CrossEncoder based on answerdotai/ModernBERT-base
This is a Cross Encoder model finetuned from answerdotai/ModernBERT-base using the sentence-transformers library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Model Details
Model Description
- Model Type: Cross Encoder
- Base model: answerdotai/ModernBERT-base
- Maximum Sequence Length: 8192 tokens
- Number of Output Labels: 1 label
Model Sources
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
model = CrossEncoder("tomaarsen/reranker-modernbert-base-msmarco-bce")
pairs = [
['what gb sp model is bright', 'From the list above it is easy to understand that Gegabyte is bigger than Megabyte. Or GB is bigger between MB and GB. Thanks.'],
['does immunotherapy work', 'The US and Drug Administration (FDA) this week convened a panel of outside experts to weigh in on the readiness of a first-of-its-kind cancer therapy. The treatment, which works by tweaking a patientâ\x80\x99s own cells, is a type of immunotherapy called CAR T-cell therapy and has been in clinical trials for several years. One drug maker is now seeking FDA approval to use the treatment in pediatric and young adult patients ages 3 to 25 with B-cell acute lymphoblastic leukemia (ALL) that has not responded to standard care.'],
['how long to wear oasis contacts', 'There is something wrong with my Xperia Z last week, and I did a resetting to make the phone become original, but I forgot backing up some important contacts, so I have to find the way for Sony Xperia Z contacts recovery, finanlly, I got this Android Data Recovery software to recover lost contacts from my phone.here is something wrong with my Xperia Z last week, and I did a resetting to make the phone become original, but I forgot backing up some important contacts, so I have to find the way for Sony Xperia Z contacts recovery, finanlly, I got this Android Data Recovery software to recover lost contacts from my phone.'],
['water baby definition', "Someone very comfortable in the water, Good swimmers, and never scared while in bodies of water. There's Jadine, back in the lake. She's such a water baby. #water #aqua #babies #water babies #water kids."],
['youngest suicide case', 'Samantha Kuberskki was found hanging by a belt at her home in Oregon after being sent to her room for arguing with her mother. A six-year-old girl who was sent to her room for punishment is feared to be one of the youngest people to have ever committed suicide in the U.S. Samantha Kuberskki was found hanging by a belt at her home in Oregon after being sent to her room for arguing with her mother. Her death was ruled as suicide by the coroner - sparking a bitter row with police who investigated her death and insist it was an accident.'],
