SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2 on the parquet 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
Maximum Sequence Length: 384 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- parquet

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False, 'architecture': '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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Metabolite Identification Using Artificial Neural Network Metabolite identification is a major bottleneck in mass spectrometrybased metabolomic studies. In a typical untargeted metabolomic study that uses tandem MS (MSMS), the identities of metabolites are determined by matching the experimental MSMS data against those in spectral libraries. However, existing spectral libraries cover only a small fraction of known compounds. We introduce a neural network model to predict the identity of analytes whose reference measurements are not available in spectral libraries. Specifically, an artificial neural network model is trained to predict the fingerprints of compounds based on their experimental MSMS spectra. Candidates are retrieved from metabolite databases based on molecular formula or precursor mass. The candidate with the most similar fingerprint is chosen as the predicted one. The method was evaluated via an independent dataset. The results show that our neural network model improves MSMSbased metabolite identification accuracy by a considerable margin compared to previous computational methods, such as Metfrag and CSI:FingerID.',
    'Efficient Shared Peak Counting in Database Peptide Search Using Compact Data Structure for FragmentIon Index Database search is the most commonly employed method for identification of peptides from MSMS spectra data. The search involves comparing experimentally obtained MSMS spectra against a set of theoretical spectra predicted from a protein sequence database. One of the most commonly employed similarity metrics for spectral comparison is the sharedpeak count between a pair of MSMS spectra. Most modern methods index all generated fragmention data from theoretical spectra to speed up the shared peak count computations between a given experimental spectrum and all theoretical spectra. However, the bottleneck for this method is the gigantic memory footprint of fragmention index that leads to nonscalable solutions. In this paper, we present a novel data structure, called Compact FragmentIon Index Representation (CFIR), that efficiently compresses highly redundant ionmass information in the data to reduce the index size. Our proposed data structure outperforms all existing fragmention indexing data structures by at least 2xc3x97 in memory consumption while exhibiting the same time complexity for index construction and peptide search. The results also show comparable indexing speed, search speed and speedup scalability for CFIRindex and the stateoftheart algorithms.',
    'Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.',
]
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, 0.6391, 0.0971],
#         [0.6391, 1.0000, 0.0272],
#         [0.0971, 0.0272, 1.0000]])

Training Details

Training Dataset

parquet

Dataset: parquet
Size: 38,864 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 136 tokens mean: 248.76 tokens max: 384 tokens	min: 126 tokens mean: 256.93 tokens max: 384 tokens	min: 126 tokens mean: 237.9 tokens max: 384 tokens

Samples:

