SentenceTransformer based on hon9kon9ize/bert-large-cantonese

This is a sentence-transformers model finetuned from hon9kon9ize/bert-large-cantonese on the yue-all-nli and sentence-transformers/all-nli dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: hon9kon9ize/bert-large-cantonese
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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 SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("hon9kon9ize/bert-large-cantonese-nli")
# Run inference
sentences = [
    '有個男人拋過咗個杆，後面有啲人同埋遮蓬。',
    '有個男人騰空躍起。',
    '個男人追緊個賊。',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

Datasets: sts-dev and sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	sts-dev	sts-test
pearson_cosine	0.8077	0.7648
spearman_cosine	0.8072	0.7611

Training Details

Training Dataset

Unnamed Dataset

Size: 1,115,217 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 4 tokens mean: 25.84 tokens max: 216 tokens	min: 4 tokens mean: 16.04 tokens max: 53 tokens	min: 4 tokens mean: 16.68 tokens max: 52 tokens

Samples:

anchor	positive	negative
`From Lincoln to Wilson it was 34, and since Wilson it has been 25.`	`Since Wilson it has been 25, and from Lincoln to Wilson 34.`	`From Lincoln to Wilson it was 99.`
`「我會上返我房。」跟住我就跟咗佢。`	`「我上房啦。」跟住我就跟咗佢上去。`	`我落去廚房沖杯茶先。`
`(To restore Internet Explorer, you'll need to have it on your original Windows 95 CD-ROM or another disk.`	`You need to have the original disk to restore Internet Explorer.`	`Internet explorer can be restored without a disk.`

Loss: GISTEmbedLoss with these parameters:

{'guide': SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
), 'temperature': 0.01}

Evaluation Dataset

yue-all-nli

Dataset: yue-all-nli at f6757e9
Size: 6,572 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 24.98 tokens max: 103 tokens	min: 6 tokens mean: 13.09 tokens max: 50 tokens	min: 7 tokens mean: 13.7 tokens max: 40 tokens

Samples:

anchor	positive	negative
`「兩個女人攬住，仲手持外賣包裝。」`	`兩個女人手持包裹。`	`班男人喺間熟食店外面打緊交。`
`「兩個著住藍色球衣嘅細路，一個背號係9，另一個係2，企喺浴室嘅木梯度，喺個鋅盤度洗手。`	`兩個著住有號碼球衣嘅細路仔喺度洗手。`	`兩個著住外套嘅小朋友返緊學。`
`一個男人喺安吉利斯市舉行嘅世界博覽會活動期間，向顧客賣甜甜圈。`	`有個男人賣緊甜甜圈俾個客人。`	`一個女人喺間細細嘅咖啡店飲緊咖啡。`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 80
per_device_eval_batch_size: 80
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
bf16: True
use_liger_kernel: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 80
per_device_eval_batch_size: 80
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: 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: 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: False
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: False
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: True
eval_use_gather_object: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	sts-dev_spearman_cosine	sts-test_spearman_cosine
0	0	-	-	0.6088	-
0.0007	10	8.9182	-	-	-
0.0014	20	8.3335	-	-	-
0.0022	30	7.8536	-	-	-
0.0029	40	7.2262	-	-	-
0.0036	50	5.892	-	-	-
0.0043	60	5.1125	-	-	-
0.0050	70	4.5982	-	-	-
0.0057	80	3.9755	-	-	-
0.0065	90	3.7588	-	-	-
0.0072	100	3.6977	-	-	-
0.0079	110	3.5546	-	-	-
0.0086	120	3.3271	-	-	-
0.0093	130	3.1647	-	-	-
0.0100	140	3.0859	-	-	-
0.0108	150	2.9647	-	-	-
0.0115	160	2.9156	-	-	-
0.0122	170	2.9001	-	-	-
0.0129	180	2.721	-	-	-
0.0136	190	2.7586	-	-	-
0.0143	200	2.601	-	-	-
0.0151	210	2.5653	-	-	-
0.0158	220	2.5726	-	-	-
0.0165	230	2.4649	-	-	-
0.0172	240	2.4707	-	-	-
0.0179	250	2.5434	-	-	-
0.0187	260	2.3945	-	-	-
0.0194	270	2.2557	-	-	-
0.0201	280	2.3749	-	-	-
0.0208	290	2.1641	-	-	-
0.0215	300	2.0744	-	-	-
0.0222	310	2.2832	-	-	-
0.0230	320	2.2298	-	-	-
0.0237	330	2.2006	-	-	-
0.0244	340	2.0964	-	-	-
0.0251	350	2.208	-	-	-
0.0258	360	2.1591	-	-	-
0.0265	370	1.9641	-	-	-
0.0273	380	2.1122	-	-	-
0.0280	390	1.9392	-	-	-
0.0287	400	1.9073	-	-	-
0.0294	410	2.115	-	-	-
0.0301	420	1.7491	-	-	-
0.0308	430	1.8553	-	-	-
0.0316	440	1.997	-	-	-
0.0323	450	1.8158	-	-	-
0.0330	460	1.8158	-	-	-
0.0337	470	1.78	-	-	-
0.0344	480	1.8675	-	-	-
0.0351	490	1.7096	-	-	-
0.0359	500	1.7854	-	-	-
0.0366	510	1.8731	-	-	-
0.0373	520	1.8561	-	-	-
0.0380	530	1.6927	-	-	-
0.0387	540	1.7424	-	-	-
0.0395	550	1.7394	-	-	-
0.0402	560	1.7621	-	-	-
0.0409	570	1.8423	-	-	-
0.0416	580	1.8296	-	-	-
0.0423	590	1.7202	-	-	-
0.0430	600	1.7966	-	-	-
0.0438	610	1.5712	-	-	-
0.0445	620	1.6416	-	-	-
0.0452	630	1.5977	-	-	-
0.0459	640	1.7382	-	-	-
0.0466	650	1.6744	-	-	-
0.0473	660	1.7537	-	-	-
0.0481	670	1.7131	-	-	-
0.0488	680	1.6714	-	-	-
0.0495	690	1.651	-	-	-
0.0502	700	1.6177	-	-	-
0.0509	710	1.5179	-	-	-
0.0516	720	1.612	-	-	-
0.0524	730	1.561	-	-	-
0.0531	740	1.5283	-	-	-
0.0538	750	1.5407	-	-	-
0.0545	760	1.4809	-	-	-
0.0552	770	1.5749	-	-	-
0.0560	780	1.5744	-	-	-
0.0567	790	1.5428	-	-	-
0.0574	800	1.4429	-	-	-
0.0581	810	1.4721	-	-	-
0.0588	820	1.3807	-	-	-
0.0595	830	1.4229	-	-	-
0.0603	840	1.5178	-	-	-
0.0610	850	1.4231	-	-	-
0.0617	860	1.4853	-	-	-
0.0624	870	1.4032	-	-	-
0.0631	880	1.473	-	-	-
0.0638	890	1.3368	-	-	-
0.0646	900	1.3146	-	-	-
0.0653	910	1.4207	-	-	-
0.0660	920	1.3747	-	-	-
0.0667	930	1.406	-	-	-
0.0674	940	1.2829	-	-	-
0.0681	950	1.3115	-	-	-
0.0689	960	1.4112	-	-	-
0.0696	970	1.4644	-	-	-
0.0703	980	1.3492	-	-	-
0.0710	990	1.4076	-	-	-
0.0717	1000	1.4196	-	-	-
0.0724	1010	1.4376	-	-	-
0.0732	1020	1.2883	-	-	-
0.0739	1030	1.3183	-	-	-
0.0746	1040	1.4535	-	-	-
0.0753	1050	1.28	-	-	-
0.0760	1060	1.2585	-	-	-
0.0768	1070	1.3905	-	-	-
0.0775	1080	1.3414	-	-	-
0.0782	1090	1.2586	-	-	-
0.0789	1100	1.2527	-	-	-
0.0796	1110	1.2847	-	-	-
0.0803	1120	1.2187	-	-	-
0.0811	1130	1.2561	-	-	-
0.0818	1140	1.3247	-	-	-
0.0825	1150	1.2935	-	-	-
0.0832	1160	1.4118	-	-	-
0.0839	1170	1.2976	-	-	-
0.0846	1180	1.2084	-	-	-
0.0854	1190	1.3157	-	-	-
0.0861	1200	1.2973	-	-	-