]
scores = model.predict(pairs)
print(scores.shape)
ranks = model.rank(
'what gb sp model is bright',
[
'From the list above it is easy to understand that Gegabyte is bigger than Megabyte. Or GB is bigger between MB and GB. Thanks.',
'The US and Drug Administration (FDA) this week convened a panel of outside experts to weigh in on the readiness of a first-of-its-kind cancer therapy. The treatment, which works by tweaking a patientâ\x80\x99s own cells, is a type of immunotherapy called CAR T-cell therapy and has been in clinical trials for several years. One drug maker is now seeking FDA approval to use the treatment in pediatric and young adult patients ages 3 to 25 with B-cell acute lymphoblastic leukemia (ALL) that has not responded to standard care.',
'There is something wrong with my Xperia Z last week, and I did a resetting to make the phone become original, but I forgot backing up some important contacts, so I have to find the way for Sony Xperia Z contacts recovery, finanlly, I got this Android Data Recovery software to recover lost contacts from my phone.here is something wrong with my Xperia Z last week, and I did a resetting to make the phone become original, but I forgot backing up some important contacts, so I have to find the way for Sony Xperia Z contacts recovery, finanlly, I got this Android Data Recovery software to recover lost contacts from my phone.',
"Someone very comfortable in the water, Good swimmers, and never scared while in bodies of water. There's Jadine, back in the lake. She's such a water baby. #water #aqua #babies #water babies #water kids.",
'Samantha Kuberskki was found hanging by a belt at her home in Oregon after being sent to her room for arguing with her mother. A six-year-old girl who was sent to her room for punishment is feared to be one of the youngest people to have ever committed suicide in the U.S. Samantha Kuberskki was found hanging by a belt at her home in Oregon after being sent to her room for arguing with her mother. Her death was ruled as suicide by the coroner - sparking a bitter row with police who investigated her death and insist it was an accident.',
]
)
Evaluation
Metrics
Cross Encoder Reranking
| Metric |
NanoMSMARCO |
NanoNFCorpus |
NanoNQ |
| map |
0.6519 (+0.1623) |
0.3432 (+0.0728) |
0.6951 (+0.2744) |
| mrr@10 |
0.6449 (+0.1674) |
0.5016 (+0.0017) |
0.7152 (+0.2885) |
| ndcg@10 |
0.7069 (+0.1665) |
0.3801 (+0.0550) |
0.7469 (+0.2462) |
Cross Encoder Nano BEIR
| Metric |
Value |
| map |
0.5634 (+0.1698) |
| mrr@10 |
0.6206 (+0.1525) |
| ndcg@10 |
0.6113 (+0.1559) |
Training Details
Training Dataset
Unnamed Dataset
Evaluation Dataset
Unnamed Dataset
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 8e-05
num_train_epochs: 1
warmup_ratio: 0.1
seed: 12
bf16: True
dataloader_num_workers: 4
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: 128
per_device_eval_batch_size: 128
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: 8e-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: 4
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}
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
Click to expand
| Epoch |
Step |
Training Loss |
Validation Loss |
NanoMSMARCO_ndcg@10 |
NanoNFCorpus_ndcg@10 |
NanoNQ_ndcg@10 |
NanoBEIR_mean_ndcg@10 |
| -1 |
-1 |
- |
- |
0.0245 (-0.5159) |
0.2709 (-0.0541) |
0.0858 (-0.4148) |
0.1271 (-0.3283) |
| 0.0000 |
1 |
1.1359 |
- |
- |
- |
- |
- |
| 0.0064 |
1000 |
0.9962 |
- |
- |
- |
- |
- |
| 0.0128 |
2000 |
0.3958 |
- |
- |
- |
- |
- |
| 0.0192 |
3000 |
0.3292 |
- |
- |
- |
- |
- |
| 0.0256 |
4000 |
0.3023 |
- |
- |
- |
- |
- |
| 0.0320 |
5000 |
0.2839 |
0.2495 |
0.6231 (+0.0827) |
0.3748 (+0.0498) |
0.7295 (+0.2288) |
0.5758 (+0.1204) |
| 0.0384 |
6000 |
0.2812 |
- |
- |
- |
- |
- |
| 0.0448 |
7000 |
0.2755 |
- |
- |
- |
- |
- |
| 0.0512 |
8000 |
0.2672 |
- |
- |
- |
- |
- |
| 0.0576 |
9000 |
0.2624 |
- |
- |
- |
- |
- |
| 0.0640 |
10000 |
0.2627 |
0.2368 |
0.6976 (+0.1572) |
0.4094 (+0.0844) |
0.7139 (+0.2133) |
0.6070 (+0.1516) |
| 0.0704 |
11000 |
0.2586 |
- |
- |
- |
- |
- |
| 0.0768 |
12000 |
0.253 |
- |
- |
- |
- |
- |
| 0.0832 |
13000 |
0.25 |
- |
- |
- |
- |
- |
| 0.0896 |
14000 |
0.2545 |
- |
- |
- |
- |
- |
| 0.0960 |
15000 |
0.2516 |
0.2297 |
0.6671 (+0.1267) |
0.3685 (+0.0434) |
0.7301 (+0.2295) |
0.5886 (+0.1332) |
| 0.1025 |
16000 |
0.241 |
- |
- |
- |
- |
- |
| 0.1089 |
17000 |
0.2459 |
- |
- |
- |
- |
- |
| 0.1153 |
18000 |
0.2371 |
- |
- |
- |
- |
- |
| 0.1217 |
19000 |
0.2374 |
- |
- |
- |
- |
- |
| 0.1281 |
20000 |
0.234 |
0.2226 |
0.6377 (+0.0973) |
0.3988 (+0.0737) |
0.7002 (+0.1996) |
0.5789 (+0.1235) |
| 0.1345 |
21000 |
0.2293 |
- |
- |
- |
- |
- |
| 0.1409 |
22000 |
0.2222 |
- |
- |
- |
- |
- |
| 0.1473 |
23000 |
0.2231 |
- |
- |
- |
- |
- |
| 0.1537 |
24000 |
0.2212 |
- |
- |
- |
- |
- |
| 0.1601 |
25000 |
0.2165 |
0.2266 |
0.7114 (+0.1710) |
0.3775 (+0.0524) |
0.7314 (+0.2308) |
0.6068 (+0.1514) |
| 0.1665 |
26000 |
0.2119 |
- |
- |
- |
- |
- |
| 0.1729 |
27000 |
0.2086 |
- |
- |
- |
- |
- |
| 0.1793 |
28000 |
0.204 |
- |
- |
- |
- |
- |
| 0.1857 |
29000 |
0.204 |
- |
- |
- |
- |
- |
| 0.1921 |
30000 |
0.1959 |
0.1913 |
0.6630 (+0.1225) |
0.3962 (+0.0712) |
0.7027 (+0.2020) |
0.5873 (+0.1319) |
| 0.1985 |
31000 |
0.195 |
- |
- |
- |
- |
- |
| 0.2049 |
32000 |
0.1899 |
