Reranker Bert Tiny Gooaq Bce Tanh V4
これはbert-tinyからファインチューニングされたクロスエンコーダーモデルで、テキストペアの類似度スコアを計算するために使用され、意味的テキスト類似性、意味検索などのタスクに適しています。
ダウンロード数 1,971
リリース時間 : 3/4/2025
モデル概要
このモデルはBERT-tinyアーキテクチャに基づいており、sentence-transformersライブラリを使用して開発され、主にテキストペアの類似度計算とランキングタスクに使用されます。
モデル特徴
効率的で軽量
BERT-tinyアーキテクチャに基づき、モデルパラメータが少なく、計算効率が高い
意味理解
テキストの意味を効果的に理解し、類似度を計算できる
マルチタスク対応
意味検索、テキスト分類、クラスタリングなど、さまざまなタスクに使用可能
モデル能力
テキスト類似度計算
意味検索
テキスト再ランキング
質問応答マッチング
テキスト分類
使用事例
情報検索
検索結果の再ランキング
検索エンジンが返した結果を意味的に再ランキングする
gooaq-devデータセットでmapが0.5677を達成
質問応答システム
質問応答マッチング
質問と候補回答の関連性を計算する
NanoNQデータセットでndcg@10が0.4859を達成
language:
- en license: apache-2.0 tags:
- sentence-transformers
- cross-encoder
- text-classification
- generated_from_trainer
- dataset_size:578402
- loss:BinaryCrossEntropyLoss base_model: prajjwal1/bert-tiny pipeline_tag: text-ranking library_name: sentence-transformers metrics:
- map
- mrr@10
- ndcg@10 co2_eq_emissions: emissions: 7.3866990525881215 energy_consumed: 0.019003501532248668 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.099 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index:
- name: BERT-tiny trained on GooAQ results: []
BERT-tiny trained on GooAQ
This is a Cross Encoder model finetuned from prajjwal1/bert-tiny using the sentence-transformers library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
This model was trained using train_script.py.
Model Details
Model Description
- Model Type: Cross Encoder
- Base model: prajjwal1/bert-tiny
- Maximum Sequence Length: 512 tokens
- Number of Output Labels: 1 label
- Language: en
- License: apache-2.0
Model Sources
- Documentation: Sentence Transformers Documentation
- Documentation: Cross Encoder Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Cross Encoders on Hugging Face
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 CrossEncoder
# Download from the 🤗 Hub
model = CrossEncoder("cross-encoder-testing/reranker-bert-tiny-gooaq-bce")
# Get scores for pairs of texts
pairs = [
['are javascript developers in demand?', "JavaScript is the skill that is most in-demand for IT in 2020, according to a report from developer skills tester DevSkiller. The report, “Top IT Skills report 2020: Demand and Hiring Trends,” has JavaScript switching places with Java when compared to last year's report, with Java in third place this year, behind SQL."],
['are javascript developers in demand?', 'In one line difference between the two is: JavaScript is the programming language where as AngularJS is a framework based on JavaScript. ... It is also the basic for all java script based technologies like jquery, angular JS, bootstrap JS and so on. Angular JS is a framework written in javascript and uses MVC architecture.'],
['are javascript developers in demand?', 'Java applications are run in a virtual machine or web browser while JavaScript is run on a web browser. Java code is compiled whereas while JavaScript code is in text and in a web page. JavaScript is an OOP scripting language, whereas Java is an OOP programming language.'],
['are javascript developers in demand?', 'Things in the body tag are the things that should be displayed: the actual content. Javascript in the body is executed as it is read and as the page is rendered. Javascript in the head is interpreted before anything is rendered.'],
['are javascript developers in demand?', 'Web apps tend to be built using JavaScript, CSS and HTML5. Unlike mobile apps, there is no standard software development kit for building web apps. However, developers do have access to templates. Compared to mobile apps, web apps are usually quicker and easier to build — but they are much simpler in terms of features.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'are javascript developers in demand?',
[
"JavaScript is the skill that is most in-demand for IT in 2020, according to a report from developer skills tester DevSkiller. The report, “Top IT Skills report 2020: Demand and Hiring Trends,” has JavaScript switching places with Java when compared to last year's report, with Java in third place this year, behind SQL.",
'In one line difference between the two is: JavaScript is the programming language where as AngularJS is a framework based on JavaScript. ... It is also the basic for all java script based technologies like jquery, angular JS, bootstrap JS and so on. Angular JS is a framework written in javascript and uses MVC architecture.',
'Java applications are run in a virtual machine or web browser while JavaScript is run on a web browser. Java code is compiled whereas while JavaScript code is in text and in a web page. JavaScript is an OOP scripting language, whereas Java is an OOP programming language.',
'Things in the body tag are the things that should be displayed: the actual content. Javascript in the body is executed as it is read and as the page is rendered. Javascript in the head is interpreted before anything is rendered.',
'Web apps tend to be built using JavaScript, CSS and HTML5. Unlike mobile apps, there is no standard software development kit for building web apps. However, developers do have access to templates. Compared to mobile apps, web apps are usually quicker and easier to build — but they are much simpler in terms of features.',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
Evaluation
Metrics
Cross Encoder Reranking
- Datasets:
gooaq-dev
,NanoMSMARCO
,NanoNFCorpus
andNanoNQ
- Evaluated with
CrossEncoderRerankingEvaluator
Metric | gooaq-dev | NanoMSMARCO | NanoNFCorpus | NanoNQ |
---|---|---|---|---|
map | 0.5677 (+0.0366) | 0.4280 (-0.0616) | 0.3397 (+0.0787) | 0.4149 (-0.0047) |
mrr@10 | 0.5558 (+0.0318) | 0.4129 (-0.0646) | 0.5196 (+0.0198) | 0.4132 (-0.0135) |
ndcg@10 | 0.6157 (+0.0245) | 0.4772 (-0.0632) | 0.3308 (+0.0058) | 0.4859 (-0.0147) |
Cross Encoder Nano BEIR
- Dataset:
NanoBEIR_R100_mean
- Evaluated with
CrossEncoderNanoBEIREvaluator
Metric | Value |
---|---|
map | 0.3942 (+0.0041) |
mrr@10 | 0.4486 (-0.0194) |
ndcg@10 | 0.4313 (-0.0241) |
Training Details
Training Dataset
Unnamed Dataset
- Size: 578,402 training samples
- Columns:
question
,answer
, andlabel
- Approximate statistics based on the first 1000 samples:
question answer label type string string int details - min: 21 characters
- mean: 43.81 characters
- max: 96 characters
- min: 51 characters
- mean: 252.46 characters
- max: 405 characters
- 0: ~82.90%
- 1: ~17.10%
- Samples:
question answer label are javascript developers in demand?
JavaScript is the skill that is most in-demand for IT in 2020, according to a report from developer skills tester DevSkiller. The report, “Top IT Skills report 2020: Demand and Hiring Trends,” has JavaScript switching places with Java when compared to last year's report, with Java in third place this year, behind SQL.
1
are javascript developers in demand?
In one line difference between the two is: JavaScript is the programming language where as AngularJS is a framework based on JavaScript. ... It is also the basic for all java script based technologies like jquery, angular JS, bootstrap JS and so on. Angular JS is a framework written in javascript and uses MVC architecture.
0
are javascript developers in demand?
Java applications are run in a virtual machine or web browser while JavaScript is run on a web browser. Java code is compiled whereas while JavaScript code is in text and in a web page. JavaScript is an OOP scripting language, whereas Java is an OOP programming language.
0
- Loss:
BinaryCrossEntropyLoss
with these parameters:{ "activation_fct": "torch.nn.modules.linear.Identity", "pos_weight": 5 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 2048per_device_eval_batch_size
: 2048learning_rate
: 0.0005num_train_epochs
: 1warmup_ratio
: 0.1seed
: 12bf16
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 2048per_device_eval_batch_size
: 2048per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 0.0005weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 1max_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
: 12data_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
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | gooaq-dev_ndcg@10 | NanoMSMARCO_ndcg@10 | NanoNFCorpus_ndcg@10 | NanoNQ_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 |
---|---|---|---|---|---|---|---|
-1 | -1 | - | 0.0887 (-0.5025) | 0.0063 (-0.5341) | 0.3262 (+0.0012) | 0.0000 (-0.5006) | 0.1108 (-0.3445) |
0.0035 | 1 | 1.1945 | - | - | - | - | - |
0.0707 | 20 | 1.1664 | 0.4082 (-0.1830) | 0.1805 (-0.3600) | 0.3168 (-0.0083) | 0.2243 (-0.2763) | 0.2405 (-0.2149) |
0.1413 | 40 | 1.1107 | 0.5260 (-0.0652) | 0.3453 (-0.1951) | 0.3335 (+0.0085) | 0.3430 (-0.1576) | 0.3406 (-0.1147) |
0.2120 | 60 | 1.022 | 0.5623 (-0.0289) | 0.3929 (-0.1475) | 0.3512 (+0.0262) | 0.3472 (-0.1535) | 0.3638 (-0.0916) |
0.2827 | 80 | 0.973 | 0.5691 (-0.0221) | 0.4048 (-0.1356) | 0.3530 (+0.0280) | 0.3833 (-0.1174) | 0.3804 (-0.0750) |
0.3534 | 100 | 0.963 | 0.5814 (-0.0098) | 0.4385 (-0.1019) | 0.3471 (+0.0221) | 0.4227 (-0.0779) | 0.4028 (-0.0526) |
0.4240 | 120 | 0.9419 | 0.5963 (+0.0050) | 0.4106 (-0.1298) | 0.3540 (+0.0289) | 0.4843 (-0.0163) | 0.4163 (-0.0391) |
0.4947 | 140 | 0.9331 | 0.5953 (+0.0041) | 0.4310 (-0.1094) | 0.3367 (+0.0117) | 0.4163 (-0.0843) | 0.3947 (-0.0607) |
0.5654 | 160 | 0.9263 | 0.6070 (+0.0158) | 0.4626 (-0.0778) | 0.3443 (+0.0193) | 0.4823 (-0.0184) | 0.4297 (-0.0256) |
0.6360 | 180 | 0.9212 | 0.6069 (+0.0156) | 0.4602 (-0.0802) | 0.3391 (+0.0141) | 0.4782 (-0.0224) | 0.4258 (-0.0295) |
0.7067 | 200 | 0.901 | 0.6126 (+0.0214) | 0.4602 (-0.0803) | 0.3413 (+0.0162) | 0.4780 (-0.0227) | 0.4265 (-0.0289) |
0.7774 | 220 | 0.8997 | 0.6136 (+0.0224) | 0.4801 (-0.0604) | 0.3349 (+0.0098) | 0.4903 (-0.0103) | 0.4351 (-0.0203) |
0.8481 | 240 | 0.9021 | 0.6132 (+0.0220) | 0.4850 (-0.0554) | 0.3438 (+0.0188) | 0.4855 (-0.0151) | 0.4381 (-0.0173) |
0.9187 | 260 | 0.9013 | 0.6188 (+0.0276) | 0.4820 (-0.0584) | 0.3387 (+0.0137) | 0.4851 (-0.0156) | 0.4353 (-0.0201) |
0.9894 | 280 | 0.8996 | 0.6157 (+0.0245) | 0.4772 (-0.0632) | 0.3305 (+0.0054) | 0.4859 (-0.0147) | 0.4312 (-0.0242) |
-1 | -1 | - | 0.6157 (+0.0245) | 0.4772 (-0.0632) | 0.3308 (+0.0058) | 0.4859 (-0.0147) | 0.4313 (-0.0241) |
Environmental Impact
Carbon emissions were measured using CodeCarbon.
- Energy Consumed: 0.019 kWh
- Carbon Emitted: 0.007 kg of CO2
- Hours Used: 0.099 hours
Training Hardware
- On Cloud: No
- GPU Model: 1 x NVIDIA GeForce RTX 3090
- CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
- RAM Size: 31.78 GB
Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.48.3
- PyTorch: 2.5.0+cu121
- Accelerate: 1.3.0
- Datasets: 2.20.0
- 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",
}
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