0.0868	1210	1.24	-	-	-
0.0875	1220	1.3447	-	-	-
0.0882	1230	1.2246	-	-	-
0.0889	1240	1.2742	-	-	-
0.0897	1250	1.1727	-	-	-
0.0904	1260	1.3523	-	-	-
0.0911	1270	1.228	-	-	-
0.0918	1280	1.1218	-	-	-
0.0925	1290	1.1027	-	-	-
0.0933	1300	1.3143	-	-	-
0.0940	1310	1.1356	-	-	-
0.0947	1320	1.2	-	-	-
0.0954	1330	1.118	-	-	-
0.0961	1340	1.1889	-	-	-
0.0968	1350	1.1722	-	-	-
0.0976	1360	1.1186	-	-	-
0.0983	1370	1.2979	-	-	-
0.0990	1380	1.1622	-	-	-
0.0997	1390	1.2492	-	-	-
0.1004	1400	1.1449	-	-	-
0.1011	1410	1.1141	-	-	-
0.1019	1420	1.3464	-	-	-
0.1026	1430	1.1921	-	-	-
0.1033	1440	1.0709	-	-	-
0.1040	1450	1.1985	-	-	-
0.1047	1460	1.1392	-	-	-
0.1054	1470	1.1942	-	-	-
0.1062	1480	1.1897	-	-	-
0.1069	1490	1.1081	-	-	-
0.1076	1500	1.1641	-	-	-
0.1083	1510	1.1414	-	-	-
0.1090	1520	1.1705	-	-	-
0.1097	1530	1.1629	-	-	-
0.1105	1540	1.1342	-	-	-
0.1112	1550	1.1283	-	-	-
0.1119	1560	1.1242	-	-	-
0.1126	1570	1.1932	-	-	-
0.1133	1580	1.075	-	-	-
0.1141	1590	1.1319	-	-	-
0.1148	1600	1.2497	-	-	-
0.1155	1610	0.9953	-	-	-
0.1162	1620	1.1005	-	-	-
0.1169	1630	1.0989	-	-	-
0.1176	1640	1.0662	-	-	-
0.1184	1650	1.1136	-	-	-
0.1191	1660	1.129	-	-	-
0.1198	1670	1.0414	-	-	-
0.1205	1680	1.0026	-	-	-
0.1212	1690	1.035	-	-	-
0.1219	1700	1.0701	-	-	-
0.1227	1710	1.0386	-	-	-
0.1234	1720	1.1529	-	-	-
0.1241	1730	1.0426	-	-	-
0.1248	1740	1.077	-	-	-
0.1255	1750	1.1176	-	-	-
0.1262	1760	1.0752	-	-	-
0.1270	1770	1.0145	-	-	-
0.1277	1780	1.0539	-	-	-
0.1284	1790	1.1963	-	-	-
0.1291	1800	1.1032	-	-	-
0.1298	1810	1.0652	-	-	-
0.1306	1820	1.144	-	-	-
0.1313	1830	1.1093	-	-	-
0.1320	1840	1.0215	-	-	-
0.1327	1850	1.152	-	-	-
0.1334	1860	0.9941	-	-	-
0.1341	1870	1.0329	-	-	-
0.1349	1880	0.9976	-	-	-
0.1356	1890	0.9901	-	-	-
0.1363	1900	1.1014	-	-	-
0.1370	1910	1.0418	-	-	-
0.1377	1920	1.1701	-	-	-
0.1384	1930	0.973	-	-	-
0.1392	1940	1.0644	-	-	-
0.1399	1950	1.0022	-	-	-
0.1406	1960	0.9863	-	-	-
0.1413	1970	0.929	-	-	-
0.1420	1980	1.0516	-	-	-
0.1427	1990	1.0123	-	-	-
0.1435	2000	1.0731	-	-	-
0.1442	2010	1.0337	-	-	-
0.1449	2020	0.9423	-	-	-
0.1456	2030	0.966	-	-	-
0.1463	2040	1.046	-	-	-
0.1470	2050	1.045	-	-	-
0.1478	2060	0.9203	-	-	-
0.1485	2070	0.9721	-	-	-
0.1492	2080	1.0341	-	-	-
0.1499	2090	1.0463	-	-	-
0.1506	2100	1.0051	-	-	-
0.1514	2110	1.0055	-	-	-
0.1521	2120	0.9688	-	-	-
0.1528	2130	1.0974	-	-	-
0.1535	2140	0.8852	-	-	-
0.1542	2150	1.0356	-	-	-
0.1549	2160	0.9915	-	-	-
0.1557	2170	0.9609	-	-	-
0.1562	2178	-	1.2093	0.7866	-
0.1564	2180	1.0349	-	-	-
0.1571	2190	0.899	-	-	-
0.1578	2200	1.0112	-	-	-
0.1585	2210	1.0009	-	-	-
0.1592	2220	0.9221	-	-	-
0.1600	2230	0.9595	-	-	-
0.1607	2240	0.9516	-	-	-
0.1614	2250	1.0355	-	-	-
0.1621	2260	0.9125	-	-	-
0.1628	2270	0.9046	-	-	-
0.1635	2280	0.9773	-	-	-
0.1643	2290	1.0821	-	-	-
0.1650	2300	0.9773	-	-	-
0.1657	2310	0.9909	-	-	-
0.1664	2320	0.9288	-	-	-
0.1671	2330	0.9354	-	-	-
0.1679	2340	0.9196	-	-	-
0.1686	2350	0.8961	-	-	-
0.1693	2360	0.9685	-	-	-
0.1700	2370	0.9669	-	-	-
0.1707	2380	0.9542	-	-	-
0.1714	2390	0.9911	-	-	-
0.1722	2400	0.8908	-	-	-
0.1729	2410	0.9395	-	-	-
0.1736	2420	0.9501	-	-	-
0.1743	2430	0.9534	-	-	-
0.1750	2440	0.9839	-	-	-
0.1757	2450	0.9619	-	-	-
0.1765	2460	0.937	-	-	-
0.1772	2470	0.8549	-	-	-
0.1779	2480	0.9377	-	-	-
0.1786	2490	0.8928	-	-	-
0.1793	2500	0.8883	-	-	-
0.1800	2510	0.9459	-	-	-
0.1808	2520	0.8577	-	-	-
0.1815	2530	0.8096	-	-	-
0.1822	2540	0.8988	-	-	-
0.1829	2550	0.9359	-	-	-
0.1836	2560	0.944	-	-	-
0.1843	2570	0.9284	-	-	-
0.1851	2580	0.8583	-	-	-
0.1858	2590	0.8604	-	-	-
0.1865	2600	0.9531	-	-	-
0.1872	2610	0.9074	-	-	-
0.1879	2620	0.8655	-	-	-
0.1887	2630	0.9546	-	-	-
0.1894	2640	0.9259	-	-	-
0.1901	2650	0.945	-	-	-
0.1908	2660	0.861	-	-	-
0.1915	2670	0.8892	-	-	-
0.1922	2680	0.9798	-	-	-
0.1930	2690	0.8644	-	-	-
0.1937	2700	0.95	-	-	-
0.1944	2710	0.931	-	-	-
0.1951	2720	0.7496	-	-	-