- |
- |
- |
- |
- |
| 0.2113 |
33000 |
0.1887 |
- |
- |
- |
- |
- |
| 0.2177 |
34000 |
0.1865 |
- |
- |
- |
- |
- |
| 0.2241 |
35000 |
0.1878 |
0.1765 |
0.6709 (+0.1304) |
0.3858 (+0.0607) |
0.7060 (+0.2053) |
0.5875 (+0.1322) |
| 0.2305 |
36000 |
0.1822 |
- |
- |
- |
- |
- |
| 0.2369 |
37000 |
0.1795 |
- |
- |
- |
- |
- |
| 0.2433 |
38000 |
0.1802 |
- |
- |
- |
- |
- |
| 0.2497 |
39000 |
0.1762 |
- |
- |
- |
- |
- |
| 0.2561 |
40000 |
0.1694 |
0.1739 |
0.6902 (+0.1498) |
0.3771 (+0.0521) |
0.7198 (+0.2192) |
0.5957 (+0.1403) |
| 0.2625 |
41000 |
0.1718 |
- |
- |
- |
- |
- |
| 0.2689 |
42000 |
0.1706 |
- |
- |
- |
- |
- |
| 0.2753 |
43000 |
0.1659 |
- |
- |
- |
- |
- |
| 0.2817 |
44000 |
0.1593 |
- |
- |
- |
- |
- |
| 0.2881 |
45000 |
0.1608 |
0.1532 |
0.7132 (+0.1728) |
0.3606 (+0.0356) |
0.7393 (+0.2386) |
0.6044 (+0.1490) |
| 0.2945 |
46000 |
0.1589 |
- |
- |
- |
- |
- |
| 0.3010 |
47000 |
0.1563 |
- |
- |
- |
- |
- |
| 0.3074 |
48000 |
0.1553 |
- |
- |
- |
- |
- |
| 0.3138 |
49000 |
0.155 |
- |
- |
- |
- |
- |
| 0.3202 |
50000 |
0.1501 |
0.1373 |
0.7168 (+0.1764) |
0.3830 (+0.0579) |
0.6954 (+0.1948) |
0.5984 (+0.1430) |
| 0.3266 |
51000 |
0.1508 |
- |
- |
- |
- |
- |
| 0.3330 |
52000 |
0.1497 |
- |
- |
- |
- |
- |
| 0.3394 |
53000 |
0.1478 |
- |
- |
- |
- |
- |
| 0.3458 |
54000 |
0.1445 |
- |
- |
- |
- |
- |
| 0.3522 |
55000 |
0.1468 |
0.1403 |
0.6828 (+0.1424) |
0.3780 (+0.0530) |
0.7147 (+0.2141) |
0.5919 (+0.1365) |
| 0.3586 |
56000 |
0.1422 |
- |
- |
- |
- |
- |
| 0.3650 |
57000 |
0.1369 |
- |
- |
- |
- |
- |
| 0.3714 |
58000 |
0.1364 |
- |
- |
- |
- |
- |
| 0.3778 |
59000 |
0.1328 |
- |
- |
- |
- |
- |
| 0.3842 |
60000 |
0.1351 |
0.1448 |
0.6881 (+0.1477) |
0.3430 (+0.0179) |
0.7267 (+0.2260) |
0.5859 (+0.1306) |
| 0.3906 |
61000 |
0.1312 |
- |
- |
- |
- |
- |
| 0.3970 |
62000 |
0.1308 |
- |
- |
- |
- |
- |
| 0.4034 |
63000 |
0.1289 |
- |
- |
- |
- |
- |
| 0.4098 |
64000 |
0.1273 |
- |
- |
- |
- |
- |
| 0.4162 |
65000 |
0.1257 |
0.1290 |
0.7288 (+0.1883) |
0.3830 (+0.0580) |
0.7180 (+0.2173) |
0.6099 (+0.1545) |
| 0.4226 |
66000 |
0.1246 |
- |
- |
- |
- |
- |
| 0.4290 |
67000 |
0.1275 |
- |
- |
- |
- |
- |
| 0.4354 |
68000 |
0.1246 |
- |
- |
- |
- |
- |
| 0.4418 |
69000 |
0.1214 |
- |
- |
- |
- |
- |
| 0.4482 |
70000 |
0.115 |
0.1184 |
0.6911 (+0.1506) |
0.3903 (+0.0652) |
0.7189 (+0.2182) |
0.6001 (+0.1447) |
| 0.4546 |
71000 |
0.113 |
- |
- |
- |
- |
- |
| 0.4610 |
72000 |
0.1156 |
- |
- |
- |
- |
- |
| 0.4674 |
73000 |
0.1142 |
- |
- |
- |
- |
- |
| 0.4738 |
74000 |
0.1133 |
- |
- |
- |
- |
- |
| 0.4802 |
75000 |
0.1132 |
0.1194 |
0.7069 (+0.1665) |
0.3801 (+0.0550) |
0.7469 (+0.2462) |
0.6113 (+0.1559) |
| 0.4866 |
