundamental factors: higher revenue growth, lower debt-to-equity, and higher free cash flow yield. The fund is non-diversified and normally invests at least 90% of its net assets in securities included in this index.</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.COSINE",
"triplet_margin": 0.05
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 3e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: True
- `dataloader_drop_last`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `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
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `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}
- `tp_size`: 0
- `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
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss |
|:----------:|:-------:|:-------------:|:---------------:|
| 0.0069 | 10 | 0.0448 | - |
| 0.0138 | 20 | 0.0354 | - |
| 0.0207 | 30 | 0.0293 | - |
| 0.0276 | 40 | 0.0381 | - |
| 0.0345 | 50 | 0.0228 | - |
| 0.0414 | 60 | 0.0238 | - |
| 0.0483 | 70 | 0.0229 | - |
| 0.0552 | 80 | 0.0148 | - |
| 0.0622 | 90 | 0.0175 | - |
| 0.0691 | 100 | 0.0161 | - |
| 0.0760 | 110 | 0.0124 | - |
| 0.0829 | 120 | 0.0111 | - |
| 0.0898 | 130 | 0.0165 | - |
| 0.0967 | 140 | 0.0162 | - |
| 0.1036 | 150 | 0.0141 | - |
| 0.1105 | 160 | 0.0116 | - |
| 0.1174 | 170 | 0.01 | - |
| 0.1243 | 180 | 0.0134 | - |
| 0.1312 | 190 | 0.0117 | - |
| 0.1381 | 200 | 0.0127 | 0.0131 |
| 0.1450 | 210 | 0.0083 | - |
| 0.1519 | 220 | 0.0116 | - |
| 0.1588 | 230 | 0.0099 | - |
| 0.1657 | 240 | 0.0086 | - |
| 0.1727 | 250 | 0.0099 | - |
| 0.1796 | 260 | 0.0047 | - |
| 0.1865 | 270 | 0.0052 | - |
| 0.1934 | 280 | 0.0086 | - |
| 0.2003 | 290 | 0.0084 | - |
| 0.2072 | 300 | 0.0068 | - |
| 0.2141 | 310 | 0.005 | - |
| 0.2210 | 320 | 0.0077 | - |
| 0.2279 | 330 | 0.0044 | - |
| 0.2348 | 340 | 0.0039 | - |
| 0.2417 | 350 | 0.0058 | - |
| 0.2486 | 360 | 0.0045 | - |
| 0.2555 | 370 | 0.0045 | - |
| 0.2624 | 380 | 0.0064 | - |
| 0.2693 | 390 | 0.0037 | - |
| **0.2762** | **400** | **0.0083** | **0.013** |
| 0.2831 | 410 | 0.0057 | - |
| 0.2901 | 420 | 0.0043 | - |
| 0.2970 | 430 | 0.0028 | - |
| 0.3039 | 440 | 0.0036 | - |
| 0.3108 | 450 | 0.0031 | - |
| 0.3177 | 460 | 0.0072 | - |
| 0.3246 | 470 | 0.0025 | - |
| 0.3315 | 480 | 0.0041 | - |
| 0.3384 | 490 | 0.0049 | - |
| 0.3453 | 500 | 0.0035 | - |
| 0.3522 | 510 | 0.0023 | - |
| 0.3591 | 520 | 0.0043 | - |
| 0.3660 | 530 | 0.0032 | - |
| 0.3729 | 540 | 0.0031 | - |
| 0.3798 | 550 | 0.0039 | - |
| 0.3867 | 560 | 0.0042 | - |
| 0.3936 | 570 | 0.0055 | - |
| 0.4006 | 580 | 0.0041 | - |
| 0.4075 | 590 | 0.0026 | - |
| 0.4144 | 600 | 0.002 | 0.0133 |
| 0.4213 | 610 | 0.0027 | - |