anchor	positive	negative
The longterm effect of media violence exposure on aggression of youngsters Abstract The effect of media violence on aggression has always been a trending issue, and a better understanding of the psychological mechanism of the impact of media violence on youth aggression is an extremely important research topic for preventing the negative impacts of media violence and juvenile delinquency. From the perspective of anger, this study explored the longterm effect of different degrees of media violence exposure on the aggression of youngsters, as well as the role of aggressive emotions. The studies found that individuals with a high degree of media violence exposure (HMVE) exhibited higher levels of proactive aggression in both irritation situations and higher levels of reactive aggression in lowirritation situations than did participants with a low degree of media violence exposure (LMVE). After being provoked, the anger of all participants was significantly increased, and the anger and p...	Link between Facial Expressions and Emotional States Induced by Exposure to Multimedia Content The explosive growth of digital videos has created new challenges for computer science. While many advances on video indexing, retrieval and summarization based on general, subjectindependent, objective descriptors have been made in the past years, research on the use of individual subjective preferences and affective states is at the forefront of research and poses great challenges. In this article, we study the relationship between emotional states reported by viewers and their facial physiological changes observed during the display of different video genres. A dataset of twenty videos was created from YouTube video sharing platform. During the exhibition of the videos, the viewerxe2x80x99s facial activities have been recorded and analyzed by means of Action Units (AUs). After that, emotional states selfreported by the viewers were assigned to video shots. Labels were divided into four cat...	Unmanned agricultural product sales system The invention relates to the field of agricultural product sales, provides an unmanned agricultural product sales system, and aims to solve the problem of agricultural product waste caused by the factthat most farmers can only prepare goods according to guessing and experiences when selling agricultural products at present. The unmanned agricultural product sales system comprises an acquisition module for acquiring selection information of customers; a storage module which prestores a vegetable preparation scheme; a matching module which is used for matching a corresponding side dish schemefrom the storage module according to the selection information of the client; a pushing module which is used for pushing the matched side dish scheme back to the client; an acquisition module which isalso used for acquiring confirmation information of a client; an order module which is used for generating order information according to the confirmation infor...
The longterm effect of media violence exposure on aggression of youngsters Abstract The effect of media violence on aggression has always been a trending issue, and a better understanding of the psychological mechanism of the impact of media violence on youth aggression is an extremely important research topic for preventing the negative impacts of media violence and juvenile delinquency. From the perspective of anger, this study explored the longterm effect of different degrees of media violence exposure on the aggression of youngsters, as well as the role of aggressive emotions. The studies found that individuals with a high degree of media violence exposure (HMVE) exhibited higher levels of proactive aggression in both irritation situations and higher levels of reactive aggression in lowirritation situations than did participants with a low degree of media violence exposure (LMVE). After being provoked, the anger of all participants was significantly increased, and the anger and p...	Link between Facial Expressions and Emotional States Induced by Exposure to Multimedia Content The explosive growth of digital videos has created new challenges for computer science. While many advances on video indexing, retrieval and summarization based on general, subjectindependent, objective descriptors have been made in the past years, research on the use of individual subjective preferences and affective states is at the forefront of research and poses great challenges. In this article, we study the relationship between emotional states reported by viewers and their facial physiological changes observed during the display of different video genres. A dataset of twenty videos was created from YouTube video sharing platform. During the exhibition of the videos, the viewerxe2x80x99s facial activities have been recorded and analyzed by means of Action Units (AUs). After that, emotional states selfreported by the viewers were assigned to video shots. Labels were divided into four cat...	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
The longterm effect of media violence exposure on aggression of youngsters Abstract The effect of media violence on aggression has always been a trending issue, and a better understanding of the psychological mechanism of the impact of media violence on youth aggression is an extremely important research topic for preventing the negative impacts of media violence and juvenile delinquency. From the perspective of anger, this study explored the longterm effect of different degrees of media violence exposure on the aggression of youngsters, as well as the role of aggressive emotions. The studies found that individuals with a high degree of media violence exposure (HMVE) exhibited higher levels of proactive aggression in both irritation situations and higher levels of reactive aggression in lowirritation situations than did participants with a low degree of media violence exposure (LMVE). After being provoked, the anger of all participants was significantly increased, and the anger and p...	Link between Facial Expressions and Emotional States Induced by Exposure to Multimedia Content The explosive growth of digital videos has created new challenges for computer science. While many advances on video indexing, retrieval and summarization based on general, subjectindependent, objective descriptors have been made in the past years, research on the use of individual subjective preferences and affective states is at the forefront of research and poses great challenges. In this article, we study the relationship between emotional states reported by viewers and their facial physiological changes observed during the display of different video genres. A dataset of twenty videos was created from YouTube video sharing platform. During the exhibition of the videos, the viewerxe2x80x99s facial activities have been recorded and analyzed by means of Action Units (AUs). After that, emotional states selfreported by the viewers were assigned to video shots. Labels were divided into four cat...	Minimum number of additive tuples in groups of prime order For a prime number p and a sequence of integers a0, . . . , akxe2x88x88 0,1, . . . , p, lets (a0, . . . , ak) be the minimum number of (k 1)tuples (x0, . . . , xk) xe2x88x88A0xc3x97xc2xb7xc2xb7xc2xb7xc3x97Akwithx0x1xc2xb7xc2xb7xc2xb7xk, over subsets a0, . . . , Akxe2x8ax86Zp of sizes a0, . . . , ak respectively. We observe that an elegant argument of Samotij and Sudakov can be extended to show that there exists an extremal configuration with all sets Ai being intervals of appropriate length. The same conclusion also holds for the related problem ,posed by Bajnok, whena0xc2xb7xc2xb7xc2xb7ak:aandA0xc2xb7xc2xb7xc2xb7Ak, provided k is not equal 1 modulop. Finally, by applying basic Fourier analysis, we show for Bajnokxe2x80x99s problem that if pu003e13 and axe2x88x88 3, . . . , pxe2x88x923are fixed whilekxe2x89xa11 (modp) tends to infinity, then the extremal configuration alternates between at least two affine nonequivalent sets.

