metadata

base_model: Mihaiii/Venusaur
datasets:
  - Mihaiii/qa-assistant-2
language:
  - en
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:16011
  - loss:CosineSimilarityLoss
widget:
  - source_sentence: What impact does high-speed rail have on connectivity between cities?
    sentences:
      - >-
        Art supplies can be quite expensive, especially high-quality paints and
        brushes.
      - >-
        High-speed rail can be a more comfortable and convenient mode of travel
        compared to buses or cars.
      - >-
        Engineers use a variety of methods to test the safety of autonomous
        vehicles, including controlled track testing and public road trials.
  - source_sentence: What is the best soil type for growing tomatoes?
    sentences:
      - >-
        Sandy loam soil is often considered ideal for growing tomatoes due to
        its good drainage and nutrient-holding capacity.
      - >-
        Socialist political systems are often contrasted with capitalist
        systems, which prioritize private ownership and market-driven economies.
      - >-
        The core principles of Sikhism include the belief in one God, the
        importance of honest living, and the practice of selfless service.
  - source_sentence: What are the three main types of rocks?
    sentences:
      - >-
        Mount Everest is the highest mountain in the world, located in the
        Himalayas.
      - >-
        Archaeologists sometimes face challenges such as funding and access to
        advanced technology, which can impact their ability to preserve
        findings.
      - >-
        Some people are concerned about the ethical implications of genetic
        modification in food production.
  - source_sentence: How do vaccines help prevent diseases?
    sentences:
      - >-
        The theory also posits that during periods of economic downturn,
        increased government spending can help stimulate demand and pull the
        economy out of recession.
      - >-
        The Gurdwara is a place where Sikhs can participate in religious rituals
        and ceremonies, such as weddings and naming ceremonies.
      - >-
        The development of vaccines involves rigorous testing to ensure their
        safety and efficacy before they are approved for public use.
  - source_sentence: What are the social structures of ants?
    sentences:
      - >-
        The social hierarchy of ants is a complex system that ensures the
        survival and efficiency of the colony.
      - >-
        In a parliamentary system, the executive branch derives its legitimacy
        from and is accountable to the legislature; the executive and
        legislative branches are thus interconnected.
      - >-
        Proper waste management and recycling can contribute to a more
        sustainable farming operation.
model-index:
  - name: SentenceTransformer based on Mihaiii/Venusaur
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.826101669872389
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8277251878978443
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8199515763304537
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8225731321378551
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8214525375708358
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8236879484111633
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.8037304918463798
            name: Pearson Dot
          - type: spearman_dot
            value: 0.8082305683494836
            name: Spearman Dot
          - type: pearson_max
            value: 0.826101669872389
            name: Pearson Max
          - type: spearman_max
            value: 0.8277251878978443
            name: Spearman Max

SentenceTransformer based on Mihaiii/Venusaur

This is a sentence-transformers model finetuned from Mihaiii/Venusaur on the Mihaiii/qa-assistant-2 dataset. 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: Mihaiii/Venusaur
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- Mihaiii/qa-assistant-2
Language: en

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': 512, '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})
)

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 = [
    'What are the social structures of ants?',
    'The social hierarchy of ants is a complex system that ensures the survival and efficiency of the colony.',
    'In a parliamentary system, the executive branch derives its legitimacy from and is accountable to the legislature; the executive and legislative branches are thus interconnected.',
]
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

Semantic Similarity

Dataset: sts-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8261
spearman_cosine	0.8277
pearson_manhattan	0.82
spearman_manhattan	0.8226
pearson_euclidean	0.8215
spearman_euclidean	0.8237
pearson_dot	0.8037
spearman_dot	0.8082
pearson_max	0.8261
spearman_max	0.8277

Training Details

Training Dataset

Mihaiii/qa-assistant-2

Dataset: Mihaiii/qa-assistant-2 at 9650e69
Size: 16,011 training samples
Columns: question, answer, and score
Approximate statistics based on the first 1000 samples:
question answer score
type string string float
details
min: 6 tokens
mean: 12.73 tokens
max: 27 tokens

min: 10 tokens
mean: 22.42 tokens
max: 65 tokens

min: 0.02
mean: 0.53
max: 1.0

	question	answer	score
type	string	string	float
details	min: 6 tokens mean: 12.73 tokens max: 27 tokens	min: 10 tokens mean: 22.42 tokens max: 65 tokens	min: 0.02 mean: 0.53 max: 1.0

