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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:53913
- loss:MultipleNegativesRankingLoss
base_model: Alibaba-NLP/gte-multilingual-base
widget:
- source_sentence: How does the monitoring system for well integrity function after
CO2 injection?
sentences:
- 'Drilling is a complex process and delivering a successful well requires identifying
proper technologies and utilizing them efficiently to save time & cost. Today
in Oil & Gas industry there is a huge focus on digital technologies to improve
Drilling Process efficiency and PDO decided to implement an innovative approach
of process optimization by implementing a unique project "electronically Delivering
the Limit (eDtL)".
The overall approach with eDtL project was to implement a platform which can provide
Drilling Operations team the technical limit for all Drilling Activities, which
is the theoretical minimum time required to perform an activity, based on available
knowledge and technology.
eDtL system utilizes rig sensors data transmitted in Real-Time from Drilling Rigs
to automatically detect the Rig Activity and focus on identifying the areas of
Drilling Performance Improvements and minimizing redundant tasks for rig and office
teams. The identified opportunities are communicated with rig team for implementation
and the performance is tracked again to highlight the improvements.
eDtL system also provides capability for continuous improvement of organizational
processes by introducing automation of redundant tasks. One of such improvement
was partial automation of Daily Drilling Report which was historically manually
recorded by rig team daily.'
- 'ADNOC has embarked on a major Carbon Capture and Storage (CCS) project where
large quantities of CO2 are injected into deep saline aquifers for permanent storage
instead of releasing into the atmosphere.
An advanced chemical tracer technology was deployed in the first CCS project in
the UAE for continuous CO2 monitoring to ensure permanent and safe CO2 storage.
In case of containment breach, the chemical tracer technology can confirm the
leakage and identify its source.
After CO2 injection for permanent storage, any containment breaching would be
detected in the shallow soil monitoring borehole. Few soil monitoring boreholes
were excavated across the field in which Capillary Adsorption Tubes (CAT) were
inserted for some time and replaced by another according to the sampling frequency
plan. The tube is sent to the lab for CO2 leak detection and reporting. The high
detection resolution is in the order of 0.1 parts per trillion (ppt). This has
a positive impact on the system economics because smaller quantities of chemical
tracer material are required.
The tracer injection monitoring system is ongoing in the first CO2 storage area
of Abu Dhabi. The monitoring includes soil monitoring which are shallow boreholes.
The soil monitoring boreholes were excavated close to the CO2 injection well to
ensure that there are no well integrity issues developed due to thermal effects
by CO2 injection. The soil monitoring boreholes to be verified by surface gas
CO2 monitors. Soil monitors were located around the radial storage area, to detect
CO2 leakage and to understand CO2 migration to the soil through the cap rock (in
case of leakage). The monitoring system for caprock and well integrity will provide:
Surface soil monitoring for cap rock integrity, integrity confirmation for legacy
wells, integrity confirmation of injection well in the post-injection monitoring
period, leakage quantification, leakage origin if multiple injectors. The monitoring
system can continue for up to 30 years of the operational period as well as the
full post-injection monitoring, measurement and verification horizon.
This paper presents a description of a sophisticated CO2 monitoring technology
that is being deployed in UAE''s first CCS project. CO2 tracer technology is considered
as one of the most accurate methods to detect CO2 leakage at surface. Its high-detection
resolution allows early leakage identification and early mitigation action. In
addition, it proves to be relatively low cost, operationally easy to execute,
and requires a small operational footprint.'
- 'Carbon Capture and Storage, as a solution to mitigate the increase in greenhouse
gases emissions in the atmosphere, is still bringing intensive worldwide R&D activities.
In particular, significant acceleration of in situ CCS experiments supports technical
developments as well as acceptability of this technology. Among the major risks
identified to this technology, wells are often considered to be the weakest spots
with respect to CO2 confinement in the geological reservoir. Therefore, long-term
well integrity performance assessment is one of the critical steps that must be
addressed before large scale CCS technology deployment is accepted as a safe solution
to reduce CO2 emissions.
A risk-based methodology associated with well integrity is proposed within CO2
geological storage. The main objectives of this approach are to identify and quantify
risks associated with CO2 leakages along wells over time (from tens to thousands
of years), to evaluate risks and to propose relevant actions to reduce unacceptable
risks. The methodological framework emphasized the use of the risk concept as
a relevant criterion to (i) evaluate the overall performance of well confinement
with respect to different stakes, (ii) include different levels of uncertainty
associated to the studied system, and (iii) provide a reliable decision making
support. For the quantification of risk, a coupled CO2 flow model (gas flow and
degradation processes) was used to identify possible leakage pathways along the
wellbore and quantify possible CO2 leakage towards sensitive targets (surface,
fresh water, any aquifers…) for different scenarios. This approach offers an operational
response to some of the challenges inherent to well integrity management over
well lifecycle.
This paper focuses on the application of the methodology to a synthetic case based
on an existing well. The practical outcomes and the added values will be presented:
(i) an objective and structured process, (ii) scenarios identification and quantification
of CO2 migration along the wellbore for each scenario, (iii) risk mapping, (iv)
and operational action plans for risk treatment of well integrity.'
