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: 512 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 does the `DPMSolverMultistepInverse` scheduler relate to DDIM inversion and DPM-Solver's forward and reverse processes?", 'DPMSolverMultistepInverseis the inverted scheduler fromDPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 StepsandDPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Modelsby Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. The implementation is mostly based on the DDIM inversion definition ofNull-text Inversion for Editing Real Images using Guided Diffusion Modelsand notebook implementation of theDiffEditlatent inversion fromXiang-cd/DiffEdit-stable-diffusion.'],
    ['How can I optimize AudioLDM prompt engineering and inference for faster, higher-quality audio generation?', 'The model usually performs well without requiring any finetuning. The architecture follows a classic encoder-decoder architecture, which means that it relies on thegenerate()function for inference. One can useWhisperProcessorto prepare audio for the model, and decode the predicted ID’s back into text. To convert the model and the processor, we recommend using the following: The script will automatically determine all necessary parameters from the OpenAI checkpoint. Atiktokenlibrary needs to be installed to perform the conversion of the OpenAI tokenizer to thetokenizersversion.'],
    ['What AuraFlow-related attention processing tasks does the `AuraFlowAttnProcessor2_0` model excel at?', '() Attention processor used in Mochi.'],
    ['What are the key capabilities and applications of the CLIP (Contrastive Language-Image Pre-training) model?', 'The code snippet below shows how to compute image & text features and similarities: Currently, following scales of pretrained Chinese-CLIP models are available on 🤗 Hub:'],
    ['How effectively does CogVideoX translate text prompts into 720x480 videos?', 'To generate a video from prompt, run the following Python code: You can change these parameters in the pipeline call: We can also generate longer videos by doing the processing in a chunk-by-chunk manner:'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    "How does the `DPMSolverMultistepInverse` scheduler relate to DDIM inversion and DPM-Solver's forward and reverse processes?",
    [
        'DPMSolverMultistepInverseis the inverted scheduler fromDPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 StepsandDPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Modelsby Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. The implementation is mostly based on the DDIM inversion definition ofNull-text Inversion for Editing Real Images using Guided Diffusion Modelsand notebook implementation of theDiffEditlatent inversion fromXiang-cd/DiffEdit-stable-diffusion.',
        'The model usually performs well without requiring any finetuning. The architecture follows a classic encoder-decoder architecture, which means that it relies on thegenerate()function for inference. One can useWhisperProcessorto prepare audio for the model, and decode the predicted ID’s back into text. To convert the model and the processor, we recommend using the following: The script will automatically determine all necessary parameters from the OpenAI checkpoint. Atiktokenlibrary needs to be installed to perform the conversion of the OpenAI tokenizer to thetokenizersversion.',
        '() Attention processor used in Mochi.',
        'The code snippet below shows how to compute image & text features and similarities: Currently, following scales of pretrained Chinese-CLIP models are available on 🤗 Hub:',
        'To generate a video from prompt, run the following Python code: You can change these parameters in the pipeline call: We can also generate longer videos by doing the processing in a chunk-by-chunk manner:',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Binary Classification

Dataset: Quora-dev
Evaluated with CEBinaryClassificationEvaluator

Metric	Value
accuracy	0.9118
accuracy_threshold	-0.3893
f1	0.8254
f1_threshold	-1.1288
precision	0.7898
recall	0.8643
average_precision	0.8781

Training Details

Training Dataset

Unnamed Dataset

Size: 25,200 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 int
details
min: 19 characters
mean: 114.7 characters
max: 785 characters
min: 9 characters
mean: 700.88 characters
max: 11836 characters
0: ~76.50%
1: ~23.50%

Samples:

sentence_0	sentence_1	label
`How does the [object Object] scheduler relate to DDIM inversion and DPM-Solver's forward and reverse processes?`	DPMSolverMultistepInverseis the inverted scheduler fromDPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 StepsandDPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Modelsby Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. The implementation is mostly based on the DDIM inversion definition ofNull-text Inversion for Editing Real Images using Guided Diffusion Modelsand notebook implementation of theDiffEditlatent inversion fromXiang-cd/DiffEdit-stable-diffusion.	`1`
`How can I optimize AudioLDM prompt engineering and inference for faster, higher-quality audio generation?`	The model usually performs well without requiring any finetuning. The architecture follows a classic encoder-decoder architecture, which means that it relies on thegenerate()function for inference. One can useWhisperProcessorto prepare audio for the model, and decode the predicted ID’s back into text. To convert the model and the processor, we recommend using the following: The script will automatically determine all necessary parameters from the OpenAI checkpoint. Atiktokenlibrary needs to be installed to perform the conversion of the OpenAI tokenizer to thetokenizersversion.	`0`
`What AuraFlow-related attention processing tasks does the [object Object] model excel at?`	`() Attention processor used in Mochi.`	`0`

Loss: FitMixinLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 2

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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
num_train_epochs: 2
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: False
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}
tp_size: 0
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	Quora-dev_average_precision
0.1269	100	-	0.7617
0.2538	200	-	0.7991
0.3807	300	-	0.8186
0.5076	400	-	0.8476
0.6345	500	0.327	0.8500
0.7614	600	-	0.8518
0.8883	700	-	0.8616
1.0	788	-	0.8599
1.0152	800	-	0.8537
1.1421	900	-	0.8542
1.2690	1000	0.267	0.8663
1.3959	1100	-	0.8662
1.5228	1200	-	0.8781

Framework Versions

Python: 3.11.10
Sentence Transformers: 4.0.1
Transformers: 4.50.3
PyTorch: 2.5.1+cu124
Accelerate: 1.6.0
Datasets: 3.5.0
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",
}

	sentence_0	sentence_1	label
type	string	string	int
details	min: 19 characters mean: 114.7 characters max: 785 characters	min: 9 characters mean: 700.88 characters max: 11836 characters	0: ~76.50% 1: ~23.50%

truong1301/reranker

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