0.1958	2730	0.8855	-	-	-
0.1965	2740	0.8926	-	-	-
0.1973	2750	0.8792	-	-	-
0.1980	2760	0.8397	-	-	-
0.1987	2770	0.9355	-	-	-
0.1994	2780	0.9362	-	-	-
0.2001	2790	0.8009	-	-	-
0.2008	2800	0.7962	-	-	-
0.2016	2810	0.8475	-	-	-
0.2023	2820	0.8982	-	-	-
0.2030	2830	0.8046	-	-	-
0.2037	2840	0.9624	-	-	-
0.2044	2850	0.8324	-	-	-
0.2052	2860	0.7491	-	-	-
0.2059	2870	0.9558	-	-	-
0.2066	2880	0.8514	-	-	-
0.2073	2890	0.7838	-	-	-
0.2080	2900	0.7808	-	-	-
0.2087	2910	1.0037	-	-	-
0.2095	2920	0.9283	-	-	-
0.2102	2930	0.8422	-	-	-
0.2109	2940	0.8594	-	-	-
0.2116	2950	0.8546	-	-	-
0.2123	2960	0.8496	-	-	-
0.2130	2970	0.845	-	-	-
0.2138	2980	0.7914	-	-	-
0.2145	2990	0.8316	-	-	-
0.2152	3000	0.8721	-	-	-
0.2159	3010	0.6566	-	-	-
0.2166	3020	0.8026	-	-	-
0.2173	3030	0.8237	-	-	-
0.2181	3040	0.8142	-	-	-
0.2188	3050	0.7932	-	-	-
0.2195	3060	0.8751	-	-	-
0.2202	3070	0.8583	-	-	-
0.2209	3080	0.8627	-	-	-
0.2216	3090	0.8593	-	-	-
0.2224	3100	0.8595	-	-	-
0.2231	3110	0.8371	-	-	-
0.2238	3120	0.8579	-	-	-
0.2245	3130	0.8658	-	-	-
0.2252	3140	0.7946	-	-	-
0.2260	3150	0.8652	-	-	-
0.2267	3160	0.7412	-	-	-
0.2274	3170	0.7563	-	-	-
0.2281	3180	0.9034	-	-	-
0.2288	3190	0.8193	-	-	-
0.2295	3200	0.8001	-	-	-
0.2303	3210	0.794	-	-	-
0.2310	3220	0.8222	-	-	-
0.2317	3230	0.8456	-	-	-
0.2324	3240	0.8093	-	-	-
0.2331	3250	0.7943	-	-	-
0.2338	3260	0.8158	-	-	-
0.2346	3270	0.8379	-	-	-
0.2353	3280	0.8711	-	-	-
0.2360	3290	0.8452	-	-	-
0.2367	3300	0.7533	-	-	-
0.2374	3310	0.8267	-	-	-
0.2381	3320	0.818	-	-	-
0.2389	3330	0.8427	-	-	-
0.2396	3340	0.7445	-	-	-
0.2403	3350	0.8246	-	-	-
0.2410	3360	0.8033	-	-	-
0.2417	3370	0.8017	-	-	-
0.2425	3380	0.7425	-	-	-
0.2432	3390	0.8606	-	-	-
0.2439	3400	0.7978	-	-	-
0.2446	3410	0.8827	-	-	-
0.2453	3420	0.7308	-	-	-
0.2460	3430	0.819	-	-	-
0.2468	3440	0.7908	-	-	-
0.2475	3450	0.7302	-	-	-
0.2482	3460	0.7982	-	-	-
0.2489	3470	0.8015	-	-	-
0.2496	3480	0.7676	-	-	-
0.2503	3490	0.8107	-	-	-
0.2511	3500	0.8125	-	-	-
0.2518	3510	0.8307	-	-	-
0.2525	3520	0.7488	-	-	-
0.2532	3530	0.92	-	-	-
0.2539	3540	0.6889	-	-	-
0.2546	3550	0.7914	-	-	-
0.2554	3560	0.7938	-	-	-
0.2561	3570	0.8257	-	-	-
0.2568	3580	0.7064	-	-	-
0.2575	3590	0.7717	-	-	-
0.2582	3600	0.631	-	-	-
0.2589	3610	0.8091	-	-	-
0.2597	3620	0.7559	-	-	-
0.2604	3630	0.7483	-	-	-
0.2611	3640	0.7886	-	-	-
0.2618	3650	0.6773	-	-	-
0.2625	3660	0.7352	-	-	-
0.2633	3670	0.8096	-	-	-
0.2640	3680	0.79	-	-	-
0.2647	3690	0.6886	-	-	-
0.2654	3700	0.7923	-	-	-
0.2661	3710	0.7476	-	-	-
0.2668	3720	0.8992	-	-	-
0.2676	3730	0.733	-	-	-
0.2683	3740	0.6898	-	-	-
0.2690	3750	0.7969	-	-	-
0.2697	3760	0.7556	-	-	-
0.2704	3770	0.786	-	-	-
0.2711	3780	0.827	-	-	-
0.2719	3790	0.75	-	-	-
0.2726	3800	0.7101	-	-	-
0.2733	3810	0.8142	-	-	-
0.2740	3820	0.7928	-	-	-
0.2747	3830	0.788	-	-	-
0.2754	3840	0.7075	-	-	-
0.2762	3850	0.695	-	-	-
0.2769	3860	0.7571	-	-	-
0.2776	3870	0.6798	-	-	-
0.2783	3880	0.8626	-	-	-
0.2790	3890	0.7438	-	-	-
0.2798	3900	0.7301	-	-	-
0.2805	3910	0.7628	-	-	-
0.2812	3920	0.726	-	-	-
0.2819	3930	0.7678	-	-	-
0.2826	3940	0.7113	-	-	-
0.2833	3950	0.723	-	-	-
0.2841	3960	0.771	-	-	-
0.2848	3970	0.787	-	-	-
0.2855	3980	0.6815	-	-	-
0.2862	3990	0.7384	-	-	-
0.2869	4000	0.7465	-	-	-
0.2876	4010	0.6722	-	-	-
0.2884	4020	0.7361	-	-	-
0.2891	4030	0.7149	-	-	-
0.2898	4040	0.773	-	-	-
0.2905	4050	0.7779	-	-	-
0.2912	4060	0.7116	-	-	-
0.2919	4070	0.6573	-	-	-
0.2927	4080	0.8571	-	-	-
0.2934	4090	0.7424	-	-	-
0.2941	4100	0.7404	-	-	-
0.2948	4110	0.7462	-	-	-
0.2955	4120	0.625	-	-	-
0.2962	4130	0.7387	-	-	-
0.2970	4140	0.7127	-	-	-
0.2977	4150	0.7645	-	-	-
0.2984	4160	0.7598	-	-	-
0.2991	4170	0.649	-	-	-
0.2998	4180	0.7683	-	-	-
0.3006	4190	0.6465	-	-	-
0.3013	4200	0.7598	-	-	-
0.3020	4210	0.7458	-	-	-
0.3027	4220	0.7207	-	-	-
0.3034	4230	0.6873	-	-	-
0.3041	4240	0.7018	-	-	-
0.3049	4250	0.7524	-	-	-
