metadata

language:
  - en
tags:
  - sentence-transformers
  - cross-encoder
  - text-classification
  - generated_from_trainer
  - dataset_size:404290
  - loss:BinaryCrossEntropyLoss
base_model: distilbert/distilroberta-base
datasets:
  - sentence-transformers/quora-duplicates
pipeline_tag: text-classification
library_name: sentence-transformers
metrics:
  - accuracy
  - accuracy_threshold
  - f1
  - f1_threshold
  - precision
  - recall
  - average_precision
co2_eq_emissions:
  emissions: 26.889480385249758
  energy_consumed: 0.06917762292257246
  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: 0.214
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: CrossEncoder based on distilbert/distilroberta-base
    results:
      - task:
          type: cross-encoder-classification
          name: Cross Encoder Classification
        dataset:
          name: quora duplicates dev
          type: quora-duplicates-dev
        metrics:
          - type: accuracy
            value: 0.8938
            name: Accuracy
          - type: accuracy_threshold
            value: 0.5088549852371216
            name: Accuracy Threshold
          - type: f1
            value: 0.8612281373675477
            name: F1
          - type: f1_threshold
            value: 0.3856155276298523
            name: F1 Threshold
          - type: precision
            value: 0.8182920912178554
            name: Precision
          - type: recall
            value: 0.908919428725411
            name: Recall
          - type: average_precision
            value: 0.920292628179356
            name: Average Precision
      - task:
          type: cross-encoder-classification
          name: Cross Encoder Classification
        dataset:
          name: quora duplicates test
          type: quora-duplicates-test
        metrics:
          - type: accuracy
            value: 0.8938
            name: Accuracy
          - type: accuracy_threshold
            value: 0.5091445446014404
            name: Accuracy Threshold
          - type: f1
            value: 0.8612281373675477
            name: F1
          - type: f1_threshold
            value: 0.38580775260925293
            name: F1 Threshold
          - type: precision
            value: 0.8182920912178554
            name: Precision
          - type: recall
            value: 0.908919428725411
            name: Recall
          - type: average_precision
            value: 0.92029239602284
            name: Average Precision

CrossEncoder based on distilbert/distilroberta-base

This is a Cross Encoder model finetuned from distilbert/distilroberta-base on the quora-duplicates dataset 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.

Model Details

Model Description

Model Type: Cross Encoder
Base model: distilbert/distilroberta-base
Maximum Sequence Length: 514 tokens
Training Dataset:
- quora-duplicates
Language: en

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("sentence_transformers_model_id")
# Get scores for pairs...
pairs = [
    ['What is the step by step guide to invest in share market in india?', 'What is the step by step guide to invest in share market?'],
    ['What is the story of Kohinoor (Koh-i-Noor) Diamond?', 'What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?'],
    ['How can I increase the speed of my internet connection while using a VPN?', 'How can Internet speed be increased by hacking through DNS?'],
    ['Why am I mentally very lonely? How can I solve it?', 'Find the remainder when [math]23^{24}[/math] is divided by 24,23?'],
    ['Which one dissolve in water quikly sugar, salt, methane and carbon di oxide?', 'Which fish would survive in salt water?'],
]
scores = model.predict(pairs)
print(scores.shape)
# [5]

# ... or rank different texts based on similarity to a single text
ranks = model.rank(
    'What is the step by step guide to invest in share market in india?',
    [
        'What is the step by step guide to invest in share market?',
        'What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?',
        'How can Internet speed be increased by hacking through DNS?',
        'Find the remainder when [math]23^{24}[/math] is divided by 24,23?',
        'Which fish would survive in salt water?',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Classification

Datasets: quora-duplicates-dev and quora-duplicates-test
Evaluated with CEClassificationEvaluator

Metric	quora-duplicates-dev	quora-duplicates-test
accuracy	0.8938	0.8938
accuracy_threshold	0.5089	0.5091
f1	0.8612	0.8612
f1_threshold	0.3856	0.3858
precision	0.8183	0.8183
recall	0.9089	0.9089
average_precision	0.9203	0.9203

Training Details

Training Dataset

quora-duplicates

Dataset: quora-duplicates at 451a485
Size: 404,290 training samples
Columns: sentence1, sentence2, and label

Approximate statistics based on the first 1000 samples:

	sentence1	sentence2	label
type	string	string	int
details	min: 1 characters mean: 59.15 characters max: 354 characters	min: 6 characters mean: 60.74 characters max: 399 characters	0: ~64.20% 1: ~35.80%

