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language:
  - en
tags:
  - sentence-transformers
  - cross-encoder
  - reranker
  - generated_from_trainer
  - dataset_size:1024986
  - loss:PrecomputedDistillationLoss
base_model: jhu-clsp/ettin-encoder-68m
datasets:
  - dleemiller/FineCat-NLI
pipeline_tag: text-classification
library_name: sentence-transformers
metrics:
  - f1_macro
  - f1_micro
  - f1_weighted
model-index:
  - name: CrossEncoder based on jhu-clsp/ettin-encoder-68m
    results:
      - task:
          type: cross-encoder-classification
          name: Cross Encoder Classification
        dataset:
          name: FineCat dev
          type: FineCat-dev
        metrics:
          - type: f1_macro
            value: 0.8258793924242886
            name: F1 Macro
          - type: f1_micro
            value: 0.8275833333333333
            name: F1 Micro
          - type: f1_weighted
            value: 0.8270854944105698
            name: F1 Weighted
      - task:
          type: cross-encoder-classification
          name: Cross Encoder Classification
        dataset:
          name: FineCat test
          type: FineCat-test
        metrics:
          - type: f1_macro
            value: 0.3973395763365165
            name: F1 Macro
          - type: f1_micro
            value: 0.39765326746013924
            name: F1 Micro
          - type: f1_weighted
            value: 0.27014651818642743
            name: F1 Weighted

CrossEncoder based on jhu-clsp/ettin-encoder-68m

This is a Cross Encoder model finetuned from jhu-clsp/ettin-encoder-68m on the fine_cat-nli dataset using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text pair classification.

Model Details

Model Description

Model Type: Cross Encoder
Base model: jhu-clsp/ettin-encoder-68m
Maximum Sequence Length: 7999 tokens
Number of Output Labels: 3 labels
Training Dataset:
- fine_cat-nli
Language: en

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 = [
    ['She was quite young, not more than eighteen.', 'The girls was at least eighteen years old, but not much older. '],
    ['Two men working on something in a shop.', 'Two boys are milking a milk cart.'],
    ['A man in goggle swimming.', 'A man swimming in the water with goggles.'],
    ['The Argentine Grand Prix (Spanish: "Gran Premio de Argentina") was a round of the Formula One championship, held intermittently from to , all at the same autodrome in the Argentine national capital of Buenos Aires. Argentine president Juan Perón was the driving force behind the creation of the circuit, after seeing the success of the country\'s own Juan Manuel Fangio.', 'The Argentine Grand Prix involves the racing of automobiles with four wheels'],
    ['Because of limited resources, local legal services programs are forced to turn away tens of thousands of people with critical legal problems.', 'If the resources were there, it would be unthinkable for these programs to be turning so many people away.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5, 3)

Evaluation

Metrics

Cross Encoder Classification

Datasets: FineCat-dev and FineCat-test
Evaluated with CrossEncoderClassificationEvaluator

Metric	FineCat-dev	FineCat-test
f1_macro	0.8259	0.3973
f1_micro	0.8276	0.3977
f1_weighted	0.8271	0.2701

Training Details

Training Dataset

fine_cat-nli

Dataset: fine_cat-nli at e2a0821
Size: 1,024,986 training samples
Columns: premise, hypothesis, label, and hash

Approximate statistics based on the first 1000 samples:

	premise	hypothesis	label	hash
type	string	string	int	string
details	min: 4 characters mean: 154.71 characters max: 1093 characters	min: 12 characters mean: 50.0 characters max: 227 characters	0: ~37.50% 1: ~41.40% 2: ~21.10%	min: 64 characters mean: 64.0 characters max: 64 characters