Loss: CachedGISTEmbedLoss with these parameters:

{
    "guide": "SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')",
    "temperature": 0.01,
    "mini_batch_size": 32,
    "margin_strategy": "relative",
    "margin": 0.1,
    "contrast_anchors": true,
    "contrast_positives": true,
    "gather_across_devices": false
}

Evaluation Dataset

parquet

Dataset: parquet
Size: 4,858 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 140 tokens mean: 244.64 tokens max: 384 tokens	min: 133 tokens mean: 249.07 tokens max: 384 tokens	min: 127 tokens mean: 235.29 tokens max: 384 tokens

Samples:

anchor	positive	negative
Secrecy Energy Efficient Beamforming for SatelliteTerrestrial Coordinated Communication Systems This paper investigates the secrecy energy efficiency maximization (SEEM) problem in a satelliteterrestrial coordinated communication systems. First, we formulate a coordinated transmission optimization problem by using the secrecy energy efficiency (SEE) as optimization criterion and meanwhile the transmission quality of the terrestrial link and the transmission power of the satellite and ground base station as constraints. Due to the fractional form of SEE, the formulated optimization problem is nonconvex and mathematically intractable. Then, we transform the original fractional problem into an equivalent subtractive problem, and employ the difference of twoconvex functions (D.C.) approximation method to arrive at an approximate convex problem. Finally, we propose a convergence proven iterative algorithm to solve the modified maximization problem. Simulation results are presented to illust...	On the Ergodic Secrecy Rate of Massive MIMO Transmission with Partial Legitimate User CSI In this paper, we consider the downlink transmission over a singlecell massive multipleinputmultipleoutput (MIMO) system in the presence of multiple singleantenna eavesdroppers (massive MIMOME). We concentrate on the practical scenario where partial channel state information (CSI) of legitimate users and no CSI of eavesdroppers are available at the base station and consider both types of eavesdroppers including the noncolluding and colluding eavesdroppers. Random unitary beamforming (RUB) based scheme is used to describe the partial CSI of legitimate users. We derive the closedform expression of ergodic secrecy rate for RUB based massive MIMOME transmission, and its single legitimate user particular case. We also present numerical results to illustrate the performancecomplexity tradeoff among different massive MIMO transmission schemes. We show that RUB based scheme can enhance secrecy performance...	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
Secrecy Energy Efficient Beamforming for SatelliteTerrestrial Coordinated Communication Systems This paper investigates the secrecy energy efficiency maximization (SEEM) problem in a satelliteterrestrial coordinated communication systems. First, we formulate a coordinated transmission optimization problem by using the secrecy energy efficiency (SEE) as optimization criterion and meanwhile the transmission quality of the terrestrial link and the transmission power of the satellite and ground base station as constraints. Due to the fractional form of SEE, the formulated optimization problem is nonconvex and mathematically intractable. Then, we transform the original fractional problem into an equivalent subtractive problem, and employ the difference of twoconvex functions (D.C.) approximation method to arrive at an approximate convex problem. Finally, we propose a convergence proven iterative algorithm to solve the modified maximization problem. Simulation results are presented to illust...	On the Ergodic Secrecy Rate of Massive MIMO Transmission with Partial Legitimate User CSI In this paper, we consider the downlink transmission over a singlecell massive multipleinputmultipleoutput (MIMO) system in the presence of multiple singleantenna eavesdroppers (massive MIMOME). We concentrate on the practical scenario where partial channel state information (CSI) of legitimate users and no CSI of eavesdroppers are available at the base station and consider both types of eavesdroppers including the noncolluding and colluding eavesdroppers. Random unitary beamforming (RUB) based scheme is used to describe the partial CSI of legitimate users. We derive the closedform expression of ergodic secrecy rate for RUB based massive MIMOME transmission, and its single legitimate user particular case. We also present numerical results to illustrate the performancecomplexity tradeoff among different massive MIMO transmission schemes. We show that RUB based scheme can enhance secrecy performance...	Does a friendly robot make you feel better As robots are taking a more prominent role in our daily lives, it becomes increasingly important to consider how their presence influences us. Several studies have investigated effects of robot behavior on the extent to which that robot is positively evaluated. Likewise, studies have shown that the emotions a robot shows tend to be contagious: a happy robot makes us feel happy as well. It is unknown, however, whether the affect that people experience while interacting with a robot also influences their evaluation of the robot. This study aims to discover whether peoplexe2x80x99s affective and evaluative responses to a social robot are related. Results show that affective responses and evaluations are related, and that these effects are strongest when a robot shows meaningful motions. These results are consistent with earlier findings in terms of how people evaluate social robots.
Secrecy Energy Efficient Beamforming for SatelliteTerrestrial Coordinated Communication Systems This paper investigates the secrecy energy efficiency maximization (SEEM) problem in a satelliteterrestrial coordinated communication systems. First, we formulate a coordinated transmission optimization problem by using the secrecy energy efficiency (SEE) as optimization criterion and meanwhile the transmission quality of the terrestrial link and the transmission power of the satellite and ground base station as constraints. Due to the fractional form of SEE, the formulated optimization problem is nonconvex and mathematically intractable. Then, we transform the original fractional problem into an equivalent subtractive problem, and employ the difference of twoconvex functions (D.C.) approximation method to arrive at an approximate convex problem. Finally, we propose a convergence proven iterative algorithm to solve the modified maximization problem. Simulation results are presented to illust...	On the Ergodic Secrecy Rate of Massive MIMO Transmission with Partial Legitimate User CSI In this paper, we consider the downlink transmission over a singlecell massive multipleinputmultipleoutput (MIMO) system in the presence of multiple singleantenna eavesdroppers (massive MIMOME). We concentrate on the practical scenario where partial channel state information (CSI) of legitimate users and no CSI of eavesdroppers are available at the base station and consider both types of eavesdroppers including the noncolluding and colluding eavesdroppers. Random unitary beamforming (RUB) based scheme is used to describe the partial CSI of legitimate users. We derive the closedform expression of ergodic secrecy rate for RUB based massive MIMOME transmission, and its single legitimate user particular case. We also present numerical results to illustrate the performancecomplexity tradeoff among different massive MIMO transmission schemes. We show that RUB based scheme can enhance secrecy performance...	A Critical Look at the 2019 College Admissions Scandal Discusses the 2019 College admissions scandal. Let me begin with a disclaimer: I am making no legal excuses for the participants in the current scandal. I am only offering contextual background that places it in the broader academic, cultural, and political perspective required for understanding. It is only the most recent installment of a wellworn narrative: the controlling elite make their own rules and live by them, if they can get away with it. Unfortunately, some of the participants, who are either serving or facing jail time, didnxe2x80x99t know to not go into a gunfight with a sharp stick. Money alone is not enough to avoid prosecution for fraud: you need political clout. The best protection a defendant can have is a prosecutor who fears political reprisal. Compare how the Koch brothers escaped prosecution for stealing millions of oil dollars from Native American tribes1,2 with the fate of actresses Lori Loughlin and Felicit...