76000 |
0.1085 |
- |
- |
- |
- |
- |
| 0.4930 |
77000 |
0.1095 |
- |
- |
- |
- |
- |
| 0.4994 |
78000 |
0.1105 |
- |
- |
- |
- |
- |
| 0.5059 |
79000 |
0.1068 |
- |
- |
- |
- |
- |
| 0.5123 |
80000 |
0.1039 |
0.1085 |
0.7017 (+0.1612) |
0.3565 (+0.0315) |
0.7199 (+0.2192) |
0.5927 (+0.1373) |
| 0.5187 |
81000 |
0.1059 |
- |
- |
- |
- |
- |
| 0.5251 |
82000 |
0.1001 |
- |
- |
- |
- |
- |
| 0.5315 |
83000 |
0.1019 |
- |
- |
- |
- |
- |
| 0.5379 |
84000 |
0.1021 |
- |
- |
- |
- |
- |
| 0.5443 |
85000 |
0.0982 |
0.0962 |
0.6842 (+0.1438) |
0.3516 (+0.0266) |
0.7431 (+0.2425) |
0.5930 (+0.1376) |
| 0.5507 |
86000 |
0.0967 |
- |
- |
- |
- |
- |
| 0.5571 |
87000 |
0.0962 |
- |
- |
- |
- |
- |
| 0.5635 |
88000 |
0.098 |
- |
- |
- |
- |
- |
| 0.5699 |
89000 |
0.0973 |
- |
- |
- |
- |
- |
| 0.5763 |
90000 |
0.0957 |
0.0863 |
0.6729 (+0.1325) |
0.3852 (+0.0601) |
0.7147 (+0.2141) |
0.5909 (+0.1356) |
| 0.5827 |
91000 |
0.0925 |
- |
- |
- |
- |
- |
| 0.5891 |
92000 |
0.0948 |
- |
- |
- |
- |
- |
| 0.5955 |
93000 |
0.0887 |
- |
- |
- |
- |
- |
| 0.6019 |
94000 |
0.0918 |
- |
- |
- |
- |
- |
| 0.6083 |
95000 |
0.0926 |
0.0846 |
0.6857 (+0.1453) |
0.3503 (+0.0253) |
0.7321 (+0.2315) |
0.5894 (+0.1340) |
| 0.6147 |
96000 |
0.0881 |
- |
- |
- |
- |
- |
| 0.6211 |
97000 |
0.0871 |
- |
- |
- |
- |
- |
| 0.6275 |
98000 |
0.0867 |
- |
- |
- |
- |
- |
| 0.6339 |
99000 |
0.0854 |
- |
- |
- |
- |
- |
| 0.6403 |
100000 |
0.0833 |
0.0790 |
0.6665 (+0.1261) |
0.3415 (+0.0165) |
0.6905 (+0.1898) |
0.5662 (+0.1108) |
| 0.6467 |
101000 |
0.0837 |
- |
- |
- |
- |
- |
| 0.6531 |
102000 |
0.0834 |
- |
- |
- |
- |
- |
| 0.6595 |
103000 |
0.0798 |
- |
- |
- |
- |
- |
| 0.6659 |
104000 |
0.0825 |
- |
- |
- |
- |
- |
| 0.6723 |
105000 |
0.0803 |
0.0750 |
0.6897 (+0.1493) |
0.3415 (+0.0165) |
0.7096 (+0.2090) |
0.5803 (+0.1249) |
| 0.6787 |
106000 |
0.076 |
- |
- |
- |
- |
- |
| 0.6851 |
107000 |
0.0782 |
- |
- |
- |
- |
- |
| 0.6915 |
108000 |
0.0786 |
- |
- |
- |
- |
- |
| 0.6979 |
109000 |
0.075 |
- |
- |
- |
- |
- |
| 0.7044 |
110000 |
0.0747 |
0.0690 |
0.6665 (+0.1261) |
0.3384 (+0.0134) |
0.7209 (+0.2202) |
0.5753 (+0.1199) |
| 0.7108 |
111000 |
0.0728 |
- |
- |
- |
- |
- |
| 0.7172 |
112000 |
0.0708 |
- |
- |
- |
- |
- |
| 0.7236 |
113000 |
0.0714 |
- |
- |
- |
- |
- |
| 0.7300 |
114000 |
0.0725 |
- |
- |
- |
- |
- |
| 0.7364 |
115000 |
0.0708 |
0.0659 |
0.6753 (+0.1348) |
0.3423 (+0.0172) |
0.7093 (+0.2087) |
0.5756 (+0.1202) |
| 0.7428 |
116000 |
0.0684 |
- |
- |
- |
- |
- |
| 0.7492 |
117000 |
0.0709 |
- |
- |
- |
- |
- |
| 0.7556 |
118000 |
0.0661 |
- |
- |
- |
- |
- |
| 0.7620 |
119000 |
0.0685 |
- |
- |
- |
- |
- |
| 0.7684 |
120000 |
0.0655 |
0.0613 |