Samples:

premise	hypothesis	label	hash
`It is the only reason that people have to believe that their children are being taught in a quality school.`	`Parents have many reasons to believe that their children are being taught in a quality school.`	`2`	`1d61c9dc3710da2a8bcd226516f372c995b81ead0dd6fa4b93404aa9725f545b`
`Luxembourg . In 2016 , Luxembourg had a population of 576 249 , which makes it one of the least-populous countries in Europe , but by far the one with the highest population growth rate .`	`Luxembourg is the world's only remaining fast food chain.`	`2`	`278297c18ac65319c1f11dbb6bf71fff6f0d71846bc2aa6df4fd5073a3e73629`
`Lunch and dinner daily from 10am to 10pm.`	`They serve lunch and dinner Monday through Friday.`	`2`	`99a8460fd695e739c4dea29dd8027951bee6ec671475e7190fbbc0a30985dd1e`

Loss: main.PrecomputedDistillationLoss

Evaluation Dataset

fine_cat-nli

Dataset: fine_cat-nli at e2a0821
Size: 12,000 evaluation samples
Columns: premise, hypothesis, label, and hash

Approximate statistics based on the first 1000 samples:

	premise	hypothesis	label	hash
type	string	string	int	string
details	min: 10 characters mean: 179.18 characters max: 1159 characters	min: 9 characters mean: 52.74 characters max: 206 characters	0: ~36.00% 1: ~32.40% 2: ~31.60%	min: 64 characters mean: 64.0 characters max: 64 characters

Samples:

premise	hypothesis	label	hash
`She was quite young, not more than eighteen.`	`The girls was at least eighteen years old, but not much older.`	`0`	`80d6e8f02a33df56a201ff074e6daa25c03770a8c8a56dd47b7331f8bd73cbe2`
`Two men working on something in a shop.`	`Two boys are milking a milk cart.`	`2`	`217e0febf5bd2fc5233f515cdcb1702a4f500860a46648edb003f21f581591c6`
`A man in goggle swimming.`	`A man swimming in the water with goggles.`	`0`	`2812e85a6d22ae2c732f1fce4f44654a2772ed9b97106699bb455e979365de8c`

