metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:100006
  - loss:CachedMultipleNegativesRankingLoss
base_model: answerdotai/ModernBERT-base
widget:
  - source_sentence: how much weight can you lose in a week healthy?
    sentences:
      - Biology
      - >-
        Summary: According to experts, losing 1–2 pounds (0.45–0.9 kg) per week
        is a healthy and safe rate, while losing more than this is considered
        too fast. However, you may lose more than that during your first week of
        an exercise or diet plan.
      - >-
        The number of valence electrons is the number of electrons in the outer
        shell, that the atom uses for bonding. Nitrogen has 5 electrons in its
        n=2 (outer) shell.
  - source_sentence: how long after having a baby can i get a tattoo?
    sentences:
      - >-
        It is suggested that mothers wait at least until 9-12 months after
        birth, when the child is no longer dependent solely on breastmilk before
        getting a tattoo. Reputable tattoo artists will have a waiver for the
        client to sign that asks about pregnancy and breastfeeding.
      - Medicine
      - >-
        Americans on average are down to 44 gallons of soda per year, and up to
        about 58 gallons of water. That's 7,242 ounces of water annually -- 20
        ounces daily, which is 2.5 cups.
  - source_sentence: is all uhmw anti static?
    sentences:
      - >-
        The bacteria Streptococcus pyogenes causes it. It's most common in
        infants and children, but it frequently occurs in teenagers and adults
        as well. It causes white streaks or spots in the throat.
      - Chemistry
      - >-
        UHMW is available in a special anti-static grade that helps protect
        against EsD (static discharge) or to help keep dust and particles from
        building up on the product surface. The anti-static additives are
        built-in so the anti-static properties will last throughout the life of
        the material.
  - source_sentence: is closing cost tax deductible?
    sentences:
      - Medicine
      - >-
        1 tablespoon (tbsp) of granulated sugar equals to 12.5998 grams (g) in
        granulated sugar mass.
      - >-
        In general, the only settlement or closing costs you can deduct are home
        mortgage interest and certain real estate taxes. You deduct them in the
        year you buy your home if you itemize your deductions. ... See IRS
        Publication 530, "Tax Information for Homeowners" and look for
        "Settlement or closing costs" for more details.
  - source_sentence: what is the connection between cancer and the cell cycle?
    sentences:
      - Biology
      - >-
        Conclusion. Cancer is unchecked cell growth. Mutations in genes can
        cause cancer by accelerating cell division rates or inhibiting normal
        controls on the system, such as cell cycle arrest or programmed cell
        death. As a mass of cancerous cells grows, it can develop into a tumor.
      - >-
        Your vomit may appear black if the blood has been oxidized by the acids
        in your stomach. The iron in your blood turns brown to black with time.
        Since the blood is no longer bright red, it means that the bleeding has
        either stopped or is only happening in a small amount.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: SentenceTransformer based on answerdotai/ModernBERT-base
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: cosine_accuracy@1
            value: 0.1
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.18
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.24
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.34
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.1
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.06
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.04800000000000001
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.034
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.1
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.15
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.21
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.31
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.19343658524041285
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.16590476190476192
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.17642959153410534
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: cosine_accuracy@1
            value: 0.12
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.28
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.4
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.52
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.12
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.09333333333333332
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.08
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.052000000000000005
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.12
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.28
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.4
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.52
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2984940860938879
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2304365079365079
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.24691442502099614
            name: Cosine Map@100
      - task:
          type: nano-beir
          name: Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: cosine_accuracy@1
            value: 0.11
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.23
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.32
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.43000000000000005
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.11
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.07666666666666666
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.064
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.043000000000000003
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.11
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.21500000000000002
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.305
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.41500000000000004
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.24596533566715037
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.1981706349206349
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.21167200827755073
            name: Cosine Map@100

SentenceTransformer based on answerdotai/ModernBERT-base

This is a sentence-transformers model finetuned from answerdotai/ModernBERT-base on the csv 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: 8192 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- csv

