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
Supported Modality: Text

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Cross Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Cross Encoders on Hugging Face

Full Model Architecture

CrossEncoder(
  (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'BertForSequenceClassification'})
)

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 inputs
pairs = [
    ['skill: manage health care staff', 'skill: manage healthcare staff'],
    ['skill: manage health care staff', 'skill: manage staff'],
    ['skill: manage health care staff', 'skill: manage physiotherapy staff'],
    ['skill: manage health care staff', 'skill: work with nursing staff'],
    ['skill: manage health care staff', 'skill: manage chiropractic staff'],
]
scores = model.predict(pairs)
print(scores)
# [ 8.621   5.0407  1.0124 -1.7446  0.0759]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'skill: manage health care staff',
    [
        'skill: manage healthcare staff',
        'skill: manage staff',
        'skill: manage physiotherapy staff',
        'skill: work with nursing staff',
        'skill: manage chiropractic staff',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Training Details

Training Dataset

Unnamed Dataset

Size: 10,246 training samples
Columns: sentence_A, sentence_B, and label
Approximate statistics based on the first 100 samples:
sentence_A sentence_B label
type string string float
modality text text
details
min: 7 tokens
mean: 8.14 tokens
max: 11 tokens
min: 6 tokens
mean: 7.94 tokens
max: 15 tokens
min: 0.0
mean: 0.22
max: 1.0

Samples:

sentence_A	sentence_B	label
`skill: manage security systems`	`skill: manage technical security systems`	`1.0`
`skill: manage security systems`	`skill: Physical access management software`	`0.0`
`skill: manage security systems`	`skill: manage theft prevention`	`0.0`

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

Evaluation Dataset

Unnamed Dataset

Size: 1,416 evaluation samples
Columns: sentence_A, sentence_B, and label
Approximate statistics based on the first 100 samples:
sentence_A sentence_B label
type string string float
modality text text
details
min: 6 tokens
mean: 9.23 tokens
max: 14 tokens
min: 6 tokens
mean: 8.06 tokens
max: 16 tokens
min: 0.0
mean: 0.21
max: 1.0

Samples:

sentence_A	sentence_B	label
`skill: manage health care staff`	`skill: manage healthcare staff`	`1.0`
`skill: manage health care staff`	`skill: manage staff`	`0.0`
`skill: manage health care staff`	`skill: manage physiotherapy staff`	`0.0`

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: 32
num_train_epochs: 2
learning_rate: 2e-05
warmup_steps: 0.1
per_device_eval_batch_size: 64
load_best_model_at_end: True

All Hyperparameters

Click to expand

per_device_train_batch_size: 32
num_train_epochs: 2
max_steps: -1
learning_rate: 2e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0.1
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: 1.0
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: None
trackio_bucket_id: None
trackio_static_space_id: None
per_device_eval_batch_size: 64
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: True
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_static_graph: None
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	Training Loss	Validation Loss
0.0623	20	0.2485	-
0.1246	40	0.1945	-
0.1869	60	0.1679	-
0.2492	80	0.1314	-
0.3115	100	0.1673	-
0.3738	120	0.1750	-
0.4361	140	0.1375	-
0.4984	160	0.1779	-
0.5607	180	0.1119	-
0.6231	200	0.1516	-
0.6854	220	0.1284	-
0.7477	240	0.1068	-
0.8100	260	0.1367	-
0.8723	280	0.0968	-
0.9346	300	0.0934	-
0.9969	320	0.1276	-
1.0	321	-	0.1635
1.0592	340	0.1069	-
1.1215	360	0.0679	-
1.1838	380	0.0987	-
1.2461	400	0.0829	-
1.3084	420	0.0646	-
1.3707	440	0.1205	-
1.4330	460	0.0682	-
1.4953	480	0.1087	-
1.5576	500	0.0874	-
1.6199	520	0.0880	-
1.6822	540	0.0678	-
1.7445	560	0.0743	-
1.8069	580	0.1250	-
1.8692	600	0.1010	-
1.9315	620	0.0914	-
1.9938	640	0.0813	-
2.0	642	-	0.1889

The bold row denotes the saved checkpoint.

Training Time

Training: 48.1 seconds
Evaluation: 1.0 seconds
Total: 49.1 seconds

Framework Versions

Python: 3.13.13
Sentence Transformers: 5.5.0
Transformers: 5.8.1
PyTorch: 2.12.0
Accelerate: 1.14.0
Datasets: 5.0.0
Tokenizers: 0.22.2

Additional Resources

Training and Finetuning Reranker Models with Sentence Transformers: the end-to-end guide for training or finetuning Cross Encoder (reranker) models.
Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video reranker models through the same API.
Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: training multimodal Cross Encoders.

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_A	sentence_B	label
type	string	string	float
modality	text	text
details	min: 7 tokens mean: 8.14 tokens max: 11 tokens	min: 6 tokens mean: 7.94 tokens max: 15 tokens	min: 0.0 mean: 0.22 max: 1.0

rohit-vt/tink-reranker-phase1_minilm

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

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Training Time

Framework Versions

Additional Resources

Citation

BibTeX

Sentence Transformers

rohit-vt/
tink-reranker-phase1_minilm