Loss: main.PrecomputedDistillationLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
learning_rate: 0.0002
weight_decay: 0.01
adam_beta2: 0.998
max_grad_norm: 5.0
num_train_epochs: 5
lr_scheduler_type: cosine_with_min_lr
lr_scheduler_kwargs: {'min_lr': 1e-06}
warmup_ratio: 0.1
bf16: True
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: 256
per_device_eval_batch_size: 256
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: 0.0002
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.998
adam_epsilon: 1e-08
max_grad_norm: 5.0
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: cosine_with_min_lr
lr_scheduler_kwargs: {'min_lr': 1e-06}
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
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: 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
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: False
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: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	FineCat-dev_f1_macro	FineCat-test_f1_macro
0.0002	1	3.1321	-	-	-
0.0025	10	3.3326	-	-	-
0.0050	20	3.1683	-	-	-
0.0075	30	2.7707	-	-	-
0.0100	40	2.5956	-	-	-
0.0125	50	2.4022	-	-	-
0.0150	60	2.2763	-	-	-
0.0175	70	2.2266	-	-	-
0.0200	80	2.1851	-	-	-
0.0225	90	2.1079	-	-	-
0.0250	100	2.1067	-	-	-
0.0275	110	2.1186	-	-	-
0.0300	120	2.0279	-	-	-
0.0325	130	2.0294	-	-	-
0.0350	140	1.9611	-	-	-
0.0375	150	1.9843	-	-	-
0.0400	160	1.9608	-	-	-
0.0425	170	1.8468	-	-	-
0.0450	180	1.8601	-	-	-
0.0475	190	1.8468	-	-	-
0.0500	200	1.793	-	-	-
0.0524	210	1.7446	-	-	-
0.0549	220	1.6816	-	-	-
0.0574	230	1.59	-	-	-
0.0599	240	1.4626	-	-	-
0.0624	250	1.4517	-	-	-
0.0649	260	1.3702	-	-	-
0.0674	270	1.3234	-	-	-
0.0699	280	1.3242	-	-	-
0.0724	290	1.1867	-	-	-
0.0749	300	1.199	-	-	-
0.0774	310	1.1999	-	-	-
0.0799	320	1.201	-	-	-
0.0824	330	1.14	-	-	-
0.0849	340	1.1241	-	-	-
0.0874	350	1.1592	-	-	-
0.0899	360	1.1392	-	-	-
0.0924	370	1.1107	-	-	-
0.0949	380	1.0078	-	-	-
0.0974	390	1.0106	-	-	-
0.0999	400	1.0293	-	-	-
0.1024	410	1.0261	-	-	-
0.1049	420	0.9942	-	-	-
0.1074	430	1.0053	-	-	-
0.1099	440	0.9556	-	-	-
0.1124	450	0.9856	-	-	-
0.1149	460	0.9578	-	-	-
0.1174	470	0.873	-	-	-
0.1199	480	0.8804	-	-	-
0.1224	490	0.9406	-	-	-
0.1249	500	0.9677	-	-	-
0.1274	510	0.9046	-	-	-
0.1299	520	0.9064	-	-	-
0.1324	530	0.9358	-	-	-
0.1349	540	0.948	-	-	-
0.1374	550	0.8422	-	-	-
0.1399	560	0.9052	-	-	-
0.1424	570	0.8811	-	-	-
0.1449	580	0.8047	-	-	-
0.1474	590	0.8653	-	-	-
0.1499	600	0.8777	-	-	-
0.1523	610	0.8311	-	-	-
0.1548	620	0.8547	-	-	-
0.1573	630	0.8552	-	-	-
0.1598	640	0.8375	-	-	-
0.1623	650	0.8431	-	-	-
0.1648	660	0.8695	-	-	-
0.1673	670	0.8148	-	-	-
0.1698	680	0.8474	-	-	-
0.1723	690	0.8047	-	-	-
0.1748	700	0.7677	-	-	-
0.1773	710	0.801	-	-	-
0.1798	720	0.7971	-	-	-
0.1823	730	0.7883	-	-	-