- source_sentence: How did the detailed pre-survey planning impact the success of
the offshore seismic acquisition campaign?
sentences:
- 'We showcase an innovative campaigning and business-focused approach to reservoir
monitoring of multiple fields using 4D (time-lapse) seismic. Benefits obtained
in terms of cost, speed and the quality of insights gained are discussed, in comparison
with a piecemeal approach. Challenges and lessons learned are described, with
a view to this approach becoming more widely adopted and allowing 4D monitoring
to be extended to smaller or more marginal fields.
An offshore seismic acquisition campaign was planned and successfully executed
for a sequence of four 4D monitor surveys for fields located within 250 km of
each other on the greater Northwest Shelf of Australia. The four monitors were
acquired in H1 2020 comprising (in this order): Pluto Gas Field M2 (second monitor),
Brunello Gas Field M1 (first monitor), Laverda Oil Field M1 and Cimatti Oil Field
M1.
Cost savings expected from campaigning were realised, despite three cyclones during
operations, with success largely attributed to detailed pre-survey planning. Also
important were the choice of vessel and planning for operational flexibility.
The baseline surveys were diverse and required careful planning to achieve repeatability
between vintages over each field, and to optimise the acquisition sequence – minimising
time required to reconfigure the streamer spreads between surveys. The Cimatti
baseline survey was acquired using a dual-vessel operation; modelling, combined
with now-standard steerable streamers, showed a single-vessel monitor survey was
feasible. These optimisations provided cost savings incremental to the principal
economy of sharing vessel mobilisation costs across the whole campaign.
Both processing and evaluation (ongoing at the time of writing) are essentially
separate per field, but follow a consistent approach. Processing is carried out
by more than one contractor to debottleneck this phase, with products, including
intermediate quality control (QC) volumes, delivered as pre-stack depth migrations.
While full evaluation of the monitor surveys to static and dynamic reservoir model
updates will continue beyond 2020, key initial reservoir insights are expected
to emerge within days of processing completion, with some even earlier from QC
volumes. Furthermore, concurrent 4D evaluations are expected to result in fruitful
exchanges of ideas and technologies between fields.'
- 'Advances in seismic acquisition, processing, computing hardware and theory continue
to enhance seismic-image quality. However, an investment decision on seismic projects
should be based not only technical criteria but a quantifiable expected value
above all currently available field data including well information. This presentation
will include a case history of a major carbonate oil field demonstrating how this
value was estimated before a major reprocessing project and how this value is
being achieved.
This field contains over 1000 wellbore penetrations. A 3D seismic survey was acquired
over the field during 2001–2002, but the reservoir development team believed that
these data to date had added limited value. The motivation for evaluating the
potential for further investment in seismic data was a multi-billion-dollar field-redevelopment
plan.
The Value of Information (VOI) exercise to justify a seismic project began with
an evaluation of technical issues that limited the use of existing seismic data.
Through a targeted fast track reprocessing effort it was determined that the existing
survey had been designed and acquired adequately, and that the deficiencies in
the dataset at the reservoir level are primarily caused by near-surface and overburden
effects. The first-order impact is that mapped seismic surfaces exhibit a "roughness"
primarily from the overlying "non-geologic" noise. There was concern that many
subtle faults interpreted at the reservoir level could be "non-geologic" artifacts
which resulted in reluctance to incorporate these into the reservoir model. Amplitude
balancing issues in the original data precluded quantitative assessments such
as porosity prediction. The targeted reprocessing also verified that existing
algorithms and traditional workflows alone were insufficient to resolve the technical
issues.
Working with the reservoir development team the key business drivers for reprocessing
were identified as follows:
Increase individual well productivity and recovery
Image and define new opportunities in current poor-data areas
Save on well cost by preventing re-drills
Improve overall field development plan
Specific expected value metrics and risks were assigned to the above objectives
and a VOI assessment was completed. It was estimated that successfully achieving
the above business objectives would result in a potential value at least 15 times
the cost of the reprocessing. This resulted in management approval of the full
field reprocessing.
Following completion of the seismic reprocessing, the project team objectively
assessed whether the technical criteria had been achieved and if the business
criteria will be achieved. In both cases the team determined that value metrics
will be met. The reprocessing has impacted drill-wells as well as field development
planning. In addition, the reprocessed seismic data will produce additional potential
value as a result of opportunities not recognized at the start of the project.'
- 'Sabiriyah Mauddud is a giant reservoir in NK under active water flood with about
200 producers and 32 injectors. The reservoir has no aquifer or insignificant
energy support and had been on production since 1960s. Water flood started in
the year 1997, initially with a pilot and later on expanded to the full field
in a phased manner. Initial development was on pattern flood concept with all
vertical injectors & producers which has now been replaced with Produce High-Inject
Low (PHIL) concept using horizontal wells. In light of the significance of this
reservoir for Kuwait''s production, all efforts are made to optimize the performance
of this reservoir. To achieve this objective, Pressure monitoring & performance
analysis is considered to be the backbone of all production as well as injection
activities.
