This is a Cross Encoder model finetuned from BAAI/bge-reranker-large 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: BAAI/bge-reranker-large
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 = [
    ['Hi, is there a way to show an image once the personalization toggle is on?', "Hi there, This is Hazel from Tepo Support team. I hope your day is going great! Yes, you can ultilize our conditional settings to show an image based on the toggle's selection, as demonstrated on our demo product [here](https://tepo.app/products/conditional-logic) <3 For step-by-step instructions on how to set up conditional settings on your end, please check out our [guide](https://support.tepo.app/en/article/set-up-conditional-logic-1sp9tac/). If you prefer watching than reading, feel free to take a look at our tutorial video [here](https://www.youtube.com/watch?v=7voAaJ2lzkc)"],
    ['Hi, is there a way to show an image once the personalization toggle is on?', "Hello there, I apologize for getting back to you a bit later, as we've been away during non-working hours. I hope your weekend went great! For your information, we have a feature to change the product image based on the option's selection. I have enabled this feature for your store, so feel free to follow our step-by-step instructions [here](https://support.tepo.app/en/article/change-product-images-based-on-selected-options-1831rew/) to set up this feature for your store. Please let me know if you encounter any difficulties. I am more than happy to help!"],
    ['Hi, is there a way to show an image once the personalization toggle is on?', "Hello, Thank you for contacting Tepo Support team! I hope you're having a wonderful day! To enlarge the size of the image swatches, you can refer to our guide [here](https://support.tepo.app/en/article/customize-style-for-app-options-using-css-26kmin/) for step-by-step instructions. This will allow you to make the necessary adjustments on your end. Feel free to give it a try, and if you encounter any difficulties or if coding isn't your forte, please don't hesitate to share the product URL with me. I'll be more than happy to assist further!"],
    ['Hi, is there a way to show an image once the personalization toggle is on?', 'Good day~ This is Robin from Tepo Support team! I hope your day has been amazing so far! To make sure I’m on the same page, when you say getting the older version of the option set, are you referring to undoing some changes after you’ve already hit save? If I’ve misunderstood anything, please don’t hesitate to correct me. I want to make sure I assist you as best as I can!'],
    ['Hi, is there a way to show an image once the personalization toggle is on?', "> Hello there, > This is Hazel from Tepo Support team. I hope your day is going great! > Thanks for sharing your concern with us. Regarding the packing slip, we do have a step-by-step instruction [here](https://support.tepo.app/en/article/how-to-show-options-in-shopify-packing-slip-1dyay7y/) that you can follow more easily on your end. Feel free to give it a try on your end to show Tepo options in your packing slip In Shopify, a courier slip is also known as a packing slip. I've seen that Hazel has sent you the instruction to add the code into the packing slip template to show the TEPO options already. May I ask if you give it a try on your end yet?"],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Hi, is there a way to show an image once the personalization toggle is on?',
    [
        "Hi there, This is Hazel from Tepo Support team. I hope your day is going great! Yes, you can ultilize our conditional settings to show an image based on the toggle's selection, as demonstrated on our demo product [here](https://tepo.app/products/conditional-logic) <3 For step-by-step instructions on how to set up conditional settings on your end, please check out our [guide](https://support.tepo.app/en/article/set-up-conditional-logic-1sp9tac/). If you prefer watching than reading, feel free to take a look at our tutorial video [here](https://www.youtube.com/watch?v=7voAaJ2lzkc)",
        "Hello there, I apologize for getting back to you a bit later, as we've been away during non-working hours. I hope your weekend went great! For your information, we have a feature to change the product image based on the option's selection. I have enabled this feature for your store, so feel free to follow our step-by-step instructions [here](https://support.tepo.app/en/article/change-product-images-based-on-selected-options-1831rew/) to set up this feature for your store. Please let me know if you encounter any difficulties. I am more than happy to help!",
        "Hello, Thank you for contacting Tepo Support team! I hope you're having a wonderful day! To enlarge the size of the image swatches, you can refer to our guide [here](https://support.tepo.app/en/article/customize-style-for-app-options-using-css-26kmin/) for step-by-step instructions. This will allow you to make the necessary adjustments on your end. Feel free to give it a try, and if you encounter any difficulties or if coding isn't your forte, please don't hesitate to share the product URL with me. I'll be more than happy to assist further!",
        'Good day~ This is Robin from Tepo Support team! I hope your day has been amazing so far! To make sure I’m on the same page, when you say getting the older version of the option set, are you referring to undoing some changes after you’ve already hit save? If I’ve misunderstood anything, please don’t hesitate to correct me. I want to make sure I assist you as best as I can!',
        "> Hello there, > This is Hazel from Tepo Support team. I hope your day is going great! > Thanks for sharing your concern with us. Regarding the packing slip, we do have a step-by-step instruction [here](https://support.tepo.app/en/article/how-to-show-options-in-shopify-packing-slip-1dyay7y/) that you can follow more easily on your end. Feel free to give it a try on your end to show Tepo options in your packing slip In Shopify, a courier slip is also known as a packing slip. I've seen that Hazel has sent you the instruction to add the code into the packing slip template to show the TEPO options already. May I ask if you give it a try on your end yet?",
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

