---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:46716
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: Structurally, diplomonads have two equal-sized what and multiple
flagella?
sentences:
- deciding when to buy or sell a stock is not an easy task because the market is
hard to predict, being influenced by political and economic factors. thus, methodologies
based on computational intelligence have been applied to this challenging problem.
in this work, every day the stocks are ranked by technique for order preference
by similarity to ideal solution ( topsis ) using technical analysis criteria,
and the most suitable stock is selected for purchase. even so, it may occur that
the market is not favorable to purchase on certain days, or even, the topsis make
an incorrect selection. to improve the selection, another method should be used.
so, a hybrid
- 'we present the analysis of the brightest flare that was recorded in the \ emph
{ insight } - hmxt data set, in a broad energy range ( 2 $ - $ 200 kev ) from
the microquasar grs ~ 1915 + 105 during an unusual low - luminosity state. this
flare was detected by \ emph { insight } - hxmt among a series of flares during
2 june 2019 utc 16 : 37 : 06 to 20 : 11 : 36, with a 2 - 200 kev luminosity of
3. 4 $ - $ 7. 27 $ \ times10 ^ { 38 } $ er'
- nuclei
- source_sentence: What instruments used in guidance systems to indicate directions
in space must have an angular momentum that does not change in direction?
sentences:
- magnetic and transport properties of near - stoichiometric metastable fexmnygaz
alloys ( 46 < x < 52, 17 < y25, 26 < z < 30 ) with face - centered cubic ( fcc
), body - centered cubic ( bcc ), and two - phase ( fcc + bcc ) structures are
investigated. the experimental results are analyzed in terms of first - principles
calculations of stoichiometric fe2mnga alloy with the l21, l12, and the tetragonally
distorted l21 structural orderings. it is shown that the pure bcc and fcc phases
have distinct magnetic
- k nearest neighbor ( knn ) joins are used in scientific domains for data analysis,
and are building blocks of several well - known algorithms. knn - joins find the
knn of all points in a dataset. this paper focuses on a hybrid cpu / gpu approach
for low - dimensional knn - joins, where the gpu may not yield substantial performance
gains over parallel cpu algorithms. we utilize a work queue that prioritizes computing
data points in high density regions on the gpu, and low density regions on the
cpu, thereby taking advantage of each architecture ' s relative strengths. our
approach, hybridknn - join,
- the fact that these states are effectively decoupled from propagating photons.
we prove that scattering of a parity - invariant single photon on a qubit pair,
combined with a properly engineered time variation of the qubit detuning, is not
only feasible, but also more effective than strategies based on the relaxation
of the excited states of the qubits. the use of tensor network methods to simulate
the proposed scheme enables to include photon delays in collision models, thus
opening the possibility to follow the time evolution of the full quantum system,
including qubits and field, and to efficiently implement and characterize the
dynamics in non
- source_sentence: If pollination and fertilization occur, a diploid zygote forms
within an ovule, located where
sentences:
- while cosmic rays $ ( e \ gtrsim 1 \, \ mathrm { gev } ) $ are well coupled to
a galaxy ' s interstellar medium ( ism ) at scales of $ l > 100 \, \ mathrm {
pc } $, adjusting stratification and driving outflows, their impact on small scales
is less clear. based on calculations of the cosmic ray diffusion coefficient from
observations of the grammage in the milky way, cosmic rays have little time to
dynamically impact the ism on those small scales. using numerical simulations,
we explore how more complex cosmic ray transport could allow cosmic rays to couple
- centripetal force
- derived simple analytical expressions for the maximum growth rate, corresponding
to the most unstable mode of the system. these expressions provide the explicit
dependence of the growth rate on the various equilibrium parameters. for small
angles the growth time is linearly proportional to the shear angle, and in this
regime the single interface problem and the slab problem tend to the same result.
on the contrary, in the limit of large angles and for the interface problem the
growth time is essentially independent of the shear angle. in this regime we have
also been able to calculate an approximate expression for the growth time for
the slab configuration. magnetic shear can have a strong effect on the growth
rates
- source_sentence: When the hydrogen is nearly used up, the star can fuse which element
into heavier elements?
sentences:
- the 50 ~ kton iron calorimeter ( ical ) detector at the underground india based
neutrino observatory ( ino ) will make measurements on atmospheric neutrinos.
muons produced in charged current ( cc ) interactions of muon neutrinos with the
iron are tracked spatially and temporally through the signals that they produce
in the resistive plate chambers ~ ( rpcs ) that are interleaved with iron layers.
since the rpcs will be operated in the avalanche mode the signal rise - time is
$ \ sim ~ 1 ~ \ rm { nsec } $ resulting in a fast time response
- magnesium in air
- pbhs. after discussing pbh formation as well as several inflation models leading
to pbh production, we summarize various existing and future observational constraints.
we then present topics on formation of pbh binaries, gravitational waves from
pbh binaries, various observational tests of pbhs by using gravitational waves.
