metadata

language:
  - en
tags:
  - sentence-transformers
  - cross-encoder
  - text-classification
  - generated_from_trainer
  - dataset_size:5749
  - loss:BinaryCrossEntropyLoss
base_model: distilbert/distilroberta-base
datasets:
  - sentence-transformers/stsb
pipeline_tag: text-classification
library_name: sentence-transformers
metrics:
  - pearson
  - spearman
co2_eq_emissions:
  emissions: 2.6550346776830636
  energy_consumed: 0.006830514578476734
  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: 0.031
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: CrossEncoder based on distilbert/distilroberta-base
    results:
      - task:
          type: cross-encoder-correlation
          name: Cross Encoder Correlation
        dataset:
          name: stsb validation
          type: stsb-validation
        metrics:
          - type: pearson
            value: 0.877295960646044
            name: Pearson
          - type: spearman
            value: 0.8754151440157509
            name: Spearman
      - task:
          type: cross-encoder-correlation
          name: Cross Encoder Correlation
        dataset:
          name: stsb test
          type: stsb-test
        metrics:
          - type: pearson
            value: 0.8503341584157813
            name: Pearson
          - type: spearman
            value: 0.8388642249054395
            name: Spearman

CrossEncoder based on distilbert/distilroberta-base

This is a Cross Encoder model finetuned from distilbert/distilroberta-base on the stsb 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:
- stsb
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("tomaarsen/reranker-distilroberta-base-stsb")
# Get scores for pairs...
pairs = [
    ['A man with a hard hat is dancing.', 'A man wearing a hard hat is dancing.'],
    ['A young child is riding a horse.', 'A child is riding a horse.'],
    ['A man is feeding a mouse to a snake.', 'The man is feeding a mouse to the snake.'],
    ['A woman is playing the guitar.', 'A man is playing guitar.'],
    ['A woman is playing the flute.', 'A man is playing a flute.'],
]
scores = model.predict(pairs)
print(scores.shape)
# [5]

# ... or rank different texts based on similarity to a single text
ranks = model.rank(
    'A man with a hard hat is dancing.',
    [
        'A man wearing a hard hat is dancing.',
        'A child is riding a horse.',
        'The man is feeding a mouse to the snake.',
        'A man is playing guitar.',
        'A man is playing a flute.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Correlation

Datasets: stsb-validation and stsb-test
Evaluated with CECorrelationEvaluator

Metric	stsb-validation	stsb-test
pearson	0.8773	0.8503
spearman	0.8754	0.8389

Training Details

Training Dataset

stsb

Dataset: stsb at ab7a5ac
Size: 5,749 training samples
Columns: sentence1, sentence2, and score

Approximate statistics based on the first 1000 samples:

	sentence1	sentence2	score
type	string	string	float
details	min: 16 characters mean: 31.92 characters max: 113 characters	min: 16 characters mean: 31.51 characters max: 94 characters	min: 0.0 mean: 0.45 max: 1.0

Samples:

sentence1	sentence2	score
`A plane is taking off.`	`An air plane is taking off.`	`1.0`
`A man is playing a large flute.`	`A man is playing a flute.`	`0.76`
`A man is spreading shreded cheese on a pizza.`	`A man is spreading shredded cheese on an uncooked pizza.`	`0.76`

Loss: BinaryCrossEntropyLoss

Evaluation Dataset

stsb

Dataset: stsb at ab7a5ac
Size: 1,500 evaluation samples
Columns: sentence1, sentence2, and score

Approximate statistics based on the first 1000 samples:

	sentence1	sentence2	score
type	string	string	float
details	min: 12 characters mean: 57.37 characters max: 144 characters	min: 17 characters mean: 56.84 characters max: 141 characters	min: 0.0 mean: 0.42 max: 1.0

Samples:

sentence1	sentence2	score
`A man with a hard hat is dancing.`	`A man wearing a hard hat is dancing.`	`1.0`
`A young child is riding a horse.`	`A child is riding a horse.`	`0.95`
`A man is feeding a mouse to a snake.`	`The man is feeding a mouse to the snake.`	`1.0`

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: 4
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: 4
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	stsb-validation_spearman	stsb-test_spearman
-1	-1	-	-	-0.0150	-
0.2222	20	0.6905	-	-	-
0.4444	40	0.6548	-	-	-
0.6667	60	0.5906	-	-	-
0.8889	80	0.5631	0.5475	0.8589	-
1.1111	100	0.5517	-	-	-
1.3333	120	0.5473	-	-	-
1.5556	140	0.5454	-	-	-
1.7778	160	0.5402	0.5346	0.8760	-
2.0	180	0.542	-	-	-
2.2222	200	0.5229	-	-	-
2.4444	220	0.524	-	-	-
2.6667	240	0.5286	0.5373	0.8744	-
2.8889	260	0.5236	-	-	-
3.1111	280	0.5269	-	-	-
3.3333	300	0.5209	-	-	-
3.5556	320	0.5115	0.5409	0.8754	-
3.7778	340	0.5149	-	-	-
4.0	360	0.5084	-	-	-
-1	-1	-	-	-	0.8389

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.007 kWh
Carbon Emitted: 0.003 kg of CO2
Hours Used: 0.031 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",
}