SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. It maps sentences & paragraphs to a 768-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-mpnet-base-v2
- Maximum Sequence Length: 384 tokens
- Output Dimensionality: 768 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, '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:
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("s2593817/sft-question-embedding")
# Run inference
sentences = [
'How many total tours were there for each ranking date?',
'How many total pounds were purchased in the year 2018 at all London branches?',
'What is the carrier of the most expensive phone?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Training Details
Training Dataset
Unnamed Dataset
- Size: 9,306 training samples
- Columns:
sentence1
,sentence2
, andscore
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score type string string int details - min: 7 tokens
- mean: 16.25 tokens
- max: 36 tokens
- min: 7 tokens
- mean: 15.23 tokens
- max: 35 tokens
- -1: ~25.20%
- 1: ~74.80%
- Samples:
sentence1 sentence2 score How many singers do we have?
How many aircrafts do we have?
1
What is the total number of singers?
What is the total number of students?
1
Show name, country, age for all singers ordered by age from the oldest to the youngest.
List all people names in the order of their date of birth from old to young.
1
- Loss:
CoSENTLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "pairwise_cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size
: 160learning_rate
: 2e-05num_train_epochs
: 100warmup_ratio
: 0.2fp16
: Truedataloader_num_workers
: 16batch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: noprediction_loss_only
: Trueper_device_train_batch_size
: 160per_device_eval_batch_size
: 8per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 100max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.2warmup_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
: Truefp16_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
: 16dataloader_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
: Falseeval_on_start
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss |
---|---|---|
1.6949 | 100 | 9.4942 |
2.4407 | 200 | 8.3205 |
3.1864 | 300 | 6.3257 |
3.9322 | 400 | 4.7354 |
4.6780 | 500 | 3.6898 |
5.4237 | 600 | 3.3736 |
6.1695 | 700 | 3.0906 |
7.8644 | 800 | 3.1459 |
8.6102 | 900 | 3.4447 |
9.3559 | 1000 | 3.219 |
10.1017 | 1100 | 2.9808 |
10.8475 | 1200 | 2.505 |
11.5932 | 1300 | 2.0372 |
12.3390 | 1400 | 1.8879 |
13.0847 | 1500 | 1.8852 |
14.7797 | 1600 | 2.1867 |
15.5254 | 1700 | 2.0583 |
16.2712 | 1800 | 2.0132 |
17.0169 | 1900 | 1.8906 |
17.7627 | 2000 | 1.4556 |
18.5085 | 2100 | 1.2575 |
19.2542 | 2200 | 1.258 |
20.9492 | 2300 | 0.9423 |
21.6949 | 2400 | 1.398 |
22.4407 | 2500 | 1.2811 |
23.1864 | 2600 | 1.2602 |
23.9322 | 2700 | 1.2178 |
24.6780 | 2800 | 1.0895 |
25.4237 | 2900 | 0.9186 |
26.1695 | 3000 | 0.7916 |
27.8644 | 3100 | 0.7777 |
28.6102 | 3200 | 1.0487 |
29.3559 | 3300 | 0.9255 |
30.1017 | 3400 | 0.9655 |
30.8475 | 3500 | 0.897 |
31.5932 | 3600 | 0.7444 |
32.3390 | 3700 | 0.6445 |
33.0847 | 3800 | 0.5025 |
34.7797 | 3900 | 0.681 |
35.5254 | 4000 | 0.9227 |
36.2712 | 4100 | 0.8631 |
37.0169 | 4200 | 0.8573 |
37.7627 | 4300 | 0.9496 |
38.5085 | 4400 | 0.7243 |
39.2542 | 4500 | 0.7024 |
40.9492 | 4600 | 0.4793 |
41.6949 | 4700 | 0.8076 |
42.4407 | 4800 | 0.825 |
43.1864 | 4900 | 0.7553 |
43.9322 | 5000 | 0.6861 |
44.6780 | 5100 | 0.6589 |
45.4237 | 5200 | 0.5023 |
46.1695 | 5300 | 0.4013 |
47.8644 | 5400 | 0.4524 |
48.6102 | 5500 | 0.5891 |
49.3559 | 5600 | 0.5765 |
50.1017 | 5700 | 0.5708 |
50.8475 | 5800 | 0.479 |
51.5932 | 5900 | 0.4671 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.33.0
- 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",
}
CoSENTLoss
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
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Model tree for s2593817/sft-question-embedding
Base model
sentence-transformers/all-mpnet-base-v2