- source_sentence: How many different main types of diabetes are there?
sentences:
- skin
- two
- a connection between relativistic quantum mechanics in the foldy - wouthuysen
representation and the paraxial equations is established for a dirac particle
in external fields. the paraxial form of the landau eigenfunction for a relativistic
electron in a uniform magnetic field is determined. the obtained wave function
contains the gouy phase and significantly approaches to the paraxial wave function
for a free electron.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: sciq eval
type: sciq-eval
metrics:
- type: cosine_accuracy@1
value: 0.084
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.192
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.26
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.367
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.084
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.064
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.052
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0367
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.084
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.192
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.26
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.367
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.20773622543165599
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1588857142857143
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.17411230071844377
name: Cosine Map@100
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
### 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': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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 = [
'How many different main types of diabetes are there?',
'two',
'a connection between relativistic quantum mechanics in the foldy - wouthuysen representation and the paraxial equations is established for a dirac particle in external fields. the paraxial form of the landau eigenfunction for a relativistic electron in a uniform magnetic field is determined. the obtained wave function contains the gouy phase and significantly approaches to the paraxial wave function for a free electron.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `sciq-eval`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.084 |
| cosine_accuracy@3 | 0.192 |
| cosine_accuracy@5 | 0.26 |
| cosine_accuracy@10 | 0.367 |
| cosine_precision@1 | 0.084 |
| cosine_precision@3 | 0.064 |
| cosine_precision@5 | 0.052 |
| cosine_precision@10 | 0.0367 |
| cosine_recall@1 | 0.084 |
| cosine_recall@3 | 0.192 |
| cosine_recall@5 | 0.26 |
| cosine_recall@10 | 0.367 |
| **cosine_ndcg@10** | **0.2077** |
| cosine_mrr@10 | 0.1589 |
| cosine_map@100 | 0.1741 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 46,716 training samples
* Columns: sentence_0, sentence_1, and label
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | - min: 7 tokens
- mean: 17.62 tokens
- max: 63 tokens
| - min: 3 tokens
- mean: 92.89 tokens
- max: 133 tokens
| - min: 0.0
- mean: 0.23
- max: 1.0
|
* Samples:
| sentence_0 | sentence_1 | label |
|:--------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| Unlike plants, animal species rely almost exclusively on what type of reproduction? | 0. 4, \ sim 4, and \ sim 300 \ mum are stronger than 10 ^ 5, 10 ^ 8, and 10 ^ 4 times those of the local interstellar radiation field ( isrf ). below these values, the chemical pumping is the dominant source of excitation of the j > 1 levels, even at high kinetic temperatures ( \ sim 1000 k ). the far - infrared emission lines of ch + observed in the orion bar and the ngc 7027 pdrs are consistent with the predictions of our excitation model assuming an incident far - ultraviolet ( fuv ) radiation field of \ sim 3 \ times 10 | 0.0 |
| What type of energy occurs by splitting the nuclei of radioactive uranium? | we study the potential of future electron - ion collider ( eic ) data to probe four - fermion operators in the standard model effective field theory ( smeft ). the ability to perform measurements with both polarized electron and proton beams at the eic provides a powerful tool that can disentangle the effects from different smeft operators. we compare the potential constraints from an eic with those obtained from drell - yan data at the large hadron collider. we show that eic data plays an important complementary role since it probes combinations of wilson coefficients not accessible through available drell - yan measurements. | 0.0 |
| What element, which often forms polymers, has a unique ability to form covalent bonds with many other atoms? | some divergent series $ f $. the convergence sets on $ \ gamma : = \ { [ 1 : z : \ psi ( z ) ] : z \ in \ mathbb { c } \ } \ subset \ mathbb { c } ^ 2 \ subset \ mathbb { p } ^ 2 $, where $ \ psi $ is a transcendental entire holomorphic function, are also studied and we obtain that a subset on $ \ gamma $ is a convergence set in $ \ mathbb { p } ^ 2 $ if and only if it is a countable union of compact projectively convex sets, and | 0.0 |
* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 1
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `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
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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}
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin