Reranker-BERT-Tiny-GooAQ-BCE Open-Source Model - Calculate text similarity for tasks such as semantic search

Reranker Bert Tiny Gooaq Bce

Developed by cross-encoder-testing

This is a cross-encoder model fine-tuned from bert-tiny for calculating similarity scores between text pairs, suitable for various tasks such as semantic textual similarity and semantic search.

Text Embedding

Safetensors

EnglishOpen Source License:Apache-2.0 #Question Answering Reranking #Lightweight BERT #Semantic Matching

Downloads 37.19k

Release Time : 2/26/2025

Model Overview

This model is based on the BERT-tiny architecture and developed using the sentence-transformers library. It is primarily used to calculate similarity scores between text pairs, applicable to tasks like semantic textual similarity, semantic search, paraphrase mining, text classification, and clustering.

Model Features

Efficient and Lightweight

Based on the BERT-tiny architecture, the model is compact and computationally efficient.

Versatile for Multiple Tasks

Applicable to various tasks such as semantic textual similarity, semantic search, paraphrase mining, and text classification.

High Performance

Performs well on multiple evaluation datasets, especially achieving a map of 0.5677 on the GooAQ-dev dataset.

Model Capabilities

Calculate Text Similarity

Semantic Search

Text Classification

Text Clustering

Paraphrase Mining

Use Cases

Information Retrieval

Answer Reranking in QA Systems

Rerank candidate answers by relevance to improve QA system quality

Achieved a map of 0.5677 on the GooAQ-dev dataset.

Content Recommendation

🚀 BERT-tiny trained on GooAQ

This is a Cross Encoder model that computes scores for text pairs. It can be used for semantic textual similarity, search, paraphrase mining, classification, clustering, etc. Finetuned from prajjwal1/bert-tiny using the sentence-transformers library.

🚀 Quick Start

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.

✨ Features

Versatile Applications: Suitable for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Finetuned Model: Based on the prajjwal1/bert-tiny base model, finetuned to better handle specific tasks.

📦 Installation

First install the Sentence Transformers library:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

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': ...}, ...]

📚 Documentation

Model Details

Model Description

Property	Details
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

Evaluation

Metrics

Cross Encoder Reranking

Datasets: gooaq-dev, NanoMSMARCO, NanoNFCorpus and NanoNQ
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, and label

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: steps
per_device_train_batch_size: 2048
per_device_eval_batch_size: 2048
learning_rate: 0.0005
num_train_epochs: 1
warmup_ratio: 0.1
seed: 12
bf16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 2048
per_device_eval_batch_size: 2048
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: 0.0005
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: 0.1
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: 12
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: batch_sampler
multi_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

📄 License

This project is licensed under the Apache 2.0 license.

📄 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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