Dataset: support-rerank-eval
Evaluated with CrossEncoderRerankingEvaluator with these parameters:
json
1{ 2 "at_k": 10, 3 "always_rerank_positives": false 4}

Metric	Value
map	0.9087 (-0.0913)
mrr@10	0.9087 (-0.0913)
ndcg@10	0.9315 (-0.0685)

Training Details

Training Dataset

Unnamed Dataset

Size: 5,400 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 28 characters
mean: 218.29 characters
max: 1961 characters
min: 141 characters
mean: 643.39 characters
max: 3722 characters
min: 0.0
mean: 0.17
max: 1.0

Samples:

sentence1	sentence2	label
`Hi, is there a way to show an image once the personalization toggle is on?`	`Hi there, This is Hazel from Tepo Support team. I hope your day is going great! Yes, you can ultilize our conditional settings to show an image based on the toggle's selection, as demonstrated on our demo product [object Object] <3 For step-by-step instructions on how to set up conditional settings on your end, please check out our [object Object]. If you prefer watching than reading, feel free to take a look at our tutorial video [object Object]`	`1.0`
`Hi, is there a way to show an image once the personalization toggle is on?`	`Hello there, I apologize for getting back to you a bit later, as we've been away during non-working hours. I hope your weekend went great! For your information, we have a feature to change the product image based on the option's selection. I have enabled this feature for your store, so feel free to follow our step-by-step instructions [object Object] to set up this feature for your store. Please let me know if you encounter any difficulties. I am more than happy to help!`	`0.0`
`Hi, is there a way to show an image once the personalization toggle is on?`	`Hello, Thank you for contacting Tepo Support team! I hope you're having a wonderful day! To enlarge the size of the image swatches, you can refer to our guide [object Object] for step-by-step instructions. This will allow you to make the necessary adjustments on your end. Feel free to give it a try, and if you encounter any difficulties or if coding isn't your forte, please don't hesitate to share the product URL with me. I'll be more than happy to assist further!`	`0.0`

Loss: BinaryCrossEntropyLoss with these parameters:
json
1{ 2 "activation_fn": "torch.nn.modules.linear.Identity", 3 "pos_weight": 5 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 1
gradient_accumulation_steps: 32
learning_rate: 1e-05
num_train_epochs: 5
warmup_ratio: 0.2
warmup_steps: 0.2
seed: 67
fp16: True
load_best_model_at_end: True

All Hyperparameters

Click to expand

do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 1
per_device_eval_batch_size: 8
gradient_accumulation_steps: 32
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 1e-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: 5
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_ratio: 0.2
warmup_steps: 0.2
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
enable_jit_checkpoint: False
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
use_cpu: False
seed: 67
data_seed: None
bf16: False
fp16: True
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: -1
ddp_backend: None
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
disable_tqdm: True
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
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
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_for_metrics: []
eval_do_concat_batches: True
auto_find_batch_size: False
full_determinism: False
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
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
use_cache: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Image

