This is a Cross Encoder model finetuned from cross-encoder/ms-marco-MiniLM-L6-v2 using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

Model Type: Cross Encoder
Base model: cross-encoder/ms-marco-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Number of Output Labels: 1 label

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.

python

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("cross_encoder_model_id")
# Get scores for pairs of texts
pairs = [
    ['How to implement error handling', 'Implementation guide for error handling. Step-by-step instructions with code examples. Covers setup, configuration, and best practices.'],
    ['Where is UserModel defined', 'Source code location and module structure. Class definitions and interface documentation. File paths and import statements.'],
    ['Add metrics collection to scheduler', 'Feature implementation guide with API extensions. Configuration options and customization points. Testing requirements.'],
    ['Fix bug in data processor', 'Company holiday schedule and PTO policy. HR contact information.'],
    ['Refactor authentication middleware for better readability', 'Refactoring patterns and code improvement strategies. Before/after examples with measurable improvements. Migration guide.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'How to implement error handling',
    [
        'Implementation guide for error handling. Step-by-step instructions with code examples. Covers setup, configuration, and best practices.',
        'Source code location and module structure. Class definitions and interface documentation. File paths and import statements.',
        'Feature implementation guide with API extensions. Configuration options and customization points. Testing requirements.',
        'Company holiday schedule and PTO policy. HR contact information.',
        'Refactoring patterns and code improvement strategies. Before/after examples with measurable improvements. Migration guide.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Correlation

Dataset: val
Evaluated with CECorrelationEvaluator

Metric	Value
pearson	0.9929
spearman	0.9384

Training Details

Training Dataset

Unnamed Dataset

Size: 1,600 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 20 characters
mean: 32.95 characters
max: 61 characters
min: 48 characters
mean: 110.73 characters
max: 158 characters
min: 0.0
mean: 0.56
max: 1.0

Samples:

sentence_0	sentence_1	label
`How to implement error handling`	`Implementation guide for error handling. Step-by-step instructions with code examples. Covers setup, configuration, and best practices.`	`1.0`
`Where is UserModel defined`	`Source code location and module structure. Class definitions and interface documentation. File paths and import statements.`	`1.0`
`Add metrics collection to scheduler`	`Feature implementation guide with API extensions. Configuration options and customization points. Testing requirements.`	`1.0`

Loss: BinaryCrossEntropyLoss with these parameters:
json
1{ 2 "activation_fn": "torch.nn.modules.linear.Identity", 3 "pos_weight": null 4}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
eval_strategy: steps
per_device_eval_batch_size: 16

All Hyperparameters

Click to expand

per_device_train_batch_size: 16
num_train_epochs: 3
max_steps: -1
learning_rate: 5e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1
label_smoothing_factor: 0.0
bf16: False
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: trackio
eval_strategy: steps
per_device_eval_batch_size: 16
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	val_spearman
1.0	100	0.9382
2.0	200	0.9384
3.0	300	0.9384

Framework Versions

Python: 3.13.5
Sentence Transformers: 5.3.0
Transformers: 5.5.0
PyTorch: 2.11.0+cu130
Accelerate: 1.13.0
Datasets: 4.8.4
Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

bibtex

@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",
}

	sentence_0	sentence_1	label
type	string	string	float
details	min: 20 characters mean: 32.95 characters max: 61 characters	min: 48 characters mean: 110.73 characters max: 158 characters	min: 0.0 mean: 0.56 max: 1.0

zkarbie/context-lattice-semantic-reranker

CrossEncoder based on cross-encoder/ms-marco-MiniLM-L6-v2