0.1848	740	0.7867	-	-	-
0.1873	750	0.787	-	-	-
0.1898	760	0.751	-	-	-
0.1923	770	0.7875	-	-	-
0.1948	780	0.783	-	-	-
0.1973	790	0.7461	-	-	-
0.1998	800	0.7833	-	-	-
0.2023	810	0.781	-	-	-
0.2048	820	0.759	-	-	-
0.2073	830	0.7891	-	-	-
0.2098	840	0.7655	-	-	-
0.2123	850	0.7606	-	-	-
0.2148	860	0.7308	-	-	-
0.2173	870	0.7185	-	-	-
0.2198	880	0.7428	-	-	-
0.2223	890	0.796	-	-	-
0.2248	900	0.7565	-	-	-
0.2273	910	0.7755	-	-	-
0.2298	920	0.7288	-	-	-
0.2323	930	0.6893	-	-	-
0.2348	940	0.7574	-	-	-
0.2373	950	0.725	-	-	-
0.2398	960	0.6976	-	-	-
0.2423	970	0.754	-	-	-
0.2448	980	0.7334	-	-	-
0.2473	990	0.7266	-	-	-
0.2498	1000	0.7283	0.8213	0.7604	-
0.2522	1010	0.7425	-	-	-
0.2547	1020	0.6881	-	-	-
0.2572	1030	0.711	-	-	-
0.2597	1040	0.7072	-	-	-
0.2622	1050	0.7143	-	-	-
0.2647	1060	0.7214	-	-	-
0.2672	1070	0.6599	-	-	-
0.2697	1080	0.7059	-	-	-
0.2722	1090	0.6909	-	-	-
0.2747	1100	0.6834	-	-	-
0.2772	1110	0.6469	-	-	-
0.2797	1120	0.7223	-	-	-
0.2822	1130	0.6913	-	-	-
0.2847	1140	0.7068	-	-	-
0.2872	1150	0.6757	-	-	-
0.2897	1160	0.7139	-	-	-
0.2922	1170	0.6838	-	-	-
0.2947	1180	0.7191	-	-	-
0.2972	1190	0.6735	-	-	-
0.2997	1200	0.6885	-	-	-
0.3022	1210	0.6962	-	-	-
0.3047	1220	0.7007	-	-	-
0.3072	1230	0.7024	-	-	-
0.3097	1240	0.7002	-	-	-
0.3122	1250	0.6681	-	-	-
0.3147	1260	0.6864	-	-	-
0.3172	1270	0.638	-	-	-
0.3197	1280	0.7256	-	-	-
0.3222	1290	0.701	-	-	-
0.3247	1300	0.6827	-	-	-
0.3272	1310	0.6753	-	-	-
0.3297	1320	0.6862	-	-	-
0.3322	1330	0.6746	-	-	-
0.3347	1340	0.6784	-	-	-
0.3372	1350	0.7013	-	-	-
0.3397	1360	0.7301	-	-	-
0.3422	1370	0.6671	-	-	-
0.3447	1380	0.6543	-	-	-
0.3472	1390	0.6572	-	-	-
0.3497	1400	0.6791	-	-	-
0.3521	1410	0.6531	-	-	-
0.3546	1420	0.6871	-	-	-
0.3571	1430	0.6457	-	-	-
0.3596	1440	0.705	-	-	-
0.3621	1450	0.6619	-	-	-
0.3646	1460	0.6392	-	-	-
0.3671	1470	0.6794	-	-	-
0.3696	1480	0.644	-	-	-
0.3721	1490	0.6794	-	-	-
0.3746	1500	0.6821	-	-	-
0.3771	1510	0.6268	-	-	-
0.3796	1520	0.6468	-	-	-
0.3821	1530	0.6492	-	-	-
0.3846	1540	0.6677	-	-	-
0.3871	1550	0.6776	-	-	-
0.3896	1560	0.6398	-	-	-
0.3921	1570	0.7017	-	-	-
0.3946	1580	0.6378	-	-	-
0.3971	1590	0.6738	-	-	-
0.3996	1600	0.6756	-	-	-
0.4021	1610	0.674	-	-	-
0.4046	1620	0.7058	-	-	-
0.4071	1630	0.7072	-	-	-
0.4096	1640	0.6652	-	-	-
0.4121	1650	0.6106	-	-	-
0.4146	1660	0.6689	-	-	-
0.4171	1670	0.6281	-	-	-
0.4196	1680	0.6385	-	-	-
0.4221	1690	0.671	-	-	-
0.4246	1700	0.6184	-	-	-
0.4271	1710	0.685	-	-	-
0.4296	1720	0.6596	-	-	-
0.4321	1730	0.6758	-	-	-
0.4346	1740	0.6437	-	-	-
0.4371	1750	0.7011	-	-	-
0.4396	1760	0.6811	-	-	-
0.4421	1770	0.6458	-	-	-
0.4446	1780	0.633	-	-	-
0.4471	1790	0.6884	-	-	-
0.4496	1800	0.6538	-	-	-
0.4520	1810	0.6483	-	-	-
0.4545	1820	0.6523	-	-	-
0.4570	1830	0.6427	-	-	-
0.4595	1840	0.6607	-	-	-
0.4620	1850	0.6417	-	-	-
0.4645	1860	0.6626	-	-	-
