SentenceTransformer based on dunzhang/stella_en_1.5B_v5
This is a sentence-transformers model finetuned from dunzhang/stella_en_1.5B_v5. It maps sentences & paragraphs to a 1024-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: dunzhang/stella_en_1.5B_v5
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 1024 tokens
- Similarity Function: Cosine Similarity
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: Qwen2Model
(1): Pooling({'word_embedding_dimension': 1536, '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): Dense({'in_features': 1536, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
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 = [
'QuestionSummary: Function Machines\nQuestion: Which of the following pairs of function machines are correct?\nCorrectAnswer: \n\n\\(a \\Rightarrow -5 \\Rightarrow \\times2\\Rightarrow 2(a-5)\\) \nAnswer: \n\n\\(a \\Rightarrow \\times2 \\Rightarrow -5\\Rightarrow 2(a-5)\\) ',
'Does not follow the arrows through a function machine, changes the order of the operations asked.',
'Incorrectly cancels what they believe is a factor in algebraic fractions',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Information Retrieval
- Dataset:
val
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@25 | 0.6946 |
cosine_precision@100 | 0.0088 |
cosine_precision@200 | 0.0047 |
cosine_precision@300 | 0.0032 |
cosine_precision@400 | 0.0024 |
cosine_precision@500 | 0.002 |
cosine_precision@600 | 0.0016 |
cosine_precision@700 | 0.0014 |
cosine_precision@800 | 0.0012 |
cosine_precision@900 | 0.0011 |
cosine_precision@1000 | 0.001 |
cosine_recall@100 | 0.876 |
cosine_recall@200 | 0.9369 |
cosine_recall@300 | 0.9587 |
cosine_recall@400 | 0.9736 |
cosine_recall@500 | 0.9805 |
cosine_recall@600 | 0.9862 |
cosine_recall@700 | 0.9931 |
cosine_recall@800 | 0.9954 |
cosine_recall@900 | 0.9977 |
cosine_recall@1000 | 0.9977 |
cosine_ndcg@25 | 0.3564 |
cosine_mrr@25 | 0.261 |
cosine_map@25 | 0.261 |
dot_accuracy@25 | 0.4271 |
dot_precision@100 | 0.0076 |
dot_precision@200 | 0.0043 |
dot_precision@300 | 0.0031 |
dot_precision@400 | 0.0024 |
dot_precision@500 | 0.0019 |
dot_precision@600 | 0.0016 |
dot_precision@700 | 0.0014 |
dot_precision@800 | 0.0012 |
dot_precision@900 | 0.0011 |
dot_precision@1000 | 0.001 |
dot_recall@100 | 0.76 |
dot_recall@200 | 0.8657 |
dot_recall@300 | 0.9231 |
dot_recall@400 | 0.9437 |
dot_recall@500 | 0.961 |
dot_recall@600 | 0.9713 |
dot_recall@700 | 0.9793 |
dot_recall@800 | 0.9839 |
dot_recall@900 | 0.9874 |
dot_recall@1000 | 0.9897 |
dot_ndcg@25 | 0.1953 |
dot_mrr@25 | 0.1329 |
dot_map@25 | 0.1329 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 3,999 training samples
- Columns:
anchor
andpositive
- Approximate statistics based on the first 1000 samples:
anchor positive type string string details - min: 30 tokens
- mean: 87.03 tokens
- max: 363 tokens
- min: 4 tokens
- mean: 13.84 tokens
- max: 42 tokens
- Samples:
- Loss:
CachedMultipleNegativesSymmetricRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim", "mini_batch_size": 1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 1372per_device_eval_batch_size
: 1372learning_rate
: 4e-05num_train_epochs
: 5warmup_ratio
: 0.1save_only_model
: Truebf16
: Trueload_best_model_at_end
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 1372per_device_eval_batch_size
: 1372per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 4e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 5max_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
: Truerestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: 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
: Trueignore_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
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | val_cosine_map@25 |
---|---|---|---|
0.3333 | 1 | 2.2717 | 0.1775 |
0.6667 | 2 | 2.1785 | 0.2300 |
1.0 | 3 | 1.4112 | 0.2651 |
1.3333 | 4 | 1.1861 | 0.2726 |
1.6667 | 5 | 0.8742 | 0.2813 |
2.0 | 6 | 0.8327 | 0.2818 |
2.3333 | 7 | 0.7626 | 0.2777 |
2.6667 | 8 | 0.5767 | 0.2752 |
3.0 | 9 | 0.493 | 0.2698 |
3.3333 | 10 | 0.5174 | 0.2654 |
3.6667 | 11 | 0.3906 | 0.2655 |
4.0 | 12 | 0.419 | 0.2627 |
4.3333 | 13 | 0.4394 | 0.2625 |
4.6667 | 14 | 0.5449 | 0.2612 |
5.0 | 15 | 0.3731 | 0.2610 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- PyTorch: 2.2.0
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.0
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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Base model
dunzhang/stella_en_1.5B_v5Evaluation results
- Cosine Accuracy@25 on valself-reported0.695
- Cosine Precision@100 on valself-reported0.009
- Cosine Precision@200 on valself-reported0.005
- Cosine Precision@300 on valself-reported0.003
- Cosine Precision@400 on valself-reported0.002
- Cosine Precision@500 on valself-reported0.002
- Cosine Precision@600 on valself-reported0.002
- Cosine Precision@700 on valself-reported0.001
- Cosine Precision@800 on valself-reported0.001
- Cosine Precision@900 on valself-reported0.001