0.3056	4260	0.6823	-	-	-
0.3063	4270	0.5722	-	-	-
0.3070	4280	0.7361	-	-	-
0.3077	4290	0.7342	-	-	-
0.3084	4300	0.6414	-	-	-
0.3092	4310	0.6505	-	-	-
0.3099	4320	0.7799	-	-	-
0.3106	4330	0.7294	-	-	-
0.3113	4340	0.6403	-	-	-
0.3120	4350	0.7521	-	-	-
0.3125	4356	-	0.9868	0.7972	-
0.3127	4360	0.7467	-	-	-
0.3135	4370	0.7838	-	-	-
0.3142	4380	0.683	-	-	-
0.3149	4390	0.7511	-	-	-
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0.3163	4410	0.7084	-	-	-
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0.3178	4430	0.8077	-	-	-
0.3185	4440	0.7891	-	-	-
0.3192	4450	0.6701	-	-	-
0.3199	4460	0.7232	-	-	-
0.3206	4470	0.787	-	-	-
0.3214	4480	0.6689	-	-	-
0.3221	4490	0.7594	-	-	-
0.3228	4500	0.75	-	-	-
0.3235	4510	0.6784	-	-	-
0.3242	4520	0.7004	-	-	-
0.3249	4530	0.7308	-	-	-
0.3257	4540	0.752	-	-	-
0.3264	4550	0.7164	-	-	-
0.3271	4560	0.6964	-	-	-
0.3278	4570	0.7029	-	-	-
0.3285	4580	0.6584	-	-	-
0.3292	4590	0.6218	-	-	-
0.3300	4600	0.779	-	-	-
0.3307	4610	0.6442	-	-	-
0.3314	4620	0.6317	-	-	-
0.3321	4630	0.6734	-	-	-
0.3328	4640	0.7953	-	-	-
0.3335	4650	0.6617	-	-	-
0.3343	4660	0.6404	-	-	-
0.3350	4670	0.6407	-	-	-
0.3357	4680	0.6547	-	-	-
0.3364	4690	0.7249	-	-	-
0.3371	4700	0.7525	-	-	-
0.3379	4710	0.7395	-	-	-
0.3386	4720	0.6307	-	-	-
0.3393	4730	0.6092	-	-	-
0.3400	4740	0.6135	-	-	-
0.3407	4750	0.7408	-	-	-
0.3414	4760	0.6585	-	-	-
0.3422	4770	0.6396	-	-	-
0.3429	4780	0.6022	-	-	-
0.3436	4790	0.6295	-	-	-
0.3443	4800	0.8024	-	-	-
0.3450	4810	0.6866	-	-	-
0.3457	4820	0.5793	-	-	-
0.3465	4830	0.6637	-	-	-
0.3472	4840	0.6971	-	-	-
0.3479	4850	0.6391	-	-	-
0.3486	4860	0.7	-	-	-
0.3493	4870	0.6213	-	-	-
0.3500	4880	0.6752	-	-	-
0.3508	4890	0.6263	-	-	-
0.3515	4900	0.6575	-	-	-
0.3522	4910	0.6694	-	-	-
0.3529	4920	0.6498	-	-	-
0.3536	4930	0.6279	-	-	-
0.3544	4940	0.7128	-	-	-
0.3551	4950	0.6603	-	-	-
0.3558	4960	0.6351	-	-	-
0.3565	4970	0.6578	-	-	-
0.3572	4980	0.6546	-	-	-
0.3579	4990	0.7007	-	-	-
0.3587	5000	0.6485	-	-	-
0.3594	5010	0.6139	-	-	-
0.3601	5020	0.6885	-	-	-
0.3608	5030	0.6312	-	-	-
0.3615	5040	0.5633	-	-	-
0.3622	5050	0.6518	-	-	-
0.3630	5060	0.7098	-	-	-
0.3637	5070	0.583	-	-	-
0.3644	5080	0.5949	-	-	-
0.3651	5090	0.7185	-	-	-
0.3658	5100	0.6777	-	-	-
0.3665	5110	0.6809	-	-	-
0.3673	5120	0.6909	-	-	-
0.3680	5130	0.6251	-	-	-
0.3687	5140	0.6099	-	-	-
0.3694	5150	0.6659	-	-	-
0.3701	5160	0.6516	-	-	-
0.3708	5170	0.664	-	-	-
0.3716	5180	0.6589	-	-	-
0.3723	5190	0.6371	-	-	-
0.3730	5200	0.6692	-	-	-
0.3737	5210	0.6228	-	-	-
0.3744	5220	0.6432	-	-	-
0.3752	5230	0.6663	-	-	-
0.3759	5240	0.6402	-	-	-
0.3766	5250	0.5334	-	-	-
0.3773	5260	0.6676	-	-	-
0.3780	5270	0.6822	-	-	-
0.3787	5280	0.5719	-	-	-
0.3795	5290	0.6703	-	-	-
0.3802	5300	0.6444	-	-	-
0.3809	5310	0.6299	-	-	-
0.3816	5320	0.6906	-	-	-
0.3823	5330	0.5978	-	-	-
0.3830	5340	0.6278	-	-	-
0.3838	5350	0.6683	-	-	-
0.3845	5360	0.5997	-	-	-
0.3852	5370	0.6662	-	-	-
0.3859	5380	0.6641	-	-	-
0.3866	5390	0.6498	-	-	-
0.3873	5400	0.6363	-	-	-
0.3881	5410	0.6324	-	-	-
0.3888	5420	0.6702	-	-	-
0.3895	5430	0.6308	-	-	-
0.3902	5440	0.6571	-	-	-
0.3909	5450	0.6408	-	-	-
0.3917	5460	0.6606	-	-	-
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0.3938	5490	0.5959	-	-	-
0.3945	5500	0.5782	-	-	-
0.3952	5510	0.6109	-	-	-
0.3960	5520	0.6388	-	-	-
0.3967	5530	0.7233	-	-	-
0.3974	5540	0.6029	-	-	-
0.3981	5550	0.6285	-	-	-
0.3988	5560	0.6243	-	-	-
0.3995	5570	0.6196	-	-	-
0.4003	5580	0.579	-	-	-
0.4010	5590	0.5563	-	-	-
0.4017	5600	0.5994	-	-	-
0.4024	5610	0.6865	-	-	-
0.4031	5620	0.6909	-	-	-
0.4038	5630	0.6201	-	-	-
0.4046	5640	0.5478	-	-	-
0.4053	5650	0.6121	-	-	-
0.4060	5660	0.5546	-	-	-
0.4067	5670	0.6614	-	-	-
0.4074	5680	0.657	-	-	-
0.4081	5690	0.6096	-	-	-
0.4089	5700	0.6035	-	-	-
0.4096	5710	0.605	-	-	-
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0.4117	5740	0.5671	-	-	-