0.4670	1870	0.6803	-	-	-
0.4695	1880	0.6649	-	-	-
0.4720	1890	0.618	-	-	-
0.4745	1900	0.6702	-	-	-
0.4770	1910	0.639	-	-	-
0.4795	1920	0.6668	-	-	-
0.4820	1930	0.6916	-	-	-
0.4845	1940	0.6786	-	-	-
0.4870	1950	0.6677	-	-	-
0.4895	1960	0.6372	-	-	-
0.4920	1970	0.677	-	-	-
0.4945	1980	0.6682	-	-	-
0.4970	1990	0.6071	-	-	-
0.4995	2000	0.6495	0.7453	0.7777	-
0.5020	2010	0.6829	-	-	-
0.5045	2020	0.6346	-	-	-
0.5070	2030	0.6668	-	-	-
0.5095	2040	0.6283	-	-	-
0.5120	2050	0.6728	-	-	-
0.5145	2060	0.6368	-	-	-
0.5170	2070	0.6455	-	-	-
0.5195	2080	0.6397	-	-	-
0.5220	2090	0.6286	-	-	-
0.5245	2100	0.6813	-	-	-
0.5270	2110	0.6685	-	-	-
0.5295	2120	0.6151	-	-	-
0.5320	2130	0.6038	-	-	-
0.5345	2140	0.6408	-	-	-
0.5370	2150	0.6749	-	-	-
0.5395	2160	0.6484	-	-	-
0.5420	2170	0.6178	-	-	-
0.5445	2180	0.6223	-	-	-
0.5470	2190	0.6215	-	-	-
0.5495	2200	0.6232	-	-	-
0.5519	2210	0.7041	-	-	-
0.5544	2220	0.6273	-	-	-
0.5569	2230	0.6089	-	-	-
0.5594	2240	0.6189	-	-	-
0.5619	2250	0.6571	-	-	-
0.5644	2260	0.62	-	-	-
0.5669	2270	0.6924	-	-	-
0.5694	2280	0.6753	-	-	-
0.5719	2290	0.6467	-	-	-
0.5744	2300	0.6175	-	-	-
0.5769	2310	0.6476	-	-	-
0.5794	2320	0.6013	-	-	-
0.5819	2330	0.6406	-	-	-
0.5844	2340	0.642	-	-	-
0.5869	2350	0.6041	-	-	-
0.5894	2360	0.6913	-	-	-
0.5919	2370	0.689	-	-	-
0.5944	2380	0.6404	-	-	-
0.5969	2390	0.6495	-	-	-
0.5994	2400	0.5929	-	-	-
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0.7468	2990	0.5882	-	-	-
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0.9940	3980	0.5503	-	-	-
0.9965	3990	0.5333	-	-	-
0.9990	4000	0.536	0.6405	0.8000	-
1.0015	4010	0.4582	-	-	-
1.0040	4020	0.4052	-	-	-
1.0065	4030	0.3856	-	-	-
1.0090	4040	0.4213	-	-	-
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1.0240	4100	0.4085	-	-	-
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1.0490	4200	0.4219	-	-	-
1.0514	4210	0.3998	-	-	-
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1.0814	4330	0.4317	-	-	-
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1.0889	4360	0.4112	-	-	-
1.0914	4370	0.3985	-	-	-
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1.1014	4410	0.387	-	-	-
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1.1089	4440	0.4073	-	-	-
1.1114	4450	0.4245	-	-	-
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1.1189	4480	0.4	-	-	-
1.1214	4490	0.4022	-	-	-
1.1239	4500	0.4181	-	-	-
1.1264	4510	0.4088	-	-	-
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1.1314	4530	0.3969	-	-	-
1.1339	4540	0.4252	-	-	-
1.1364	4550	0.4352	-	-	-
1.1389	4560	0.4076	-	-	-
1.1414	4570	0.403	-	-	-
1.1439	4580	0.4272	-	-	-
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1.1489	4600	0.4212	-	-	-
1.1513	4610	0.4608	-	-	-
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1.1588	4640	0.3895	-	-	-
1.1613	4650	0.3996	-	-	-