This paper presents the methodology conceived and implemented to assess the reservoir
pressure performance and estimate the current reservoir pressure in different
segments/ blocks in an innovative way so as to maximize the value of "Water flooding"
in North Kuwait area along with the meeting of production aspirations using ESP
as artificial lift system in an optimized manner. Except the RFT data in newly
drilled wells, the availability of pressure data was limited during recent past,
making it necessary to integrate all the available information so as to build
a powerful tool to be used for water flood monitoring. All available information
– Repeat Formation Tester "RFT", Static Bottom Hole Pressure "SBHP" and Pump intake
pressure "PIP" under dynamic and static conditions, were collected & analyzed.
An initial study related to compartmentalization showed two main areas, north
and south, based on comprehensive analysis of all the pressure points. The analysis
also helped for identifying areas with good vertical connectivity and understanding
segments with vertical barriers matching with the geological description. In order
to have the latest pressure mapping, data were combined to have an integrated
imagery of the pressure distribution across the reservoir. During the exercise,
"Gaps" were identified which were filled in by the intake pressure live data as
well as shut in data to have a meaningful mimic of the reservoir pressure to help
the ongoing production as well as injection activities.
Based on the innovative approach as above, surveillance plan has been made to
further enhance the quality of the mapping. Several maps such as opportunity map;
PVT properties map; layer wise pressure maps etc. have been generated for ready-to-use
information to facilitate daily operations.
The objective of the paper is to share the innovative, simple, smart and very
useful approach adopted by North Kuwait to manage the giant Mauddud. This paper
presents the methodology conceived and implemented to assess the reservoir pressure
performance and estimate the current reservoir pressure in difference segments/
blocks in an innovative way so as to maximize the value of "Water flooding" in
North Kuwait area along with the meeting the production aspirations using ESP
as artificial lift system in an optimized manner.'
- source_sentence: What are the primary recovery techniques used in oil and gas extraction?
sentences:
- The extraction of oil and gas involves various techniques to enhance recovery
rates. Primary recovery relies on the natural pressure of the reservoir, while
secondary recovery techniques such as water flooding and gas injection are employed
to increase output after primary methods become inefficient. Tertiary recovery
methods, also known as enhanced oil recovery (EOR), use thermal, gas, or chemical
injection to further improve extraction rates. Each method comes with its own
cost implications and efficiency rates, which can significantly affect the overall
economics of an oilfield development project.
- 'In this paper one of the areas of conflicts observed with the performance of
horizontal wells standoff with respect to development of thin oil rim reservoirs
is examined.
In a technical paper as part of the critical review of literature on the exploitation
of thin oil rim reservoirs with large gas cap and aquifer, this author had highlighted
the problem. As part of sensitives in horizontal well standoff, Cosmos and Fatoke
(2004) tested three positions; one-third, centre and two-third positions from
the GOC in a Niger Delta field. They concluded that the landing closest to the
GOC (one-third position) yielded lowest Oil compared to the centre and two-third
positions. Surprisingly the work done by Sai Garimella et al (2011) in a 60ft
Ghariff & Al Khlata shallow marine low permeability sandstone reservoirs in a
field in Oman showed a different result with the one-third position indicating
an optimum recovery from a horizontal well. Interestingly both authors positions
on the performance had support from other authors.
This study used a 3D reservoir model, investigated different horizontal well standoff
performances and applied permeability reduction to simulate different reservoir
quality. The objective was to see if the reservoir quality was a factor in the
different horizontal well standoff performance seen from different regions of
the world while noting their different depositional environments. Results from
the investigation is presented in this paper and shows a different trend from
both authors mentioned above.'
- The oil extraction process typically involves drilling a well into the earth's
crust where oil deposits are located. The well is often lined with casing to prevent
collapse and water intrusion. Once the well is drilled, various techniques such
as primary recovery, secondary recovery, and tertiary recovery can be employed.
Primary recovery uses natural reservoir pressure to extract oil, while secondary
recovery employs water or gas injection to maintain pressure. Tertiary recovery,
also known as enhanced oil recovery, uses techniques like thermal injection or
chemicals to further reduce the viscosity of oil and increase extraction rates.
Each of these methods has distinct implications on the yield and economic viability
of oil extraction operations.
- source_sentence: What advantages do helicopters have over fixed-wing aircraft for
leak detection surveys?
sentences:
- The reservoir characteristics such as porosity and permeability are crucial for
evaluating the potential of oil and gas fields. Porosity refers to the void spaces
within rocks that can hold hydrocarbons, while permeability measures how easily
fluids can flow through rock formations. These two properties significantly influence
the extraction methods used and the overall productivity of a reservoir. Enhancing
permeability through hydraulic fracturing has become a common technique in unconventional
resource extraction, allowing for more efficient recovery of oil and gas from
low-permeability reservoirs.
- 'BP gas production operations in North America manages over 15,000 miles of onshore
pipelines that make up our vast, complex, and aging gas gathering networks. Surveying
these for leaks presents a huge resource challenge using current ground based
technology and, in turn, impacts the assurance of the safety and integrity of
these operations.