0.4125	5750	0.6159	-	-	-
0.4132	5760	0.64	-	-	-
0.4139	5770	0.5673	-	-	-
0.4146	5780	0.6086	-	-	-
0.4153	5790	0.5885	-	-	-
0.4160	5800	0.5774	-	-	-
0.4168	5810	0.6652	-	-	-
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0.4182	5830	0.6169	-	-	-
0.4189	5840	0.612	-	-	-
0.4196	5850	0.6474	-	-	-
0.4203	5860	0.6296	-	-	-
0.4211	5870	0.6413	-	-	-
0.4218	5880	0.5958	-	-	-
0.4225	5890	0.7245	-	-	-
0.4232	5900	0.6638	-	-	-
0.4239	5910	0.6368	-	-	-
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0.4254	5930	0.5735	-	-	-
0.4261	5940	0.6299	-	-	-
0.4268	5950	0.6276	-	-	-
0.4275	5960	0.6121	-	-	-
0.4282	5970	0.6351	-	-	-
0.4290	5980	0.6644	-	-	-
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0.4304	6000	0.6165	-	-	-
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0.4318	6020	0.5443	-	-	-
0.4325	6030	0.6167	-	-	-
0.4333	6040	0.58	-	-	-
0.4340	6050	0.5833	-	-	-
0.4347	6060	0.6181	-	-	-
0.4354	6070	0.574	-	-	-
0.4361	6080	0.5628	-	-	-
0.4368	6090	0.6423	-	-	-
0.4376	6100	0.6041	-	-	-
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0.4390	6120	0.5613	-	-	-
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0.4404	6140	0.64	-	-	-
0.4411	6150	0.5376	-	-	-
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0.4426	6170	0.5994	-	-	-
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0.4440	6190	0.6665	-	-	-
0.4447	6200	0.4965	-	-	-
0.4454	6210	0.5973	-	-	-
0.4462	6220	0.5608	-	-	-
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0.4483	6250	0.5044	-	-	-
0.4490	6260	0.6034	-	-	-
0.4498	6270	0.6141	-	-	-
0.4505	6280	0.5858	-	-	-
0.4512	6290	0.5974	-	-	-
0.4519	6300	0.6554	-	-	-
0.4526	6310	0.5202	-	-	-
0.4533	6320	0.5724	-	-	-
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0.4555	6350	0.5591	-	-	-
0.4562	6360	0.6472	-	-	-
0.4569	6370	0.6088	-	-	-
0.4576	6380	0.6208	-	-	-
0.4584	6390	0.6376	-	-	-
0.4591	6400	0.572	-	-	-
0.4598	6410	0.5349	-	-	-
0.4605	6420	0.5964	-	-	-
0.4612	6430	0.5505	-	-	-
0.4619	6440	0.6732	-	-	-
0.4627	6450	0.6193	-	-	-
0.4634	6460	0.5998	-	-	-
0.4641	6470	0.6722	-	-	-
0.4648	6480	0.562	-	-	-
0.4655	6490	0.6163	-	-	-
0.4663	6500	0.6194	-	-	-
0.4670	6510	0.6307	-	-	-
0.4677	6520	0.6048	-	-	-
0.4684	6530	0.6088	-	-	-
0.4687	6534	-	0.8857	0.8003	-
0.4691	6540	0.5555	-	-	-
0.4698	6550	0.5695	-	-	-
0.4706	6560	0.5297	-	-	-
0.4713	6570	0.5887	-	-	-
0.4720	6580	0.5488	-	-	-
0.4727	6590	0.5199	-	-	-
0.4734	6600	0.6172	-	-	-
0.4741	6610	0.6275	-	-	-
0.4749	6620	0.552	-	-	-
0.4756	6630	0.5625	-	-	-
0.4763	6640	0.6341	-	-	-
0.4770	6650	0.624	-	-	-
0.4777	6660	0.5949	-	-	-
0.4784	6670	0.592	-	-	-
0.4792	6680	0.63	-	-	-
0.4799	6690	0.539	-	-	-
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0.7747	10800	0.4637	-	-	-
0.7754	10810	0.3917	-	-	-
0.7761	10820	0.3441	-	-	-
0.7768	10830	0.4249	-	-	-
0.7776	10840	0.4442	-	-	-
0.7783	10850	0.4463	-	-	-
0.7790	10860	0.3829	-	-	-
0.7797	10870	0.4264	-	-	-
0.7804	10880	0.4512	-	-	-
0.7811	10890	0.3809	0.7688	0.8069	-
0.7819	10900	0.4447	-	-	-
0.7826	10910	0.4561	-	-	-
0.7833	10920	0.4434	-	-	-
0.7840	10930	0.3988	-	-	-
0.7847	10940	0.4622	-	-	-
0.7855	10950	0.4783	-	-	-
0.7862	10960	0.4139	-	-	-
0.7869	10970	0.4589	-	-	-
0.7876	10980	0.4697	-	-	-
0.7883	10990	0.443	-	-	-
0.7890	11000	0.4469	-	-	-
0.7898	11010	0.4428	-	-	-
0.7905	11020	0.4773	-	-	-
0.7912	11030	0.464	-	-	-
0.7919	11040	0.4289	-	-	-
0.7926	11050	0.4404	-	-	-
0.7933	11060	0.4808	-	-	-
0.7941	11070	0.4378	-	-	-
0.7948	11080	0.5612	-	-	-
0.7955	11090	0.4256	-	-	-
0.7962	11100	0.4124	-	-	-
0.7969	11110	0.3992	-	-	-
0.7976	11120	0.4104	-	-	-
0.7984	11130	0.5023	-	-	-
0.7991	11140	0.4849	-	-	-
0.7998	11150	0.4924	-	-	-
0.8005	11160	0.4554	-	-	-
0.8012	11170	0.4139	-	-	-
0.8020	11180	0.4228	-	-	-
0.8027	11190	0.4376	-	-	-
0.8034	11200	0.4341	-	-	-
0.8041	11210	0.4174	-	-	-