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': 8192, '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 = [
    'what is the connection between cancer and the cell cycle?',
    'Conclusion. Cancer is unchecked cell growth. Mutations in genes can cause cancer by accelerating cell division rates or inhibiting normal controls on the system, such as cell cycle arrest or programmed cell death. As a mass of cancerous cells grows, it can develop into a tumor.',
    'Biology',
]
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

Information Retrieval

Datasets: NanoNQ and NanoMSMARCO
Evaluated with InformationRetrievalEvaluator

Metric	NanoNQ	NanoMSMARCO
cosine_accuracy@1	0.1	0.12
cosine_accuracy@3	0.18	0.28
cosine_accuracy@5	0.24	0.4
cosine_accuracy@10	0.34	0.52
cosine_precision@1	0.1	0.12
cosine_precision@3	0.06	0.0933
cosine_precision@5	0.048	0.08
cosine_precision@10	0.034	0.052
cosine_recall@1	0.1	0.12
cosine_recall@3	0.15	0.28
cosine_recall@5	0.21	0.4
cosine_recall@10	0.31	0.52
cosine_ndcg@10	0.1934	0.2985
cosine_mrr@10	0.1659	0.2304
cosine_map@100	0.1764	0.2469

Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with NanoBEIREvaluator

Metric	Value
cosine_accuracy@1	0.11
cosine_accuracy@3	0.23
cosine_accuracy@5	0.32
cosine_accuracy@10	0.43
cosine_precision@1	0.11
cosine_precision@3	0.0767
cosine_precision@5	0.064
cosine_precision@10	0.043
cosine_recall@1	0.11
cosine_recall@3	0.215
cosine_recall@5	0.305
cosine_recall@10	0.415
cosine_ndcg@10	0.246
cosine_mrr@10	0.1982
cosine_map@100	0.2117

Training Details

Training Dataset

csv

Dataset: csv
Size: 100,006 training samples
Columns: question, answer, and category

Approximate statistics based on the first 1000 samples:

	question	answer	category
type	string	string	string
details	min: 8 tokens mean: 11.91 tokens max: 20 tokens	min: 16 tokens mean: 57.49 tokens max: 136 tokens	min: 4 tokens mean: 4.0 tokens max: 4 tokens

Samples:

question	answer	category
`how many times a week should you use heat on your hair?`	`Don't style hair with heat every day. Hot tools can also make hair look crispy and create split ends if overused. Blow out hair 3-5 times a week and try to limit your flat iron/curling iron usage to 1-2 times a week.”`	`Medicine`
`do african violets like to be root bound?`	`African violets only bloom when they're root bound. When it is time to repot, be sure to use an organic potting soil made specifically for African violets, such as Espoma's African Violet Mix. They flower best in small pots — choose one that's about a third of the diameter of their leaf spread.`	`Biology`
`is pgwp exempt from lmia?`	`The PGWP is exempt from Labour Market Impact Assessment (LMIA) requirements. The candidate must have attended a recognized post-secondary school, or a secondary school that offers qualifying programs, for at least eight months.`	`Medicine`

Loss: CachedMultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
learning_rate: 0.0001
num_train_epochs: 1
warmup_ratio: 0.05
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 0.0001
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.05
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	NanoNQ_cosine_ndcg@10	NanoMSMARCO_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10
0	0	-	0.0388	0.0863	0.0626
0.0763	10	0.5482	-	-	-
0.1527	20	0.1079	-	-	-
0.2290	30	0.1491	-	-	-
0.3053	40	0.1381	-	-	-
0.3817	50	0.0873	0.0909	0.2197	0.1553
0.4580	60	0.133	-	-	-
0.5344	70	0.0539	-	-	-
0.6107	80	0.029	-	-	-
0.6870	90	0.0008	-	-	-
0.7634	100	0.0997	0.1982	0.2657	0.2320
0.8397	110	0.04	-	-	-
0.9160	120	0.0053	-	-	-
0.9924	130	0.0095	-	-	-
1.0	131	-	0.1934	0.2985	0.2460

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",
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}