The Exploration and Production Technology Group evaluated new leak detection technologies
using laser, thermal imaging camera and a high speed gas sampling detector that
were deployed on aircraft and used global positioning systems coordinates to survey
gas gathering pipelines. Field trials on gas gathering systems in the North Texas,
Anadarko asset showed that the laser and gas sampling based leak detection systems
were the most accurate, but the video imaging from the thermal camera made a powerful
statement. Helicopters proved to be more suitable in leak detection surveys on
gas gathering pipelines than that of fixed-wing aircraft.
The aerial leak detection technologies produce a significant increase in efficiency
and productivity in managing the integrity of BP''s gas gathering systems. While
that improves business performance, perhaps more importantly is the fact that
small gas leaks can be easily found before they become big ones. That reduces
environmental damage and the potential for leaks to impact the public. The development
and implementation of aerial leak detection in BP is being recognized as an integrity
tool in providing a significantly improved integrity assurance to its gas gathering
operations.'
- 'One of prerequisite of any detection system is to get the requirement the risk
analysis that estimates mainly the safety and environmental impacts of a loss
of containment. From this prerequisite it is possible to consider a strategy for
an early detection of a loss of containment, and to choose a method or a technology.
Methods of detection belong to two main families:
External based Leak Detection System which used local leak sensors to generate
a leak alarm. The main External based Leak Detection Systems are acoustic emission
detectors, pressure detectors, fiber optic cable, vapor and / or liquid sensing
cables;
Internal based Leak Detection Systems which used normal field sensors (e.g. pressure
transmitters, flowmeters) for leak detection and leak localization. The main internal
Leak Detection Systems are:
○
balancing systems (line balance, volume balance, compensated mass balance etc.);
○
Real Time Transient Model;
○
pressure/ flow monitoring;
○
statistical analysis…
The main External based Leak Detection Systems was studied internally through
different evaluation and development programs and for some of them in operation.
The main findings were the followings:
The acoustic based detection is sensitive to external noises as well as some pipeline
fluid (multi-phase, critical flow, transit phase) and pipeline elements (e.g.
elbows, valves). This technology requires the management of high quantity of data,
a significant tuning period, and many sensors connected to the pipeline. Distributed
Acoustic Sensing (DAS) using the fiber optic cable media is currently used internally
to detect real time intrusion.
The pressure emission detectors may be insensitive and require accurate pressure
measurement. This technology is difficultly practical on short lines, gas or multi-phase
pipelines with transient phases.
The vapor / liquid sensing cable technology needs to be physically close to the
pipe to become wet in case of leakage. These sensitive cables should be replaced
or cleaned after a leak. This technology is ne suitable easily for long distance
application. Their retrievable capability with the implementation of pulling chamber
every few hundred meters needs to be carefully considered. In addition, this technology
is highly sensitive. This implies that false alarms may occurred in case of former
contamination (presence of hydrocarbon). This technology is also sensitive to
the soil disruption, fluid properties and is affected by the ageing (sensitive
polymer alteration). However, this technology is suitable for short distance and
for some leaks detection when there is no temperature variation between the fluid
and the soil.
The Fiber optic solution was highly considered for a leak detection through several
evaluation programs and, in particular two PIT (Projet d’Innovation Technologique)
Projects. These two PIT projects were performed between 2015 and 2019 and presented
to the following ADIPEC sessions
○
(Baque, 2017) 2017 Abu Dhabi International Petroleum Exhibition & Conference SPE-188669-MS
Early Gas Detection
○
(Baque, 2020) 2020 Abu Dhabi International Petroleum Exhibition & Conference SPE-203293-MS
Fiber Optic Liquid Leakage Detection
Note: Some of the paragraph parts of this manuscript are extracted from these
two SPE documents referred (Baque, 2017) and (Baque, 2020). Other evaluation and
development programs not presented previously are also presented in this manuscript.'
- source_sentence: What occupational health hazards are anticipated with large construction
projects during the energy transition?