0.8048	11220	0.4935	-	-	-
0.8055	11230	0.4719	-	-	-
0.8063	11240	0.4347	-	-	-
0.8070	11250	0.4232	-	-	-
0.8077	11260	0.4341	-	-	-
0.8084	11270	0.4756	-	-	-
0.8091	11280	0.4161	-	-	-
0.8098	11290	0.4281	-	-	-
0.8106	11300	0.4067	-	-	-
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0.8120	11320	0.5421	-	-	-
0.8127	11330	0.4303	-	-	-
0.8134	11340	0.458	-	-	-
0.8141	11350	0.4362	-	-	-
0.8149	11360	0.3936	-	-	-
0.8156	11370	0.437	-	-	-
0.8163	11380	0.3638	-	-	-
0.8170	11390	0.4194	-	-	-
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0.8184	11410	0.4101	-	-	-
0.8192	11420	0.458	-	-	-
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0.8213	11450	0.4534	-	-	-
0.8220	11460	0.4688	-	-	-
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0.8249	11500	0.3786	-	-	-
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0.8285	11550	0.4595	-	-	-
0.8292	11560	0.4335	-	-	-
0.8299	11570	0.4069	-	-	-
0.8306	11580	0.45	-	-	-
0.8314	11590	0.49	-	-	-
0.8321	11600	0.4664	-	-	-
0.8328	11610	0.4436	-	-	-
0.8335	11620	0.559	-	-	-
0.8342	11630	0.4535	-	-	-
0.8349	11640	0.4646	-	-	-
0.8357	11650	0.3551	-	-	-
0.8364	11660	0.4389	-	-	-
0.8371	11670	0.4897	-	-	-
0.8378	11680	0.4559	-	-	-
0.8385	11690	0.4397	-	-	-
0.8393	11700	0.3907	-	-	-
0.8400	11710	0.5208	-	-	-
0.8407	11720	0.4463	-	-	-
0.8414	11730	0.4028	-	-	-
0.8421	11740	0.3576	-	-	-
0.8428	11750	0.4087	-	-	-
0.8436	11760	0.3917	-	-	-
0.8443	11770	0.5077	-	-	-
0.8450	11780	0.4082	-	-	-
0.8457	11790	0.474	-	-	-
0.8464	11800	0.4174	-	-	-
0.8471	11810	0.3767	-	-	-
0.8479	11820	0.4821	-	-	-
0.8486	11830	0.4108	-	-	-
0.8493	11840	0.3639	-	-	-
0.8500	11850	0.4432	-	-	-
0.8507	11860	0.3886	-	-	-
0.8514	11870	0.4779	-	-	-
0.8522	11880	0.404	-	-	-
0.8529	11890	0.4651	-	-	-
0.8536	11900	0.4695	-	-	-
0.8543	11910	0.3713	-	-	-
0.8550	11920	0.4525	-	-	-
0.8557	11930	0.4531	-	-	-
0.8565	11940	0.4862	-	-	-
0.8572	11950	0.4772	-	-	-
0.8579	11960	0.424	-	-	-
0.8586	11970	0.3684	-	-	-
0.8593	11980	0.3982	-	-	-
0.8601	11990	0.4637	-	-	-
0.8608	12000	0.4752	-	-	-
0.8615	12010	0.4145	-	-	-
0.8622	12020	0.4236	-	-	-
0.8629	12030	0.4051	-	-	-
0.8636	12040	0.4203	-	-	-
0.8644	12050	0.3692	-	-	-
0.8651	12060	0.394	-	-	-
0.8658	12070	0.4427	-	-	-
0.8665	12080	0.4394	-	-	-
0.8672	12090	0.3222	-	-	-
0.8679	12100	0.3932	-	-	-
0.8687	12110	0.4082	-	-	-
0.8694	12120	0.4096	-	-	-
0.8701	12130	0.3993	-	-	-
0.8708	12140	0.4015	-	-	-
0.8715	12150	0.3619	-	-	-
0.8722	12160	0.4456	-	-	-
0.8730	12170	0.4571	-	-	-
0.8737	12180	0.4556	-	-	-
0.8744	12190	0.4558	-	-	-
0.8751	12200	0.4459	-	-	-
0.8758	12210	0.4515	-	-	-
0.8766	12220	0.4004	-	-	-
0.8773	12230	0.4654	-	-	-
0.8780	12240	0.4023	-	-	-
0.8787	12250	0.4333	-	-	-
0.8794	12260	0.4455	-	-	-
0.8801	12270	0.4145	-	-	-
0.8809	12280	0.3526	-	-	-
0.8816	12290	0.411	-	-	-
0.8823	12300	0.374	-	-	-
0.8830	12310	0.4718	-	-	-
0.8837	12320	0.401	-	-	-
0.8844	12330	0.4038	-	-	-
0.8852	12340	0.4706	-	-	-
0.8859	12350	0.4091	-	-	-
0.8866	12360	0.4187	-	-	-
0.8873	12370	0.4024	-	-	-
0.8880	12380	0.3695	-	-	-
0.8887	12390	0.3591	-	-	-
0.8895	12400	0.3335	-	-	-
0.8902	12410	0.3606	-	-	-
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0.8916	12430	0.3524	-	-	-
0.8923	12440	0.294	-	-	-
0.8930	12450	0.4481	-	-	-
0.8938	12460	0.402	-	-	-
0.8945	12470	0.4377	-	-	-
0.8952	12480	0.3799	-	-	-
0.8959	12490	0.431	-	-	-
0.8966	12500	0.4675	-	-	-
0.8974	12510	0.4373	-	-	-
0.8981	12520	0.314	-	-	-
0.8988	12530	0.4401	-	-	-
0.8995	12540	0.4617	-	-	-
0.9002	12550	0.4755	-	-	-
0.9009	12560	0.4238	-	-	-
0.9017	12570	0.3653	-	-	-
0.9024	12580	0.3693	-	-	-
0.9031	12590	0.4351	-	-	-
0.9038	12600	0.3866	-	-	-
0.9045	12610	0.4536	-	-	-
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0.9060	12630	0.5105	-	-	-
0.9067	12640	0.3794	-	-	-
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0.9088	12670	0.4064	-	-	-