| 0.4282 | 620 | 0.0032 | - |
| 0.4351 | 630 | 0.0025 | - |
| 0.4420 | 640 | 0.0042 | - |
| 0.4489 | 650 | 0.0046 | - |
| 0.4558 | 660 | 0.0011 | - |
| 0.4627 | 670 | 0.0004 | - |
| 0.4696 | 680 | 0.0019 | - |
| 0.4765 | 690 | 0.0034 | - |
| 0.4834 | 700 | 0.0032 | - |
| 0.4903 | 710 | 0.0029 | - |
| 0.4972 | 720 | 0.0038 | - |
| 0.5041 | 730 | 0.0021 | - |
| 0.5110 | 740 | 0.0008 | - |
| 0.5180 | 750 | 0.0015 | - |
| 0.5249 | 760 | 0.0018 | - |
| 0.5318 | 770 | 0.0022 | - |
| 0.5387 | 780 | 0.0006 | - |
| 0.5456 | 790 | 0.0022 | - |
| 0.5525 | 800 | 0.0006 | 0.0160 |
| 0.5594 | 810 | 0.0021 | - |
| 0.5663 | 820 | 0.0013 | - |
| 0.5732 | 830 | 0.0019 | - |
| 0.5801 | 840 | 0.0017 | - |
| 0.5870 | 850 | 0.0008 | - |
| 0.5939 | 860 | 0.0012 | - |
| 0.6008 | 870 | 0.0003 | - |
| 0.6077 | 880 | 0.0009 | - |
| 0.6146 | 890 | 0.001 | - |
| 0.6215 | 900 | 0.0011 | - |
| 0.6285 | 910 | 0.0019 | - |
| 0.6354 | 920 | 0.0009 | - |
| 0.6423 | 930 | 0.0003 | - |
| 0.6492 | 940 | 0.0001 | - |
| 0.6561 | 950 | 0.0019 | - |
| 0.6630 | 960 | 0.0006 | - |
| 0.6699 | 970 | 0.0003 | - |
| 0.6768 | 980 | 0.0005 | - |
| 0.6837 | 990 | 0.0025 | - |
| 0.6906 | 1000 | 0.001 | 0.0154 |
| 0.6975 | 1010 | 0.0009 | - |
| 0.7044 | 1020 | 0.0004 | - |
| 0.7113 | 1030 | 0.0008 | - |
| 0.7182 | 1040 | 0.001 | - |
| 0.7251 | 1050 | 0.0018 | - |
| 0.7320 | 1060 | 0.002 | - |
| 0.7390 | 1070 | 0.0 | - |
| 0.7459 | 1080 | 0.0 | - |
| 0.7528 | 1090 | 0.0003 | - |
| 0.7597 | 1100 | 0.0002 | - |
| 0.7666 | 1110 | 0.0004 | - |
| 0.7735 | 1120 | 0.0004 | - |
| 0.7804 | 1130 | 0.0001 | - |
| 0.7873 | 1140 | 0.0002 | - |
| 0.7942 | 1150 | 0.001 | - |
| 0.8011 | 1160 | 0.0003 | - |
| 0.8080 | 1170 | 0.0003 | - |
| 0.8149 | 1180 | 0.0002 | - |
| 0.8218 | 1190 | 0.0002 | - |
| 0.8287 | 1200 | 0.0 | 0.0179 |
| 0.8356 | 1210 | 0.0006 | - |
| 0.8425 | 1220 | 0.0005 | - |
| 0.8494 | 1230 | 0.0015 | - |
| 0.8564 | 1240 | 0.0009 | - |
| 0.8633 | 1250 | 0.0007 | - |
| 0.8702 | 1260 | 0.0003 | - |
| 0.8771 | 1270 | 0.0003 | - |
| 0.8840 | 1280 | 0.0 | - |
| 0.8909 | 1290 | 0.0 | - |
| 0.8978 | 1300 | 0.0009 | - |
| 0.9047 | 1310 | 0.0011 | - |
| 0.9116 | 1320 | 0.0003 | - |
| 0.9185 | 1330 | 0.0 | - |
| 0.9254 | 1340 | 0.0002 | - |
| 0.9323 | 1350 | 0.0004 | - |
| 0.9392 | 1360 | 0.0004 | - |
| 0.9461 | 1370 | 0.0007 | - |
| 0.9530 | 1380 | 0.0006 | - |
| 0.9599 | 1390 | 0.0006 | - |
| 0.9669 | 1400 | 0.0005 | 0.0167 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.1.0+cu118
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## 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",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
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