sentences:
- "institutionalized political structures to realize particular social objectives\
\ or serve particular\nconstituencies. \n**Non-hazardous waste:** Waste, other\
\ than Hazardous waste, resulting from company\noperations, including process\
\ and oil field wastes disposed of, on site or off site, as well as\noffice, commercial\
\ or packaging related wastes [ENV-7]. \n**Normalization:** The ratio of a quantitative\
\ indicator output (e.g. emissions) to an\naggregated measure of another output\
\ (e.g. oil and gas production or refinery throughput) \n[Module 1 _Reporting\
\ process_ ]. \n**Occupational illness:** An Employee or Contractor health condition\
\ or disorder requiring\nmedical treatment due to a workplace Incident, typically\
\ involving multiple exposures to\nhazardous substances or to physical agents.\
\ Examples include noise-induced hearing loss,\nrespiratory disease, and contact\
\ dermatitis [SHS-3]. \n**Occupational injury:** Harm of an Employee or Contractor\
\ resulting from a single\ninstantaneous workplace incident that results in medical\
\ treatment (beyond simple first aid),\nwork restrictions, days away from work\
\ (lost time) or a Fatality [SHS-3]. \n**Operating area:** An area where business\
\ activities take place with potential to interact with\nthe adjacent environment\
\ [ENV-4]. \n**Operation:** A generic term used to denote any kind of business\
\ activity involving productrelated processes, such as production, manufacturing\
\ and transport. Note: the term oil and\ngas operations used in the Guidance is\
\ intended to be broad and inclusive of other types of\nproduct, such as chemicals.\
\ \n**7.5**"
- "The broader work of the Directorate is carried out \nby its four standing committees.\
\ \nSafety Committee: This committee’s objective is the \ncore of the Directorate:\
\ to eliminate fatalities and \ncatastrophic process safety events in our industry.\n\
In pursuit of this aim, the committee develops\nand promotes the adoption of recommended\n\
practices – a task it performs both on its own and\nwith partners and trade associations.\
\ The resulting\npublications lay a foundation for both safety\nand efficiency,\
\ and develops the motivated and\nempowered workforce needed to provide the world\n\
with clean, affordable energy. \nHighlights of the committee’s 2023 activities\n\
include participation in events, the creation\nof expert groups, issuing of publications,\
\ and\nengagement in data reviews. \n- Events: In 2023, in addition to the regular\n\
committee and subcommittee meetings,\nthe committee held diving workshops in\n\
Rio De Janeiro and Paris. These meetings\nchampioned local stakeholders and sought\n\
to improve local diving performance. It also\nhosted two Aviation Procurement\
\ Managers\nForums – one in London, the other in Houston \n– to address industry\
\ contracting behaviours \nand its impact on contractor resilience and\nsafety.\
\ In addition, the committee conducted a\nProcess Safety Workshop at the IOGP\
\ Summit\nin Indonesia. Finally, at this year’s Offshore\nEurope conference, IOGP\
\ Safety Director Steve\nNorton moderated a panel on learning from, and\nsharing,\
\ safety lessons. \n- Expert Groups: The committee established\nthree expert\
\ groups in 2023: two to revise\nexisting Reports (365 on land transportation\n\
safety and 365-12 on in-vehicle monitoring), and\none to consider adoption and\
\ implementation of\nrecommended safety practices. \n- Publications: The committee\
\ issued ten \nguidance documents in 2023, covering critical\nareas such as diving,\
\ aviation, and process\nsafety; see page 34 for a full list of publications.\
\ \n- Data reviews: The committee published its\nannual compilations of safety\
\ performance data,\ncovering occupational, process, aviation, and\nland transportation\
\ safety. IOGP has collected\nsafety performance data from its Members\nsince\
\ 1985 and our database is the largest in\nthe upstream industry, providing companies\n\
with valuable information for benchmarking and\nperformance improvement. \n17"
- "endotoxins and fungi. The authors recommended that\nongoing real–time measurement\
\ of these exposures be\ncarried out to identify boundary conditions, phases,\
\ and\nsettings with the highest pollutant release. \n12 — Health in the energy\
\ transition \nGood quality studies are needed on the health effects of\nrenewable\
\ energy sources. Such studies should include\npopulations and patients with well-characterized\
\ exposure,\nhigh-quality information on outcome, and assessment of\npotential\
\ confounders. While retrospective (e.g., case-control)\nstudies might produce\
\ useful results, prospective longitudinal\nstudies would provide the strongest\
\ evidence. \nSeveral LCA studies have been conducted for the different\ntechnologies.\
\ These LCAs reported relative low levels of\nemissions during the lifecycle of\
\ renewable sources of\nenergy. Few of these studies included a comparison with\n\
fossil-based technologies. When more life cycle studies\nbecome available it would\
\ be important to include them\nin the literature review. While looking at the\
\ life cycle of a\ncertain technology, other health effects in the value chain\n\
could potentially be identified (reference: UNECE on Carbon\nNeutrality in the\
\ UNECE Region: Integrated Life-cycle\nAssessment of Electricity Sources). \n\
As of December 2024, very few occupational and public\nhealth hazards specific\
\ to energy transition technologies\nhave been identified. The energy transition\
\ is in an early stage\nand will evolve quickly, and additional hazards unique\
\ to\nenergy transition activities may emerge; the specifics of this\nare, at\
\ this time, uncertain. \nWhat is certain is that the energy transition will\
\ involve large\nconstruction projects whose risks (and effective methods to\n\
manage those risks) are well-known and understood. Existing\noccupational health\
\ approaches will be able to manage\nthese risks effectively, provided the correct\
\ assessments are\nconducted properly."