Loss: CachedGISTEmbedLoss with these parameters:

{
    "guide": "SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')",
    "temperature": 0.01,
    "mini_batch_size": 32,
    "margin_strategy": "relative",
    "margin": 0.1,
    "contrast_anchors": true,
    "contrast_positives": true,
    "gather_across_devices": false
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
optim: adamw_torch
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

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: 8
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: 5e-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
bf16: False
fp16: True
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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: no
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: True
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.0412	100	0.049	0.0217
0.0823	200	0.073	0.0756
0.1235	300	0.1089	0.1086
0.1647	400	0.1255	0.0670
0.2058	500	0.0805	0.0876
0.2470	600	0.103	0.0718
0.2882	700	0.0559	0.0891
0.3294	800	0.1071	0.0660
0.3705	900	0.066	0.0728
0.4117	1000	0.0631	0.0631
0.4529	1100	0.0504	0.0484
0.4940	1200	0.0568	0.0482
0.5352	1300	0.042	0.0680
0.5764	1400	0.0416	0.0695
0.6175	1500	0.0221	0.0731
0.6587	1600	0.0426	0.0587
0.6999	1700	0.0291	0.0392
0.7410	1800	0.0285	0.0410
0.7822	1900	0.0204	0.0433
0.8234	2000	0.0269	0.0390
0.8646	2100	0.0224	0.0385
0.9057	2200	0.012	0.0391
0.9469	2300	0.0006	0.0392
0.9881	2400	0.0012	0.0396

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.1.2
Transformers: 4.57.1
PyTorch: 2.9.0+cu126
Accelerate: 1.11.0
Datasets: 4.0.0
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

@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",
}

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Safetensors

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Tensor type

F32

Model tree for vaios-stergio/all-mpnet-base-v2-dblp-aminer-50k-triplets-cachedgistembedloss

Base model

sentence-transformers/all-mpnet-base-v2

Finetuned

(323)

this model