0.9095	12680	0.4395	-	-	-
0.9103	12690	0.3785	-	-	-
0.9110	12700	0.4262	-	-	-
0.9117	12710	0.3822	-	-	-
0.9124	12720	0.3707	-	-	-
0.9131	12730	0.3777	-	-	-
0.9139	12740	0.4349	-	-	-
0.9146	12750	0.3646	-	-	-
0.9153	12760	0.4035	-	-	-
0.9160	12770	0.3438	-	-	-
0.9167	12780	0.337	-	-	-
0.9174	12790	0.3754	-	-	-
0.9182	12800	0.4289	-	-	-
0.9189	12810	0.467	-	-	-
0.9196	12820	0.4256	-	-	-
0.9203	12830	0.3679	-	-	-
0.9210	12840	0.3465	-	-	-
0.9217	12850	0.3643	-	-	-
0.9225	12860	0.354	-	-	-
0.9232	12870	0.4019	-	-	-
0.9239	12880	0.3729	-	-	-
0.9246	12890	0.3746	-	-	-
0.9253	12900	0.4627	-	-	-
0.9260	12910	0.3951	-	-	-
0.9268	12920	0.3693	-	-	-
0.9275	12930	0.4226	-	-	-
0.9282	12940	0.3942	-	-	-
0.9289	12950	0.4397	-	-	-
0.9296	12960	0.4165	-	-	-
0.9303	12970	0.3927	-	-	-
0.9311	12980	0.3793	-	-	-
0.9318	12990	0.4103	-	-	-
0.9325	13000	0.3728	-	-	-
0.9332	13010	0.3846	-	-	-
0.9339	13020	0.4065	-	-	-
0.9347	13030	0.3783	-	-	-
0.9354	13040	0.3783	-	-	-
0.9361	13050	0.4102	-	-	-
0.9368	13060	0.3836	-	-	-
0.9374	13068	-	0.7355	0.8072	-
0.9375	13070	0.3368	-	-	-
0.9382	13080	0.4058	-	-	-
0.9390	13090	0.5002	-	-	-
0.9397	13100	0.3993	-	-	-
0.9404	13110	0.4017	-	-	-
0.9411	13120	0.4148	-	-	-
0.9418	13130	0.3592	-	-	-
0.9425	13140	0.3963	-	-	-
0.9433	13150	0.3987	-	-	-
0.9440	13160	0.419	-	-	-
0.9447	13170	0.4185	-	-	-
0.9454	13180	0.4298	-	-	-
0.9461	13190	0.3671	-	-	-
0.9468	13200	0.3923	-	-	-
0.9476	13210	0.3999	-	-	-
0.9483	13220	0.3333	-	-	-
0.9490	13230	0.4123	-	-	-
0.9497	13240	0.4005	-	-	-
0.9504	13250	0.4017	-	-	-
0.9512	13260	0.3607	-	-	-
0.9519	13270	0.4122	-	-	-
0.9526	13280	0.3681	-	-	-
0.9533	13290	0.3403	-	-	-
0.9540	13300	0.4423	-	-	-
0.9547	13310	0.4315	-	-	-
0.9555	13320	0.4148	-	-	-
0.9562	13330	0.4654	-	-	-
0.9569	13340	0.4161	-	-	-
0.9576	13350	0.3704	-	-	-
0.9583	13360	0.3941	-	-	-
0.9590	13370	0.3545	-	-	-
0.9598	13380	0.3683	-	-	-
0.9605	13390	0.3908	-	-	-
0.9612	13400	0.3988	-	-	-
0.9619	13410	0.4066	-	-	-
0.9626	13420	0.413	-	-	-
0.9633	13430	0.427	-	-	-
0.9641	13440	0.4173	-	-	-
0.9648	13450	0.3884	-	-	-
0.9655	13460	0.3745	-	-	-
0.9662	13470	0.3937	-	-	-
0.9669	13480	0.3334	-	-	-
0.9676	13490	0.3882	-	-	-
0.9684	13500	0.3987	-	-	-
0.9691	13510	0.4433	-	-	-
0.9698	13520	0.4267	-	-	-
0.9705	13530	0.4004	-	-	-
0.9712	13540	0.4384	-	-	-
0.9720	13550	0.4509	-	-	-
0.9727	13560	0.4531	-	-	-
0.9734	13570	0.3745	-	-	-
0.9741	13580	0.4051	-	-	-
0.9748	13590	0.3827	-	-	-
0.9755	13600	0.3747	-	-	-
0.9763	13610	0.3756	-	-	-
0.9770	13620	0.3712	-	-	-
0.9777	13630	0.4406	-	-	-
0.9784	13640	0.3874	-	-	-
0.9791	13650	0.3802	-	-	-
0.9798	13660	0.3969	-	-	-
0.9806	13670	0.418	-	-	-
0.9813	13680	0.4128	-	-	-
0.9820	13690	0.4676	-	-	-
0.9827	13700	0.3834	-	-	-
0.9834	13710	0.3687	-	-	-
0.9841	13720	0.4016	-	-	-
0.9849	13730	0.4197	-	-	-
0.9856	13740	0.3992	-	-	-
0.9863	13750	0.4337	-	-	-
0.9870	13760	0.3324	-	-	-
0.9877	13770	0.3119	-	-	-
0.9885	13780	0.3704	-	-	-
0.9892	13790	0.3604	-	-	-
0.9899	13800	0.4234	-	-	-
0.9906	13810	0.4262	-	-	-
0.9913	13820	0.4042	-	-	-
0.9920	13830	0.4085	-	-	-
0.9928	13840	0.4015	-	-	-
0.9935	13850	0.3746	-	-	-
0.9942	13860	0.451	-	-	-
0.9949	13870	0.4334	-	-	-
0.9956	13880	0.4162	-	-	-
0.9963	13890	0.4029	-	-	-
0.9971	13900	0.427	-	-	-
0.9978	13910	0.3278	-	-	-
0.9985	13920	0.4409	-	-	-
0.9992	13930	0.3603	-	-	-
0.9999	13940	0.4769	-	-	-
1.0	13941	-	-	-	0.7611

Framework Versions

Python: 3.11.2
Sentence Transformers: 3.3.1
Transformers: 4.46.1
PyTorch: 2.4.0+cu121
Accelerate: 1.0.1
Datasets: 3.1.0
Tokenizers: 0.20.3

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",
}

GISTEmbedLoss

@misc{solatorio2024gistembed,
    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
    author={Aivin V. Solatorio},
    year={2024},
    eprint={2402.16829},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}