SentenceTransformer based on answerdotai/ModernBERT-base
This is a sentence-transformers model finetuned from answerdotai/ModernBERT-base on the json dataset. 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: answerdotai/ModernBERT-base
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 dimensions
- Similarity Function: Cosine Similarity
- Training Dataset:
- json
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: ModernBertModel
(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})
)
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 = [
'EHL tendon reconstruction',
'A Combined Surgical Approach for Extensor Hallucis Longus Reconstruction: Two Case Reports. ',
'Flexor tendon reconstruction. ',
]
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]
Evaluation
Metrics
Triplet
- Dataset:
triplet-dev
- Evaluated with
TripletEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.887 |
Training Details
Training Dataset
json
- Dataset: json
- Size: 10,053 training samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 4 tokens
- mean: 8.86 tokens
- max: 34 tokens
- min: 4 tokens
- mean: 21.84 tokens
- max: 62 tokens
- min: 3 tokens
- mean: 13.65 tokens
- max: 50 tokens
- Samples:
anchor positive negative COM-induced secretome changes in U937 monocytes
Characterization of calcium oxalate crystal-induced changes in the secretome of U937 human monocytes.
Monocytes.
Metamaterials
Sound attenuation optimization using metaporous materials tuned on exceptional points.
Metamaterials: A cat's eye for all directions.
Pediatric Parasitology
Parasitic infections among school age children 6 to 11-years-of-age in the Eastern province.
[DIALOGUE ON PEDIATRIC PARASITOLOGY].
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 64per_device_eval_batch_size
: 64learning_rate
: 0.0002num_train_epochs
: 2lr_scheduler_type
: cosine_with_restartswarmup_ratio
: 0.1bf16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 64per_device_eval_batch_size
: 64per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 0.0002weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 2max_steps
: -1lr_scheduler_type
: cosine_with_restartslr_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
: 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
: 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
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_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
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Click to expand
Epoch | Step | Training Loss | triplet-dev_cosine_accuracy |
---|---|---|---|
0 | 0 | - | 0.457 |
0.0189 | 1 | 5.2934 | - |
0.0377 | 2 | 5.2413 | - |
0.0566 | 3 | 4.9969 | - |
0.0755 | 4 | 4.5579 | - |
0.0943 | 5 | 3.9145 | - |
0.1132 | 6 | 3.3775 | - |
0.1321 | 7 | 2.8787 | - |
0.1509 | 8 | 3.0147 | - |
0.1698 | 9 | 2.7166 | - |
0.1887 | 10 | 2.7875 | - |
0.2075 | 11 | 2.3848 | - |
0.2264 | 12 | 2.1921 | - |
0.2453 | 13 | 1.7009 | - |
0.2642 | 14 | 1.7649 | - |
0.2830 | 15 | 1.7948 | - |
0.3019 | 16 | 1.5384 | - |
0.3208 | 17 | 1.6039 | - |
0.3396 | 18 | 1.3364 | - |
0.3585 | 19 | 1.3852 | - |
0.3774 | 20 | 1.2427 | - |
0.3962 | 21 | 1.3216 | - |
0.4151 | 22 | 1.4202 | - |
0.4340 | 23 | 1.2754 | - |
0.4528 | 24 | 1.281 | - |
0.4717 | 25 | 1.1709 | 0.815 |
0.4906 | 26 | 1.2363 | - |
0.5094 | 27 | 1.2169 | - |
0.5283 | 28 | 1.1495 | - |
0.5472 | 29 | 1.0066 | - |
0.5660 | 30 | 1.0478 | - |
0.5849 | 31 | 1.1511 | - |
0.6038 | 32 | 0.9992 | - |
0.6226 | 33 | 1.095 | - |
0.6415 | 34 | 1.1699 | - |
0.6604 | 35 | 0.9866 | - |
0.6792 | 36 | 1.1303 | - |
0.6981 | 37 | 1.1126 | - |
0.7170 | 38 | 0.889 | - |
0.7358 | 39 | 1.0355 | - |
0.7547 | 40 | 1.0129 | - |
0.7736 | 41 | 1.118 | - |
0.7925 | 42 | 0.8494 | - |
0.8113 | 43 | 1.0829 | - |
0.8302 | 44 | 0.8751 | - |
0.8491 | 45 | 0.8115 | - |
0.8679 | 46 | 0.8579 | - |
0.8868 | 47 | 1.1111 | - |
0.9057 | 48 | 0.9032 | - |
0.9245 | 49 | 1.0394 | - |
0.9434 | 50 | 0.9691 | 0.862 |
0.9623 | 51 | 1.023 | - |
0.9811 | 52 | 0.9465 | - |
1.0 | 53 | 0.6713 | - |
1.0189 | 54 | 0.9773 | - |
1.0377 | 55 | 0.8693 | - |
1.0566 | 56 | 0.7187 | - |
1.0755 | 57 | 0.805 | - |
1.0943 | 58 | 0.728 | - |
1.1132 | 59 | 1.0967 | - |
1.1321 | 60 | 0.7036 | - |
1.1509 | 61 | 0.8213 | - |
1.1698 | 62 | 0.57 | - |
1.1887 | 63 | 0.7006 | - |
1.2075 | 64 | 0.5091 | - |
1.2264 | 65 | 0.5758 | - |
1.2453 | 66 | 0.4484 | - |
1.2642 | 67 | 0.397 | - |
1.2830 | 68 | 0.6172 | - |
1.3019 | 69 | 0.513 | - |
1.3208 | 70 | 0.4447 | - |
1.3396 | 71 | 0.3205 | - |
1.3585 | 72 | 0.5881 | - |
1.3774 | 73 | 0.2543 | - |
1.3962 | 74 | 0.3648 | - |
1.4151 | 75 | 0.4849 | 0.876 |
1.4340 | 76 | 0.3455 | - |
1.4528 | 77 | 0.3424 | - |
1.4717 | 78 | 0.224 | - |
1.4906 | 79 | 0.18 | - |
1.5094 | 80 | 0.2255 | - |
1.5283 | 81 | 0.3024 | - |
1.5472 | 82 | 0.1835 | - |
1.5660 | 83 | 0.1946 | - |
1.5849 | 84 | 0.1958 | - |
1.6038 | 85 | 0.1568 | - |
1.6226 | 86 | 0.1626 | - |
1.6415 | 87 | 0.1774 | - |
1.6604 | 88 | 0.1934 | - |
1.6792 | 89 | 0.2426 | - |
1.6981 | 90 | 0.2958 | - |
1.7170 | 91 | 0.1606 | - |
1.7358 | 92 | 0.2281 | - |
1.7547 | 93 | 0.1786 | - |
1.7736 | 94 | 0.2241 | - |
1.7925 | 95 | 0.1909 | - |
1.8113 | 96 | 0.236 | - |
1.8302 | 97 | 0.1332 | - |
1.8491 | 98 | 0.1247 | - |
1.8679 | 99 | 0.156 | - |
1.8868 | 100 | 0.2152 | 0.889 |
1.9057 | 101 | 0.1549 | - |
1.9245 | 102 | 0.2226 | - |
1.9434 | 103 | 0.21 | - |
1.9623 | 104 | 0.2139 | - |
1.9811 | 105 | 0.1864 | - |
2.0 | 106 | 0.0719 | 0.887 |
Framework Versions
- Python: 3.12.3
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0.dev0
- PyTorch: 2.5.1
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
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",
}
MultipleNegativesRankingLoss
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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answerdotai/ModernBERT-base