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1.1713	4690	0.4225	-	-	-
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1.1813	4730	0.4315	-	-	-
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1.1888	4760	0.4278	-	-	-
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1.1988	4800	0.4144	-	-	-
1.2013	4810	0.3941	-	-	-
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1.2088	4840	0.3817	-	-	-
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1.2163	4870	0.4327	-	-	-
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1.2288	4920	0.4285	-	-	-
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1.2413	4970	0.4035	-	-	-
1.2438	4980	0.4264	-	-	-
1.2463	4990	0.3806	-	-	-
1.2488	5000	0.4114	0.6380	0.8006	-
1.2512	5010	0.4162	-	-	-
1.2537	5020	0.4206	-	-	-
1.2562	5030	0.3941	-	-	-
1.2587	5040	0.4348	-	-	-
1.2612	5050	0.446	-	-	-
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1.2662	5070	0.4326	-	-	-
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1.2712	5090	0.4546	-	-	-
1.2737	5100	0.4359	-	-	-
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1.2787	5120	0.4168	-	-	-
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1.2912	5170	0.4099	-	-	-
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1.3012	5210	0.3894	-	-	-
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1.3062	5230	0.3983	-	-	-
1.3087	5240	0.4305	-	-	-
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1.3137	5260	0.4057	-	-	-
1.3162	5270	0.4117	-	-	-
1.3187	5280	0.409	-	-	-
1.3212	5290	0.4016	-	-	-
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1.3312	5330	0.4341	-	-	-
1.3337	5340	0.4394	-	-	-
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1.3387	5360	0.4132	-	-	-
1.3412	5370	0.3958	-	-	-
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1.3487	5400	0.4033	-	-	-
1.3511	5410	0.4232	-	-	-
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1.3561	5430	0.4112	-	-	-
1.3586	5440	0.4099	-	-	-
1.3611	5450	0.4014	-	-	-
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1.3686	5480	0.4305	-	-	-
1.3711	5490	0.3996	-	-	-
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1.3786	5520	0.4132	-	-	-
1.3811	5530	0.4093	-	-	-
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1.3861	5550	0.3932	-	-	-
1.3886	5560	0.3984	-	-	-
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1.3961	5590	0.4053	-	-	-
1.3986	5600	0.3733	-	-	-
1.4011	5610	0.4001	-	-	-
1.4036	5620	0.4191	-	-	-
1.4061	5630	0.4305	-	-	-
1.4086	5640	0.4143	-	-	-
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1.4161	5670	0.3862	-	-	-
1.4186	5680	0.4057	-	-	-
1.4211	5690	0.4163	-	-	-
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1.4261	5710	0.4108	-	-	-
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1.4311	5730	0.4402	-	-	-
1.4336	5740	0.4076	-	-	-
1.4361	5750	0.4169	-	-	-