Samples:

question	answer	score
`Can you describe the process of robot path planning?`	`Robots can be programmed to perform a variety of tasks, from simple repetitive actions to complex decision-making processes.`	`0.27999999999999997`
`Can humans live on Mars?`	`Mars is the fourth planet from the Sun and is often called the Red Planet due to its reddish appearance.`	`0.16`
`What are the key elements of composition in abstract art?`	`The history of abstract art dates back to the early 20th century, with pioneers like Wassily Kandinsky and Piet Mondrian.`	`0.36`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Evaluation Dataset

Mihaiii/qa-assistant-2

Dataset: Mihaiii/qa-assistant-2 at 9650e69
Size: 3,879 evaluation samples
Columns: question, answer, and score
Approximate statistics based on the first 1000 samples:
question answer score
type string string float
details
min: 7 tokens
mean: 12.71 tokens
max: 31 tokens

min: 10 tokens
mean: 22.63 tokens
max: 51 tokens

min: 0.02
mean: 0.53
max: 1.0

	question	answer	score
type	string	string	float
details	min: 7 tokens mean: 12.71 tokens max: 31 tokens	min: 10 tokens mean: 22.63 tokens max: 51 tokens	min: 0.02 mean: 0.53 max: 1.0

Samples:

question	answer	score
`What is the concept of social stratification?`	`The study of social stratification involves examining the inequalities and divisions within a society.`	`0.6799999999999999`
`How does J.K. Rowling develop the character of Hermione Granger throughout the 'Harry Potter' series?`	`The 'Harry Potter' series consists of seven books, starting with 'Harry Potter and the Philosopher's Stone' and ending with 'Harry Potter and the Deathly Hallows'.`	`0.22000000000000003`
`What is the parliamentary system and how does it function?`	`In a parliamentary system, the government can be dissolved by a vote of no confidence, which can lead to new elections.`	`0.6799999999999999`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 4
warmup_ratio: 0.1

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: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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: 4
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	sts-dev_spearman_cosine
0.0999	100	0.0593	0.0540	0.5848
0.1998	200	0.05	0.0463	0.6618
0.2997	300	0.044	0.0418	0.7102
0.3996	400	0.0413	0.0385	0.7390
0.4995	500	0.0377	0.0349	0.7707
0.5994	600	0.034	0.0333	0.7770
0.6993	700	0.0344	0.0321	0.7879
0.7992	800	0.0324	0.0311	0.7927
0.8991	900	0.0334	0.0302	0.8005
0.9990	1000	0.0304	0.0305	0.8023
1.0989	1100	0.0261	0.0306	0.8072
1.1988	1200	0.0267	0.0292	0.8104
1.2987	1300	0.0244	0.0287	0.8110
1.3986	1400	0.0272	0.0294	0.8098
1.4985	1500	0.0241	0.0281	0.8135
1.5984	1600	0.0253	0.0282	0.8143
1.6983	1700	0.0245	0.0276	0.8169
1.7982	1800	0.025	0.0274	0.8182
1.8981	1900	0.0236	0.0273	0.8193
1.9980	2000	0.0236	0.0269	0.8218
2.0979	2100	0.0215	0.0278	0.8213
2.1978	2200	0.0216	0.0269	0.8226
2.2977	2300	0.0205	0.0276	0.8207
2.3976	2400	0.0181	0.0273	0.8202
2.4975	2500	0.0197	0.0267	0.8228
2.5974	2600	0.02	0.0267	0.8238
2.6973	2700	0.0203	0.0263	0.8258
2.7972	2800	0.0184	0.0263	0.8264
2.8971	2900	0.0201	0.0269	0.8243
2.9970	3000	0.0196	0.0263	0.8251
3.0969	3100	0.0168	0.0264	0.8250
3.1968	3200	0.0176	0.0263	0.8267
3.2967	3300	0.0168	0.0263	0.8270
3.3966	3400	0.017	0.0260	0.8277
3.4965	3500	0.0164	0.0261	0.8273
3.5964	3600	0.0172	0.0259	0.8280
3.6963	3700	0.0168	0.0260	0.8274
3.7962	3800	0.0176	0.0262	0.8279
3.8961	3900	0.0182	0.0261	0.8278
3.9960	4000	0.0174	0.0260	0.8277

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.0.1+cu118
Accelerate: 0.31.0
Datasets: 2.20.0
Tokenizers: 0.19.1

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",
}