datasets:
- Sampath1987/offshore_energy_v1
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
results:
- task:
type: triplet
name: Triplet
dataset:
name: ai job validation
type: ai-job-validation
metrics:
- type: cosine_accuracy
value: 0.9713607430458069
name: Cosine Accuracy
---
# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) on the [offshore_energy_v1](https://huggingface.co/datasets/Sampath1987/offshore_energy_v1) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9bbca17d9273fd0d03d5725c7a4b0f6b45142062 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [offshore_energy_v1](https://huggingface.co/datasets/Sampath1987/offshore_energy_v1)
<!-- - **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': 8192, 'do_lower_case': False, 'architecture': 'NewModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(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("Sampath1987/EnergyEmbed-v2-e2")
# Run inference
sentences = [
'What occupational health hazards are anticipated with large construction projects during the energy transition?',
'endotoxins and fungi. The authors recommended that\nongoing real–time measurement of these exposures be\ncarried out to identify boundary conditions, phases, and\nsettings with the highest pollutant release. \n12 — Health in the energy transition \nGood quality studies are needed on the health effects of\nrenewable energy sources. Such studies should include\npopulations and patients with well-characterized exposure,\nhigh-quality information on outcome, and assessment of\npotential confounders. While retrospective (e.g., case-control)\nstudies might produce useful results, prospective longitudinal\nstudies would provide the strongest evidence. \nSeveral LCA studies have been conducted for the different\ntechnologies. These LCAs reported relative low levels of\nemissions during the lifecycle of renewable sources of\nenergy. Few of these studies included a comparison with\nfossil-based technologies. When more life cycle studies\nbecome available it would be important to include them\nin the literature review. While looking at the life cycle of a\ncertain technology, other health effects in the value chain\ncould potentially be identified (reference: UNECE on Carbon\nNeutrality in the UNECE Region: Integrated Life-cycle\nAssessment of Electricity Sources). \nAs of December 2024, very few occupational and public\nhealth hazards specific to energy transition technologies\nhave been identified. The energy transition is in an early stage\nand will evolve quickly, and additional hazards unique to\nenergy transition activities may emerge; the specifics of this\nare, at this time, uncertain. \nWhat is certain is that the energy transition will involve large\nconstruction projects whose risks (and effective methods to\nmanage those risks) are well-known and understood. Existing\noccupational health approaches will be able to manage\nthese risks effectively, provided the correct assessments are\nconducted properly.',
'institutionalized political structures to realize particular social objectives or serve particular\nconstituencies. \n**Non-hazardous waste:** Waste, other than Hazardous waste, resulting from company\noperations, including process and oil field wastes disposed of, on site or off site, as well as\noffice, commercial or packaging related wastes [ENV-7]. \n**Normalization:** The ratio of a quantitative indicator output (e.g. emissions) to an\naggregated measure of another output (e.g. oil and gas production or refinery throughput) \n[Module 1 _Reporting process_ ]. \n**Occupational illness:** An Employee or Contractor health condition or disorder requiring\nmedical treatment due to a workplace Incident, typically involving multiple exposures to\nhazardous substances or to physical agents. Examples include noise-induced hearing loss,\nrespiratory disease, and contact dermatitis [SHS-3]. \n**Occupational injury:** Harm of an Employee or Contractor resulting from a single\ninstantaneous workplace incident that results in medical treatment (beyond simple first aid),\nwork restrictions, days away from work (lost time) or a Fatality [SHS-3]. \n**Operating area:** An area where business activities take place with potential to interact with\nthe adjacent environment [ENV-4]. \n**Operation:** A generic term used to denote any kind of business activity involving productrelated processes, such as production, manufacturing and transport. Note: the term oil and\ngas operations used in the Guidance is intended to be broad and inclusive of other types of\nproduct, such as chemicals. \n**7.5**',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5659, 0.2068],
# [0.5659, 1.0000, 0.1987],
# [0.2068, 0.1987, 1.0000]])
```
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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Triplet
* Dataset: `ai-job-validation`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9714** |
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## Training Details
### Training Dataset
#### offshore_energy_v1
* Dataset: [offshore_energy_v1](https://huggingface.co/datasets/Sampath1987/offshore_energy_v1) at [4e9339c](https://huggingface.co/datasets/Sampath1987/offshore_energy_v1/tree/4e9339cce67dbff9d1e6ba25cfdcd9a4a7f529f7)
* Size: 53,913 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: 14 tokens</li><li>mean: 23.77 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 392.08 tokens</li><li>max: 961 tokens</li></ul> | <ul><li>min: 45 tokens</li><li>mean: 389.63 tokens</li><li>max: 1109 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What statistical methods were employed to enhance the accuracy of comparisons in the field testing of shaped cutters?</code> | <code>As shaped polycrystalline diamond compact (PDC) cutter geometries become more prevalent across the industry, this paper statistically reviews field testing of novel shaped PDC cutters in a variety of challenging applications. Firstly, the paper identifies the improvement in efficiency when compared with conventional PDC cutter geometries. Secondly, it confirms the reliability and robustness of the aforementioned shaped cutter geometries.<br>After several years of field testing shaped PDC cutter geometries, the question of how they hold up against conventional cylinder-shaped cutters remains unanswered. This study looks at drill bits that have the same overall design; however, each bit has different shape configurations that are deployed in a range of hole sizes and drilling applications. Data was collected from more than 100 runs and included advanced dull evaluation techniques, data mining, and comparative analyses. During data collation and interpretation, several statistical methods we...</code> | <code>This paper details the improvements to drilling performance and torsional response of fixed cutter bits when changing from a conventional 19-mm cutter diameter configuration to 25-mm cutter diameters for similar blade counts in two different hole sizes. Key performance metrics include rate of penetration (ROP), rerun-ability, torsional response, and ability to maintain tool-face control during directional drilling.<br>A high-performance drilling application was selected with several existing offset wells using a 12¼-in., five-bladed, 19-mm (519) drill bit design, and a concept bit developed using 25-mm diameter cutters while maintaining comparable ancillary features. This was tested in the same field on both vertical and S-shape sections using the same bent-housing motor assembly and drilling performance compared to the existing offsets. A 17½-in. hole size application that experiences high drillstring vibration was also selected, and a 25-mm cutter diameter drill bit was designed with co...</code> |
| <code>What are vapor recovery units (VRU) used for in oil and gas operations?</code> | <code>## 4. Vapour recovery units <br>Vapor recovery units (VRU) are used to prevent emissions by capturing the streams and<br>re-routing them either back to the process or for use as fuel. More details on the<br>components, installation, and operation of VRU are captured in the following sections.</code> | <code>##### 3.1.2 Reduction and recovery of glycol dehydration flash gas <br>Gas from the flash vessel will consist primarily of hydrocarbons and is continuously<br>produced. If installed, a flash vessel will typically remove 90% or more of the entrained<br>hydrocarbon gas and dissolved gases in the glycol leaving the contactor column. <br>Glycol flash vessels typically operate at 3-7 barg [18], meaning there is generally a sufficient<br>pressure drop for the flash gas to commonly be routed to flare or a low-pressure fuel gas<br>system. If the composition of the flash gas prevents this, or there is no fuel gas system,<br>then a Vapour Recovery Unit (VRU) may be needed for recovery into other process units. <br>Minimization of the flash gas itself is also possible by optimizing the glycol flowrate,<br>such as by adjusting the dry gas water temperature specification based on accurate site<br>conditions because the water dew point needed could vary seasonally or from site to<br>site by using more accurate ambient temperatur...</code> |
| <code>What challenges are posed by fractures and faults in the completion of MRC wells?</code> | <code>The Maximum Reservoir Contact (MRC) concept was developed to improve well productivity and sustainability by maximizing the contact area with target reservoirs. MRC is a proven technology for the development of tight/non-economical reservoirs. Completion design for MRC wells plays a vital role in enhancing well deliverability, monitoring and accessibility.<br>MRC technology was put into application to appraise a tight and thin heterogeneous carbonate reservoir in a giant offshore field in Abu Dhabi. Different completion scenarios were simulated to select the best suited completion to achieve enhanced well deliverability, monitoring and accessibility.<br>Heavy casing design with liner and tie-back system was finalized to maximize accessibility and achieve proper isolation behind casing. A special pre-perforated liner was also designed to eliminate the pressure drop across the wellbore. The MRC drain was divided mainly into two sections, blank pipe and pre-perforated liner equipped with swell ...</code> | <code>The Clair field is the largest discovered oilfield on the UK continental shelf (UKCS) but has high reservoir uncertainty associated with a complex natural fracture network. The field area covers over 200 sq km with an estimated STOIIP of 7 billion barrels. The scale and complexity of the reservoir has led to a phased multi-platform development.<br>Phase 1 started production in 2005 with 20 wells drilled prior to an extended drill break. Five new wells (A21 to A25) were drilled and brought online during 2016/17 which increased platform production by c.70%. The new wells incorporated historic lessons to mitigate the risk of wellbore instability in the overburden and be robust to the dynamic uncertainties of the fractured reservoir. Many of the well outcomes and risk events were predicted and mitigated effectively, however the new wells still provided some surprises.<br>This paper presents a summary of the lessons from the historic Clair development wells which underpinned the recent drilling c...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Evaluation Dataset
#### offshore_energy_v1
* Dataset: [offshore_energy_v1](https://huggingface.co/datasets/Sampath1987/offshore_energy_v1) at [4e9339c](https://huggingface.co/datasets/Sampath1987/offshore_energy_v1/tree/4e9339cce67dbff9d1e6ba25cfdcd9a4a7f529f7)
* Size: 6,739 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: 11 tokens</li><li>mean: 23.56 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 55 tokens</li><li>mean: 386.01 tokens</li><li>max: 1082 tokens</li></ul> | <ul><li>min: 45 tokens</li><li>mean: 382.6 tokens</li><li>max: 1175 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is the importance of quantifying carbon emissions during cementing operations in decarbonization?</code> | <code>An important step in decarbonization is using an end-to-end approach to quantify carbon emissions during cementing operations. By careful analysis of the entire cementing operations process, it is then possible to measure and compare carbon emissions at various stages of the operation. Understanding and isolating the main drivers of the carbon emissions footprint enables making better choices and developing best alternatives with lower environmental impact.<br>The methodology considers the lifecycle assessment of cement from quarry extraction to well abandonment, and includes steps such as manufacturing of raw materials, transportation and logistics, and operations in the field. For these stages, careful quantification of emissions is performed based on the manufacturer's carbon emissions of cementing products, transportation (distance and means) to the bulk plant and rig site, and equipment-related emissions such as blending and pumping units. In some cases, when assessing the footprint ...