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metadata

tags:
  - sentence-transformers
  - cross-encoder
  - reranker
  - generated_from_trainer
  - dataset_size:1485
  - loss:BinaryCrossEntropyLoss
base_model: cross-encoder/ms-marco-MiniLM-L12-v2
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
  - accuracy
  - accuracy_threshold
  - f1
  - f1_threshold
  - precision
  - recall
  - average_precision
model-index:
  - name: CrossEncoder based on cross-encoder/ms-marco-MiniLM-L12-v2
    results:
      - task:
          type: cross-encoder-classification
          name: Cross Encoder Classification
        dataset:
          name: compliance eval
          type: compliance-eval
        metrics:
          - type: accuracy
            value: 0.9636363636363636
            name: Accuracy
          - type: accuracy_threshold
            value: -1.7519245147705078
            name: Accuracy Threshold
          - type: f1
            value: 0.9662921348314608
            name: F1
          - type: f1_threshold
            value: -2.8691844940185547
            name: F1 Threshold
          - type: precision
            value: 0.9555555555555556
            name: Precision
          - type: recall
            value: 0.9772727272727273
            name: Recall
          - type: average_precision
            value: 0.9939968601076801
            name: Average Precision

CrossEncoder based on cross-encoder/ms-marco-MiniLM-L12-v2

This is a Cross Encoder model finetuned from cross-encoder/ms-marco-MiniLM-L12-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-L12-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.

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("cross_encoder_model_id")
# Get scores for pairs of texts
pairs = [
    ['the system must identify any risk profile that has expired and is currently marked as overdue to ensure ongoing suitability compliance.', "so, like, your portfolio risk profile is out of date, and i've got a flag here saying it needs renewal before we can do any new trades."],
    ['to identify risk misalignment trades, the system must flag a risk mismatch whenever the product risk rating exceeds the client risk profile.', "so, it's a solid choice, but i gotta mention, there's a bit of a risk mismatch between the fund's rating and your own suitability score, so it's a bit of a hurdle."],
    ['the system identifies an execution only wrapper when the order initiation confirms that this trade is performed on an execution only basis with no advice given.', "so... uh... let's just do it, but it's execution only, you know? no advice was provided, so you're on your own with the strategy on this one, i'm so rushed."],
    ['the system must identify any risk profile that has expired and is currently marked as overdue to ensure ongoing suitability compliance.', "hey, um, checking the dashboard here and it says your prp is overdue, you know, we haven't updated it in a bit and it's flagged."],
    ['to identify risk misalignment trades, the system must flag a risk mismatch whenever the product risk rating exceeds the client risk profile.', "don't worry about the specifics right now the main thing is getting the allocation because it's oversubscribed so can i confirm the trade"],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'the system must identify any risk profile that has expired and is currently marked as overdue to ensure ongoing suitability compliance.',
    [
        "so, like, your portfolio risk profile is out of date, and i've got a flag here saying it needs renewal before we can do any new trades.",
        "so, it's a solid choice, but i gotta mention, there's a bit of a risk mismatch between the fund's rating and your own suitability score, so it's a bit of a hurdle.",
        "so... uh... let's just do it, but it's execution only, you know? no advice was provided, so you're on your own with the strategy on this one, i'm so rushed.",
        "hey, um, checking the dashboard here and it says your prp is overdue, you know, we haven't updated it in a bit and it's flagged.",
        "don't worry about the specifics right now the main thing is getting the allocation because it's oversubscribed so can i confirm the trade",
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Classification

Dataset: compliance-eval
Evaluated with CrossEncoderClassificationEvaluator

Metric	Value
accuracy	0.9636
accuracy_threshold	-1.7519
f1	0.9663
f1_threshold	-2.8692
precision	0.9556
recall	0.9773
average_precision	0.994

Training Details

Training Dataset

Unnamed Dataset

Size: 1,485 training samples
Columns: sentence1, sentence2, and label

Approximate statistics based on the first 1000 samples:

	sentence1	sentence2	label
type	string	string	float
details	min: 135 characters mean: 302.95 characters max: 725 characters	min: 97 characters mean: 179.3 characters max: 463 characters	min: 0.0 mean: 0.49 max: 1.0