Samples:

sentence1	sentence2	label
`What are the features of the Indian caste system?`	`What triggers you the most when you play video games?`	`0`
`What is the best place to learn Mandarin Chinese in Singapore?`	`What is the best place in Singapore for durian in December?`	`0`
`What will be Hillary Clinton's India policy if she wins the election?`	`How would the bilateral relationship between India and the USA be under Hillary Clinton's presidency?`	`1`

Loss: BinaryCrossEntropyLoss

Evaluation Dataset

quora-duplicates

Dataset: quora-duplicates at 451a485
Size: 404,290 evaluation samples
Columns: sentence1, sentence2, and label

Approximate statistics based on the first 1000 samples:

	sentence1	sentence2	label
type	string	string	int
details	min: 11 characters mean: 57.9 characters max: 244 characters	min: 12 characters mean: 59.33 characters max: 221 characters	0: ~62.00% 1: ~38.00%

Samples:

sentence1	sentence2	label
`What is the step by step guide to invest in share market in india?`	`What is the step by step guide to invest in share market?`	`0`
`What is the story of Kohinoor (Koh-i-Noor) Diamond?`	`What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?`	`0`
`How can I increase the speed of my internet connection while using a VPN?`	`How can Internet speed be increased by hacking through DNS?`	`0`

Loss: BinaryCrossEntropyLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 1
warmup_ratio: 0.1
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: 64
per_device_eval_batch_size: 64
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: 5e-05
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: 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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	quora-duplicates-dev_average_precision	quora-duplicates-test_average_precision
-1	-1	-	-	0.3711	-
0.0167	100	0.6574	-	-	-
0.0333	200	0.4804	-	-	-
0.0500	300	0.4406	-	-	-
0.0666	400	0.4208	-	-	-
0.0833	500	0.3929	0.3958	0.8210	-
0.0999	600	0.3986	-	-	-
0.1166	700	0.3743	-	-	-
0.1332	800	0.3938	-	-	-
0.1499	900	0.3602	-	-	-
0.1665	1000	0.3714	0.3437	0.8565	-
0.1832	1100	0.3486	-	-	-
0.1998	1200	0.3479	-	-	-
0.2165	1300	0.3417	-	-	-
0.2331	1400	0.3425	-	-	-
0.2498	1500	0.3353	0.3264	0.8742	-
0.2664	1600	0.3335	-	-	-
0.2831	1700	0.3274	-	-	-
0.2998	1800	0.3284	-	-	-
0.3164	1900	0.3118	-	-	-
0.3331	2000	0.3073	0.3282	0.8826	-
0.3497	2100	0.3233	-	-	-
0.3664	2200	0.3072	-	-	-
0.3830	2300	0.314	-	-	-
0.3997	2400	0.3065	-	-	-
0.4163	2500	0.3046	0.2877	0.8930	-
0.4330	2600	0.2857	-	-	-
0.4496	2700	0.285	-	-	-
0.4663	2800	0.2957	-	-	-
0.4829	2900	0.2965	-	-	-
0.4996	3000	0.2824	0.2842	0.8998	-
0.5162	3100	0.3019	-	-	-
0.5329	3200	0.2841	-	-	-
0.5495	3300	0.2981	-	-	-
0.5662	3400	0.2878	-	-	-
0.5828	3500	0.278	0.2803	0.9061	-
0.5995	3600	0.2841	-	-	-
0.6162	3700	0.2794	-	-	-
0.6328	3800	0.2808	-	-	-
0.6495	3900	0.27	-	-	-
0.6661	4000	0.2719	0.2697	0.9091	-
0.6828	4100	0.2792	-	-	-
0.6994	4200	0.2669	-	-	-
0.7161	4300	0.2696	-	-	-
0.7327	4400	0.2642	-	-	-
0.7494	4500	0.2684	0.2591	0.9140	-
0.7660	4600	0.2593	-	-	-
0.7827	4700	0.2756	-	-	-
0.7993	4800	0.2584	-	-	-
0.8160	4900	0.2525	-	-	-
0.8326	5000	0.267	0.2540	0.9168	-
0.8493	5100	0.2612	-	-	-
0.8659	5200	0.2607	-	-	-
0.8826	5300	0.2565	-	-	-
0.8993	5400	0.2432	-	-	-
0.9159	5500	0.2568	0.2489	0.9198	-
0.9326	5600	0.2572	-	-	-
0.9492	5700	0.2658	-	-	-
0.9659	5800	0.2568	-	-	-
0.9825	5900	0.2539	-	-	-
0.9992	6000	0.2458	0.2503	0.9203	-
-1	-1	-	-	-	0.9203

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.069 kWh
Carbon Emitted: 0.027 kg of CO2
Hours Used: 0.214 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: 4.49.0.dev0
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",
}