metadata

language:
  - en
tags:
  - sentence-transformers
  - cross-encoder
  - text-classification
  - generated_from_trainer
  - dataset_size:942069
  - loss:CrossEntropyLoss
base_model: distilbert/distilroberta-base
datasets:
  - sentence-transformers/all-nli
pipeline_tag: text-classification
library_name: sentence-transformers
metrics:
  - f1_macro
  - f1_micro
  - f1_weighted
co2_eq_emissions:
  emissions: 5.804161792857238
  energy_consumed: 0.01493216343846247
  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: 0.058
  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: AllNLI dev
          type: AllNLI-dev
        metrics:
          - type: f1_macro
            value: 0.8495346395196971
            name: F1 Macro
          - type: f1_micro
            value: 0.851
            name: F1 Micro
          - type: f1_weighted
            value: 0.8494545162410544
            name: F1 Weighted
      - task:
          type: cross-encoder-classification
          name: Cross Encoder Classification
        dataset:
          name: AllNLI test
          type: AllNLI-test
        metrics:
          - type: f1_macro
            value: 0.7574494684363943
            name: F1 Macro
          - type: f1_micro
            value: 0.7575803825803826
            name: F1 Micro
          - type: f1_weighted
            value: 0.7582587136974347
            name: F1 Weighted

CrossEncoder based on distilbert/distilroberta-base

This is a Cross Encoder model finetuned from distilbert/distilroberta-base on the all-nli 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:
- all-nli
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-nli")
# Get scores for pairs...
pairs = [
    ['Two women are embracing while holding to go packages.', 'The sisters are hugging goodbye while holding to go packages after just eating lunch.'],
    ['Two women are embracing while holding to go packages.', 'Two woman are holding packages.'],
    ['Two women are embracing while holding to go packages.', 'The men are fighting outside a deli.'],
    ['Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.', 'Two kids in numbered jerseys wash their hands.'],
    ['Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.', 'Two kids at a ballgame wash their hands.'],
]
scores = model.predict(pairs)
print(scores.shape)
# [5]

# ... or rank different texts based on similarity to a single text
ranks = model.rank(
    'Two women are embracing while holding to go packages.',
    [
        'The sisters are hugging goodbye while holding to go packages after just eating lunch.',
        'Two woman are holding packages.',
        'The men are fighting outside a deli.',
        'Two kids in numbered jerseys wash their hands.',
        'Two kids at a ballgame wash their hands.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Classification

Datasets: AllNLI-dev and AllNLI-test
Evaluated with CEClassificationEvaluator

Metric	AllNLI-dev	AllNLI-test
f1_macro	0.8495	0.7574
f1_micro	0.851	0.7576
f1_weighted	0.8495	0.7583

Training Details

Training Dataset

all-nli

Dataset: all-nli at d482672
Size: 942,069 training samples
Columns: premise, hypothesis, and label

Approximate statistics based on the first 1000 samples:

	premise	hypothesis	label
type	string	string	int
details	min: 23 characters mean: 69.54 characters max: 227 characters	min: 11 characters mean: 38.26 characters max: 131 characters	0: ~33.40% 1: ~33.30% 2: ~33.30%

Samples:

premise	hypothesis	label
`A person on a horse jumps over a broken down airplane.`	`A person is training his horse for a competition.`	`1`
`A person on a horse jumps over a broken down airplane.`	`A person is at a diner, ordering an omelette.`	`2`
`A person on a horse jumps over a broken down airplane.`	`A person is outdoors, on a horse.`	`0`

Loss: CrossEntropyLoss

Evaluation Dataset

all-nli

Dataset: all-nli at d482672
Size: 19,657 evaluation samples
Columns: premise, hypothesis, and label

Approximate statistics based on the first 1000 samples:

	premise	hypothesis	label
type	string	string	int
details	min: 16 characters mean: 75.01 characters max: 229 characters	min: 11 characters mean: 37.66 characters max: 116 characters	0: ~33.10% 1: ~33.30% 2: ~33.60%

Samples:

premise	hypothesis	label
`Two women are embracing while holding to go packages.`	`The sisters are hugging goodbye while holding to go packages after just eating lunch.`	`1`
`Two women are embracing while holding to go packages.`	`Two woman are holding packages.`	`0`
`Two women are embracing while holding to go packages.`	`The men are fighting outside a deli.`	`2`

Loss: CrossEntropyLoss

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	AllNLI-dev_f1_macro	AllNLI-test_f1_macro
-1	-1	-	-	0.1677	-
0.0640	100	1.0454	-	-	-
0.1280	200	0.7193	-	-	-
0.1919	300	0.6247	-	-	-
0.2559	400	0.5907	-	-	-
0.3199	500	0.5671	0.4578	0.8206	-
0.3839	600	0.5384	-	-	-
0.4479	700	0.5492	-	-	-
0.5118	800	0.5281	-	-	-
0.5758	900	0.5043	-	-	-
0.6398	1000	0.5243	0.4012	0.8415	-
0.7038	1100	0.4906	-	-	-
0.7678	1200	0.4877	-	-	-
0.8317	1300	0.4506	-	-	-
0.8957	1400	0.4728	-	-	-
0.9597	1500	0.4602	0.3731	0.8495	-
-1	-1	-	-	-	0.7574

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.015 kWh
Carbon Emitted: 0.006 kg of CO2
Hours Used: 0.058 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",
}