Samples:

sentence1	sentence2	label
`the rm must use the instrument_code to identify the soft lock disclosure and inform the client that 'this fund has a soft lock-up duration of xx months. you will be subjected to an early redemption charge of x% by the fund house if you were to redeem the fund within the soft lock-up period.' and, if applicable, that 'the fund is currently still within the soft lock-up period. should you wish to proceed with the redemption, you will incur an early redemption charge of x% by the fund house.'`	`there's a bit of a soft lock on this one, you know, if you take the money out too soon there's a small charge, but it's no big deal.`	`0.0`
`the system identifies an execution only wrapper when the order initiation confirms that this trade is performed on an execution only basis with no advice given.`	`i can't believe how expensive flights have become lately, it's just ridiculous. let's just go ahead with that stock buy, i'll put it through as we discussed earlier, it’s a simple execution for us.`	`0.0`
`for a client initiated (ci) wrapper where the order initiation is 'client initiated', the bank must confirm that 'this trade is based on your initiated interest in underlying and product type' or 'this trade is based on your initiated interest in underlying or product type'.`	`exactly, i-i see what you mean, and since you're the one who initiated this conversation about the emerging markets fund, i'll just log that as your interest. did you ever get that classic car fixed up?`	`1.0`

Loss: BinaryCrossEntropyLoss with these parameters:

{
    "activation_fn": "torch.nn.modules.linear.Identity",
    "pos_weight": null
}

Evaluation Dataset

Unnamed Dataset

Size: 165 evaluation samples
Columns: sentence1, sentence2, and label

Approximate statistics based on the first 165 samples:

	sentence1	sentence2	label
type	string	string	float
details	min: 135 characters mean: 302.44 characters max: 725 characters	min: 97 characters mean: 178.02 characters max: 631 characters	min: 0.0 mean: 0.53 max: 1.0

Samples:

sentence1	sentence2	label
`the system must identify any risk profile that has expired and is currently marked as overdue to ensure ongoing suitability compliance.`	`so, like, your portfolio risk profile is out of date, and i've got a flag here saying it needs renewal before we can do any new trades.`	`1.0`
`to identify risk misalignment trades, the system must flag a risk mismatch whenever the product risk rating exceeds the client risk profile.`	`so, it's a solid choice, but i gotta mention, there's a bit of a risk mismatch between the fund's rating and your own suitability score, so it's a bit of a hurdle.`	`1.0`
`the system identifies an execution only wrapper when the order initiation confirms that this trade is performed on an execution only basis with no advice given.`	`so... uh... let's just do it, but it's execution only, you know? no advice was provided, so you're on your own with the strategy on this one, i'm so rushed.`	`1.0`

Loss: BinaryCrossEntropyLoss with these parameters:

{
    "activation_fn": "torch.nn.modules.linear.Identity",
    "pos_weight": null
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
warmup_ratio: 0.1
load_best_model_at_end: 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: 16
per_device_eval_batch_size: 16
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: 3
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
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: True
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
hub_revision: None
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
include_tokens_per_second: False
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	compliance-eval_average_precision
0.1075	10	1.9119	1.1985	0.6783
0.2151	20	0.9675	1.0970	0.6914
0.3226	30	0.7458	0.4725	0.8480
0.4301	40	0.5308	0.4431	0.8849
0.5376	50	0.3888	0.4183	0.9097
0.6452	60	0.3477	0.3472	0.9325
0.7527	70	0.3082	0.3005	0.9524
0.8602	80	0.3364	0.2682	0.9647
0.9677	90	0.3069	0.2345	0.9804
1.0753	100	0.2636	0.1847	0.9886
1.1828	110	0.2577	0.1793	0.9847
1.2903	120	0.1793	0.1940	0.9826
1.3978	130	0.19	0.2333	0.9794
1.5054	140	0.1788	0.1615	0.9858
1.6129	150	0.1277	0.1576	0.9862
1.7204	160	0.1851	0.1399	0.9903
1.8280	170	0.1652	0.1056	0.9947
1.9355	180	0.085	0.1077	0.9949
2.043	190	0.1111	0.0943	0.9955
2.1505	200	0.09	0.1137	0.9955
2.2581	210	0.1136	0.1222	0.9934
2.3656	220	0.0703	0.1155	0.9937
2.4731	230	0.0866	0.1147	0.9935
2.5806	240	0.1104	0.1089	0.9943
2.6882	250	0.1523	0.1141	0.9940
2.7957	260	0.1189	0.1297	0.9943
2.9032	270	0.0479	0.1365	0.9940

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.0
Transformers: 4.57.3
PyTorch: 2.9.0+cu126
Accelerate: 1.12.0
Datasets: 4.0.0
Tokenizers: 0.22.2

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",
}