This is a Cross Encoder model finetuned from microsoft/MiniLM-L12-H384-uncased 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: microsoft/MiniLM-L12-H384-uncased
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

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("zhensuuu/reranker-MiniLM-L12-H384-uncased-intent")
# Get scores for pairs of texts
pairs = [
    ['Add edge representing resource request', ' Model process-resource dependency relationship'],
    ['Split text into words list', ' Filter words matching given keyword.'],
    ['Calculate approximate cube root value', ' Find cube root using exponentiation'],
    ['Reverse sublist within linked list', ' Move nodes to new positions'],
    ['Defines neighbors for node A', ' Specifies direct connections from A'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Add edge representing resource request',
    [
        ' Model process-resource dependency relationship',
        ' Filter words matching given keyword.',
        ' Find cube root using exponentiation',
        ' Move nodes to new positions',
        ' Specifies direct connections from A',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

Datasets: NanoMSMARCO_R100, NanoNFCorpus_R100 and NanoNQ_R100
Evaluated with CrossEncoderRerankingEvaluator with these parameters:
json
1{ 2 "at_k": 10, 3 "always_rerank_positives": true 4}

Metric	NanoMSMARCO_R100	NanoNFCorpus_R100	NanoNQ_R100
map	0.0735 (-0.4161)	0.3017 (+0.0407)	0.0837 (-0.3359)
mrr@10	0.0476 (-0.4299)	0.4457 (-0.0541)	0.0661 (-0.3606)
ndcg@10	0.0687 (-0.4718)	0.2916 (-0.0335)	0.0748 (-0.4258)

Cross Encoder Nano BEIR

Dataset: NanoBEIR_R100_mean
Evaluated with CrossEncoderNanoBEIREvaluator with these parameters:
json
1{ 2 "dataset_names": [ 3 "msmarco", 4 "nfcorpus", 5 "nq" 6 ], 7 "rerank_k": 100, 8 "at_k": 10, 9 "always_rerank_positives": true 10}

Metric	Value
map	0.1529 (-0.2371)
mrr@10	0.1864 (-0.2816)
ndcg@10	0.1450 (-0.3104)

Training Details

Training Dataset

Unnamed Dataset

Size: 85,938 training samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 18 characters
mean: 33.49 characters
max: 49 characters
min: 18 characters
mean: 35.88 characters
max: 52 characters

Samples:

question	answer
`Check if configuration loaded successfully`	`prevent further actions if configuration absent`
`Add new user to list`	`Store received user in memory`
`Selects profitable jobs and schedules`	`Displays scheduled jobs and profit`

Loss: CachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 10.0, 3 "num_negatives": 5, 4 "activation_fn": "torch.nn.modules.activation.Sigmoid", 5 "mini_batch_size": 16 6}

Evaluation Dataset

Unnamed Dataset

Size: 1,000 evaluation samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 20 characters
mean: 33.63 characters
max: 54 characters
min: 18 characters
mean: 35.86 characters
max: 55 characters

Samples:

question	answer
`Add edge representing resource request`	`Model process-resource dependency relationship`
`Split text into words list`	`Filter words matching given keyword.`
`Calculate approximate cube root value`	`Find cube root using exponentiation`

Loss: CachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 10.0, 3 "num_negatives": 5, 4 "activation_fn": "torch.nn.modules.activation.Sigmoid", 5 "mini_batch_size": 16 6}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 2e-05
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: 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: 2e-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: 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
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	NanoMSMARCO_R100_ndcg@10	NanoNFCorpus_R100_ndcg@10	NanoNQ_R100_ndcg@10	NanoBEIR_R100_mean_ndcg@10
-1	-1	-	-	0.0146 (-0.5258)	0.2622 (-0.0628)	0.0058 (-0.4949)	0.0942 (-0.3612)
0.0030	1	1.7927	-	-	-	-	-
0.2976	100	1.2688	-	-	-	-	-
0.5952	200	0.8847	-	-	-	-	-
0.7440	250	-	0.8479	0.0586 (-0.4818)	0.2978 (-0.0272)	0.0717 (-0.4290)	0.1427 (-0.3127)
0.8929	300	0.8519	-	-	-	-	-
-1	-1	-	-	0.0687 (-0.4718)	0.2916 (-0.0335)	0.0748 (-0.4258)	0.1450 (-0.3104)

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.082 kWh
Carbon Emitted: 0.000 kg of CO2
Hours Used: 0.306 hours

Training Hardware

On Cloud: No
GPU Model: 4 x NVIDIA RTX 6000 Ada Generation
CPU Model: AMD EPYC 7763 64-Core Processor
RAM Size: 251.53 GB

Framework Versions

Python: 3.10.12
Sentence Transformers: 5.1.0
Transformers: 4.48.3
PyTorch: 2.6.0+cu124
Accelerate: 1.6.0
Datasets: 3.6.0
Tokenizers: 0.21.1

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",
}

	question	answer
type	string	string
details	min: 18 characters mean: 33.49 characters max: 49 characters	min: 18 characters mean: 35.88 characters max: 52 characters