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",
}
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Model tree for Mihaiii/test33
Dataset used to train Mihaiii/test33
Evaluation results
- Pearson Cosine on sts devself-reported0.826
- Spearman Cosine on sts devself-reported0.828
- Pearson Manhattan on sts devself-reported0.820
- Spearman Manhattan on sts devself-reported0.823
- Pearson Euclidean on sts devself-reported0.821
- Spearman Euclidean on sts devself-reported0.824
- Pearson Dot on sts devself-reported0.804
- Spearman Dot on sts devself-reported0.808
- Pearson Max on sts devself-reported0.826
- Spearman Max on sts devself-reported0.828