Epoch	Step	Training Loss	support-rerank-eval_ndcg@10
-1	-1	-	0.8413 (-0.1587)
0.0119	1	1.3864	-
0.0593	5	2.2658	-
0.1185	10	1.5262	-
0.1778	15	1.5737	-
0.2370	20	1.3171	-
0.2963	25	1.0806	-
0.3556	30	1.0587	-
0.4148	35	0.8149	-
0.4741	40	0.8706	-
0.5333	45	1.1330	-
0.5926	50	1.1210	-
0.6519	55	0.7105	-
0.7111	60	0.8596	-
0.7704	65	0.8416	-
0.8296	70	0.6543	-
0.8889	75	0.7784	-
0.9481	80	0.5754	-
1.0	85	0.7647	-
1.0593	90	0.5856	-
1.1185	95	0.5812	-
1.1778	100	0.5459	0.8906 (-0.1094)
1.2370	105	0.5259	-
1.2963	110	0.6609	-
1.3556	115	0.5932	-
1.4148	120	0.5482	-
1.4741	125	0.7539	-
1.5333	130	0.6486	-
1.5926	135	0.6475	-
1.6519	140	0.5627	-
1.7111	145	0.6848	-
1.7704	150	0.6890	-
1.8296	155	0.5712	-
1.8889	160	0.5949	-
1.9481	165	0.7066	-
2.0	170	0.7013	-
2.0593	175	0.3729	-
2.1185	180	0.4327	-
2.1778	185	0.3703	-
2.2370	190	0.4843	-
2.2963	195	0.3460	-
2.3556	200	0.3679	0.9241 (-0.0759)
2.4148	205	0.4412	-
2.4741	210	0.5300	-
2.5333	215	0.4922	-
2.5926	220	0.4407	-
2.6519	225	0.3835	-
2.7111	230	0.4581	-
2.7704	235	0.4687	-
2.8296	240	0.3968	-
2.8889	245	0.3373	-
2.9481	250	0.3882	-
3.0	255	0.5394	-
3.0593	260	0.3229	-
3.1185	265	0.2746	-
3.1778	270	0.4415	-
3.2370	275	0.2434	-
3.2963	280	0.3033	-
3.3556	285	0.3377	-
3.4148	290	0.3316	-
3.4741	295	0.2425	-
3.5333	300	0.3449	0.9141 (-0.0859)
3.5926	305	0.2569	-
3.6519	310	0.3693	-
3.7111	315	0.2660	-
3.7704	320	0.3553	-
3.8296	325	0.3099	-
3.8889	330	0.2923	-
3.9481	335	0.3726	-
4.0	340	0.5681	-
4.0593	345	0.1964	-
4.1185	350	0.2471	-
4.1778	355	0.3227	-
4.2370	360	0.2911	-
4.2963	365	0.2578	-
4.3556	370	0.2402	-
4.4148	375	0.2367	-
4.4741	380	0.1998	-
4.5333	385	0.2359	-
4.5926	390	0.2177	-
4.6519	395	0.1999	-
4.7111	400	0.2293	0.9315 (-0.0685)
4.7704	405	0.2748	-
4.8296	410	0.2312	-
4.8889	415	0.3013	-
4.9481	420	0.1602	-
5.0	425	0.3283	-

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.3
Transformers: 5.0.0
PyTorch: 2.10.0+cu128
Accelerate: 1.12.0
Datasets: 4.8.3
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",
}

	sentence1	sentence2	label
type	string	string	float
details	min: 28 characters mean: 218.29 characters max: 1961 characters	min: 141 characters mean: 643.39 characters max: 3722 characters	min: 0.0 mean: 0.17 max: 1.0

CuongCao/oe-bge-reranker-large-ft-v2

CrossEncoder based on BAAI/bge-reranker-large