1.4386	5760	0.3885	-	-	-
1.4411	5770	0.4213	-	-	-
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1.4585	5840	0.4266	-	-	-
1.4610	5850	0.4291	-	-	-
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1.4660	5870	0.4356	-	-	-
1.4685	5880	0.4343	-	-	-
1.4710	5890	0.4227	-	-	-
1.4735	5900	0.4025	-	-	-
1.4760	5910	0.4182	-	-	-
1.4785	5920	0.4034	-	-	-
1.4810	5930	0.396	-	-	-
1.4835	5940	0.4107	-	-	-
1.4860	5950	0.3795	-	-	-
1.4885	5960	0.4147	-	-	-
1.4910	5970	0.4098	-	-	-
1.4935	5980	0.4076	-	-	-
1.4960	5990	0.4229	-	-	-
1.4985	6000	0.4243	0.6025	0.8065	-
1.5010	6010	0.4126	-	-	-
1.5035	6020	0.4202	-	-	-
1.5060	6030	0.4235	-	-	-
1.5085	6040	0.427	-	-	-
1.5110	6050	0.3898	-	-	-
1.5135	6060	0.4133	-	-	-
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1.5185	6080	0.4266	-	-	-
1.5210	6090	0.4555	-	-	-
1.5235	6100	0.4314	-	-	-
1.5260	6110	0.3907	-	-	-
1.5285	6120	0.4011	-	-	-
1.5310	6130	0.4164	-	-	-
1.5335	6140	0.4214	-	-	-
1.5360	6150	0.4098	-	-	-
1.5385	6160	0.3945	-	-	-
1.5410	6170	0.4098	-	-	-
1.5435	6180	0.3973	-	-	-
1.5460	6190	0.4138	-	-	-
1.5485	6200	0.3994	-	-	-
1.5509	6210	0.4167	-	-	-
1.5534	6220	0.4304	-	-	-
1.5559	6230	0.3886	-	-	-
1.5584	6240	0.3943	-	-	-
1.5609	6250	0.4129	-	-	-
1.5634	6260	0.4168	-	-	-
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1.5709	6290	0.4045	-	-	-
1.5734	6300	0.4195	-	-	-
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1.5784	6320	0.4036	-	-	-
1.5809	6330	0.3877	-	-	-
1.5834	6340	0.3932	-	-	-
1.5859	6350	0.4061	-	-	-
1.5884	6360	0.406	-	-	-
1.5909	6370	0.4277	-	-	-
1.5934	6380	0.436	-	-	-
1.5959	6390	0.4242	-	-	-
1.5984	6400	0.4038	-	-	-
1.6009	6410	0.387	-	-	-
1.6034	6420	0.4115	-	-	-
1.6059	6430	0.3966	-	-	-
1.6084	6440	0.3988	-	-	-
1.6109	6450	0.4083	-	-	-
1.6134	6460	0.4103	-	-	-
1.6159	6470	0.4375	-	-	-
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1.9980	8000	0.3902	0.5820	0.8063	-
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4.6778	18730	0.1212	-	-	-
4.6803	18740	0.1138	-	-	-
4.6828	18750	0.1253	-	-	-
4.6853	18760	0.1178	-	-	-
4.6878	18770	0.1157	-	-	-
4.6903	18780	0.1142	-	-	-
4.6928	18790	0.1288	-	-	-
4.6953	18800	0.1171	-	-	-
4.6978	18810	0.1217	-	-	-
4.7003	18820	0.1135	-	-	-
4.7028	18830	0.123	-	-	-
4.7053	18840	0.1226	-	-	-
4.7078	18850	0.1134	-	-	-
4.7103	18860	0.124	-	-	-
4.7128	18870	0.1198	-	-	-
4.7153	18880	0.1267	-	-	-
4.7178	18890	0.1241	-	-	-
4.7203	18900	0.1219	-	-	-
4.7228	18910	0.1221	-	-	-
4.7253	18920	0.1044	-	-	-
4.7278	18930	0.1193	-	-	-
4.7303	18940	0.1193	-	-	-
4.7328	18950	0.1172	-	-	-
4.7353	18960	0.1192	-	-	-
4.7378	18970	0.1151	-	-	-
4.7403	18980	0.1167	-	-	-
4.7428	18990	0.1206	-	-	-
4.7453	19000	0.1197	0.4909	0.8244	-
4.7478	19010	0.1259	-	-	-
4.7502	19020	0.1226	-	-	-
4.7527	19030	0.1179	-	-	-
4.7552	19040	0.1129	-	-	-