</code> | <code>Objectives/Scope<br>There are many different views on the Energy Transition. What is agreed is that to achieve current climate change targets, the journey to deep decarbonisation must start now. Scope 3 emissions are clearly the major contributor to total emissions and must be actively reduced. However, if Oil and Gas extraction is to be continued, then operators must understand, measure, and reduce Scope 1 and 2 emissions. This paper examines the constituent parts of typical Scope 1 emissions for O&G assets and discusses a credible pathway and initial steps towards decarbonisation of operations.<br>Methods, Procedures, Process<br>Emissions from typical assets are investigated: data is examined to determine the overall and individual contributions of Scope 1 emissions. A three tiered approach to emissions savings is presented:<br>–<br>Reduce overall energy usage<br>–<br>Seek to Remove environmental losses<br>–<br>Replace energy supply with low carbon alternatives<br>A simple method, used to assess carbon emissions,...</code> |
| <code>What factors must engineers consider during the drilling design phase?</code> | <code>The drilling of oil and gas wells involves several stages including the exploration phase, drilling design, and perforation techniques. In the exploration phase, geologists use seismic surveys to identify potential drilling locations. During the drilling design phase, engineers must consider factors such as wellbore stability, fluid mechanics, and formation pressures. Once the well is drilled, perforation techniques are applied to enhance the flow of hydrocarbons into the wellbore. The effectiveness of these techniques can significantly impact production rates and overall project success.</code> | <code>The extraction of crude oil and natural gas is typically carried out through drilling. Drilling uses different techniques to reach the petroleum reservoirs located deep underground. One key method is rotary drilling, where a drill bit is rotated while cutting through the earth's layers to create a wellbore. Rotary drilling is favored for its efficiency in penetrating hard rock layers. Another method is directional drilling, which allows operators to drill at various angles to reach reservoirs that are not directly beneath the drilling platform. This technique increases the area covered by the well and can optimize production. In addition, hydraulic fracturing enhances recovery rates by injecting fluids under high pressure to create fractures in the rock, increasing the permeability and allowing oil and gas to flow more freely. Lastly, the safety and environmental impacts of drilling techniques are a growing concern, and advancements are continually being sought to mitigate these effect...</code> |
| <code>How does the 'Dissolved pore network' concept enhance matrix permeability in the modeling of carbonate oil reservoirs?</code> | <code>In this paper, we present a case study of using dual porosity dual permeability (DPDP) simulation for an offshore Abu Dhabi carbonate oil reservoir exhibiting complex flow behavior through matrix, fracture system and conductive faults. The main objective of the study is to present and explain the reservoir flow behaviors by constructing and using advanced reservoir geologic and simulation models. The results of the study will be utilized as part of the inputs for full field development plan.<br>Initially, an extensive work on the faults and fractures characterization was conducted to properly integrate this information into a dynamic model using DPDP modeling approach. However, the poor response of some wells or field sectors indicated the insufficiency of this concept to capture the full complexity of the reservoir system. Consequently, a new geological concept was proposed to represent the effect of enhanced matrix permeability related to facies dissolution process in the reservoir mode...</code> | <code>Integration of pressure-derived permeability thickness with other geological data plays a crucial role in estimating the apparent reservoir permeability, which is a key reservoir property required for reliable reservoir characterization as it governs fluid flow and greatly impacts decisions related to production, field development, and reservoir management. The geological model provides a representation of the subsurface reservoir, capturing the spatial distribution of lithology, porosity, permeability, and other geological properties. Analysis of pressure data provides valuable information on well condition, reservoir extent, and dynamic reservoir parameters. Integrating such data with the geological model is an enabler to better quantify and manage the uncertainty in the spatial 3D distribution of permeability away from well control.<br>This work proposes a methodology to build high-resolution geological models based on the available dynamic data, seismic data, and geologic interpretati...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
#### 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`: 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`: 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`: False
- `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`: 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`: False
- `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
- `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
- `hub_revision`: None
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | ai-job-validation_cosine_accuracy |
|:------:|:----:|:-------------:|:---------------:|:---------------------------------:|
| 0.2967 | 1000 | - | 0.1417 | 0.9610 |
| 0.5935 | 2000 | - | 0.1199 | 0.9682 |
| 0.8902 | 3000 | - | 0.1082 | 0.9717 |
| 1.1869 | 4000 | - | 0.1102 | 0.9672 |
| 1.4837 | 5000 | 0.1614 | 0.1091 | 0.9679 |
| 1.7804 | 6000 | - | 0.1037 | 0.9714 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 5.1.0
- Transformers: 4.53.3
- PyTorch: 2.8.0+cu128
- Accelerate: 1.9.0
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## 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",
}
```
#### 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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