0.6774 (+0.1369) |
0.3295 (+0.0044) |
0.7244 (+0.2238) |
0.5771 (+0.1217) |
| 0.7748 |
121000 |
0.0643 |
- |
- |
- |
- |
- |
| 0.7812 |
122000 |
0.066 |
- |
- |
- |
- |
- |
| 0.7876 |
123000 |
0.0625 |
- |
- |
- |
- |
- |
| 0.7940 |
124000 |
0.0653 |
- |
- |
- |
- |
- |
| 0.8004 |
125000 |
0.0619 |
0.0564 |
0.6797 (+0.1393) |
0.3598 (+0.0348) |
0.7193 (+0.2187) |
0.5863 (+0.1309) |
| 0.8068 |
126000 |
0.0616 |
- |
- |
- |
- |
- |
| 0.8132 |
127000 |
0.0607 |
- |
- |
- |
- |
- |
| 0.8196 |
128000 |
0.0584 |
- |
- |
- |
- |
- |
| 0.8260 |
129000 |
0.0609 |
- |
- |
- |
- |
- |
| 0.8324 |
130000 |
0.0568 |
0.0502 |
0.6855 (+0.1450) |
0.3394 (+0.0143) |
0.7297 (+0.2291) |
0.5849 (+0.1295) |
| 0.8388 |
131000 |
0.0577 |
- |
- |
- |
- |
- |
| 0.8452 |
132000 |
0.056 |
- |
- |
- |
- |
- |
| 0.8516 |
133000 |
0.0556 |
- |
- |
- |
- |
- |
| 0.8580 |
134000 |
0.0553 |
- |
- |
- |
- |
- |
| 0.8644 |
135000 |
0.0546 |
0.0471 |
0.6903 (+0.1499) |
0.3404 (+0.0153) |
0.7419 (+0.2413) |
0.5909 (+0.1355) |
| 0.8708 |
136000 |
0.0525 |
- |
- |
- |
- |
- |
| 0.8772 |
137000 |
0.0512 |
- |
- |
- |
- |
- |
| 0.8836 |
138000 |
0.0528 |
- |
- |
- |
- |
- |
| 0.8900 |
139000 |
0.0523 |
- |
- |
- |
- |
- |
| 0.8964 |
140000 |
0.0544 |
0.0442 |
0.6915 (+0.1511) |
0.3507 (+0.0257) |
0.7258 (+0.2251) |
0.5893 (+0.1340) |
| 0.9029 |
141000 |
0.0497 |
- |
- |
- |
- |
- |
| 0.9093 |
142000 |
0.0508 |
- |
- |
- |
- |
- |
| 0.9157 |
143000 |
0.0485 |
- |
- |
- |
- |
- |
| 0.9221 |
144000 |
0.0492 |
- |
- |
- |
- |
- |
| 0.9285 |
145000 |
0.0472 |
0.0442 |
0.6614 (+0.1210) |
0.3394 (+0.0144) |
0.7361 (+0.2355) |
0.5790 (+0.1236) |
| 0.9349 |
146000 |
0.0469 |
- |
- |
- |
- |
- |
| 0.9413 |
147000 |
0.0459 |
- |
- |
- |
- |
- |
| 0.9477 |
148000 |
0.0471 |
- |
- |
- |
- |
- |
| 0.9541 |
149000 |
0.0454 |
- |
- |
- |
- |
- |
| 0.9605 |
150000 |
0.0444 |
0.0429 |
0.6587 (+0.1183) |
0.3311 (+0.0060) |
0.7298 (+0.2291) |
0.5732 (+0.1178) |
| 0.9669 |
151000 |
0.0451 |
- |
- |
- |
- |
- |
| 0.9733 |
152000 |
0.0429 |
- |
- |
- |
- |
- |
| 0.9797 |
153000 |
0.0448 |
- |
- |
- |
- |
- |
| 0.9861 |
154000 |
0.0441 |
- |
- |
- |
- |
- |
| 0.9925 |
155000 |
0.0443 |
0.0418 |
0.6653 (+0.1249) |
0.3335 (+0.0084) |
0.7391 (+0.2385) |
0.5793 (+0.1239) |
| 0.9989 |
156000 |
0.0409 |
- |
- |
- |
- |
- |
| -1 |
-1 |
- |
- |
0.7069 (+0.1665) |
0.3801 (+0.0550) |
0.7469 (+0.2462) |
0.6113 (+0.1559) |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.11.10
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.49.0.dev0
- PyTorch: 2.6.0.dev20241112+cu121
- Accelerate: 1.2.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
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",
}