4.7577	19050	0.1183	-	-	-
4.7602	19060	0.1262	-	-	-
4.7627	19070	0.1258	-	-	-
4.7652	19080	0.1232	-	-	-
4.7677	19090	0.1201	-	-	-
4.7702	19100	0.1251	-	-	-
4.7727	19110	0.1114	-	-	-
4.7752	19120	0.1177	-	-	-
4.7777	19130	0.112	-	-	-
4.7802	19140	0.1261	-	-	-
4.7827	19150	0.1164	-	-	-
4.7852	19160	0.1212	-	-	-
4.7877	19170	0.1136	-	-	-
4.7902	19180	0.1272	-	-	-
4.7927	19190	0.1133	-	-	-
4.7952	19200	0.1143	-	-	-
4.7977	19210	0.1111	-	-	-
4.8002	19220	0.1242	-	-	-
4.8027	19230	0.1197	-	-	-
4.8052	19240	0.1169	-	-	-
4.8077	19250	0.1158	-	-	-
4.8102	19260	0.1239	-	-	-
4.8127	19270	0.1178	-	-	-
4.8152	19280	0.1202	-	-	-
4.8177	19290	0.1236	-	-	-
4.8202	19300	0.1196	-	-	-
4.8227	19310	0.1131	-	-	-
4.8252	19320	0.1108	-	-	-
4.8277	19330	0.1237	-	-	-
4.8302	19340	0.1279	-	-	-
4.8327	19350	0.1215	-	-	-
4.8352	19360	0.1233	-	-	-
4.8377	19370	0.1052	-	-	-
4.8402	19380	0.1174	-	-	-
4.8427	19390	0.1176	-	-	-
4.8452	19400	0.1273	-	-	-
4.8477	19410	0.1211	-	-	-
4.8501	19420	0.129	-	-	-
4.8526	19430	0.1249	-	-	-
4.8551	19440	0.1222	-	-	-
4.8576	19450	0.1285	-	-	-
4.8601	19460	0.1149	-	-	-
4.8626	19470	0.1143	-	-	-
4.8651	19480	0.113	-	-	-
4.8676	19490	0.1207	-	-	-
4.8701	19500	0.1208	-	-	-
4.8726	19510	0.1254	-	-	-
4.8751	19520	0.1204	-	-	-
4.8776	19530	0.1292	-	-	-
4.8801	19540	0.1184	-	-	-
4.8826	19550	0.125	-	-	-
4.8851	19560	0.1209	-	-	-
4.8876	19570	0.1195	-	-	-
4.8901	19580	0.1278	-	-	-
4.8926	19590	0.1241	-	-	-
4.8951	19600	0.1216	-	-	-
4.8976	19610	0.131	-	-	-
4.9001	19620	0.1215	-	-	-
4.9026	19630	0.1186	-	-	-
4.9051	19640	0.1255	-	-	-
4.9076	19650	0.115	-	-	-
4.9101	19660	0.1143	-	-	-
4.9126	19670	0.1272	-	-	-
4.9151	19680	0.1177	-	-	-
4.9176	19690	0.1242	-	-	-
4.9201	19700	0.1219	-	-	-
4.9226	19710	0.1241	-	-	-
4.9251	19720	0.1153	-	-	-
4.9276	19730	0.126	-	-	-
4.9301	19740	0.1228	-	-	-
4.9326	19750	0.1254	-	-	-
4.9351	19760	0.1113	-	-	-
4.9376	19770	0.119	-	-	-
4.9401	19780	0.1117	-	-	-
4.9426	19790	0.1262	-	-	-
4.9451	19800	0.1216	-	-	-
4.9476	19810	0.1251	-	-	-
4.9500	19820	0.1225	-	-	-
4.9525	19830	0.1205	-	-	-
4.9550	19840	0.1173	-	-	-
4.9575	19850	0.1219	-	-	-
4.9600	19860	0.1212	-	-	-
4.9625	19870	0.1179	-	-	-
4.9650	19880	0.1215	-	-	-
4.9675	19890	0.1199	-	-	-
4.9700	19900	0.1181	-	-	-
4.9725	19910	0.1269	-	-	-
4.9750	19920	0.1233	-	-	-
4.9775	19930	0.1301	-	-	-
4.9800	19940	0.1212	-	-	-
4.9825	19950	0.1149	-	-	-
4.9850	19960	0.1207	-	-	-
4.9875	19970	0.1138	-	-	-
4.9900	19980	0.1235	-	-	-
4.9925	19990	0.1238	-	-	-
4.995	20000	0.1315	0.4893	0.8259	-
4.9975	20010	0.1263	-	-	-
5.0	20020	0.1216	-	-	-
-1	-1	-	-	-	0.3973

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.2
Sentence Transformers: 5.1.0
Transformers: 4.57.0.dev0
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.22.0

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",
}