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:
- 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
, andscore
- 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
- 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
, andscore
- 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
- 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
: stepsper_device_train_batch_size
: 16per_device_eval_batch_size
: 16num_train_epochs
: 4warmup_ratio
: 0.1
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 4max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falsebatch_sampler
: batch_samplermulti_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",
}