SentenceTransformer based on NeuML/pubmedbert-base-embeddings

This is a sentence-transformers model finetuned from NeuML/pubmedbert-base-embeddings on the generator dataset. It maps sentences & paragraphs to a 768-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: NeuML/pubmedbert-base-embeddings
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- generator

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': 768, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Cre.s-LAL.s-NO2.b',
    'adult PV4b6 neuron',
    'ER3',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Training Details

Training Dataset

generator

Dataset: generator
Size: 20,000 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 3 tokens
mean: 8.2 tokens
max: 28 tokens

min: 3 tokens
mean: 8.4 tokens
max: 28 tokens

min: -1.0
mean: 0.21
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 8.2 tokens max: 28 tokens	min: 3 tokens mean: 8.4 tokens max: 28 tokens	min: -1.0 mean: 0.21 max: 1.0

Samples:

sentence1	sentence2	score
`adult clamp neuron 139`	`adult antler neuron 034`	`2.7755575615628914e-17`
`descending neuron of the anterior dorsal brain DNa07`	`mALT (VA6)`	`-0.5999999999999999`
`M_spPN4t9`	`M_spPN5t10`	`1.0`

Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

generator

Dataset: generator
Size: 1,000 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 3 tokens
mean: 8.07 tokens
max: 25 tokens

min: 3 tokens
mean: 8.22 tokens
max: 27 tokens

min: -1.0
mean: 0.18
max: 1.0
Samples:

sentence1 sentence2 score

adult LHAV3k1 neuron OA-VUMa1 -0.5999999999999999

WED155 adult fruitless aSP-g3 neuron -0.5999999999999999

ring neuron R4 medial VC2 lPN -0.8

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 8.07 tokens max: 25 tokens	min: 3 tokens mean: 8.22 tokens max: 27 tokens	min: -1.0 mean: 0.18 max: 1.0

sentence1	sentence2	score
`adult LHAV3k1 neuron`	`OA-VUMa1`	`-0.5999999999999999`
`WED155`	`adult fruitless aSP-g3 neuron`	`-0.5999999999999999`
`ring neuron R4 medial`	`VC2 lPN`	`-0.8`

Loss: CoSENTLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 512
learning_rate: 0.0002
weight_decay: 0.01
num_train_epochs: 1000
warmup_steps: 1000
log_level: error
log_level_replica: error
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 512
per_device_eval_batch_size: 8
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: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1000
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 1000
log_level: error
log_level_replica: error
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: False
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss
0.5	10	19.9109	-
1.0	20	17.4637	7.0120
1.5	30	14.7406	-
2.0	40	12.7189	3.9087
2.5	50	12.405	-
3.0	60	12.1349	3.5526
3.5	70	12.1681	-
4.0	80	11.9587	3.3929
4.5	90	12.0186	-
5.0	100	11.8004	3.1647
5.5	110	11.785	-
6.0	120	11.5547	2.8057
6.5	130	11.4569	-
7.0	140	11.2584	2.5086
7.5	150	11.2327	-
8.0	160	11.0609	2.2617
8.5	170	11.0131	-
9.0	180	10.8974	2.0437
9.5	190	10.8688	-
10.0	200	10.7265	1.9054
10.5	210	10.7545	-
11.0	220	10.6935	1.8620
11.5	230	10.6229	-
12.0	240	10.5123	1.6515
12.5	250	10.5076	-
13.0	260	10.3677	1.5777
13.5	270	10.3758	-
14.0	280	10.304	1.4596
14.5	290	10.2419	-
15.0	300	10.2104	1.3166
15.5	310	10.1622	-
16.0	320	9.961	1.1535
16.5	330	9.9302	-
17.0	340	9.9979	1.2378
17.5	350	9.9087	-
18.0	360	9.7374	1.0605
18.5	370	9.8141	-
19.0	380	9.6207	0.9017
19.5	390	9.5798	-
20.0	400	9.6272	0.9642
20.5	410	9.5769	-
21.0	420	9.5308	0.8385
21.5	430	9.4968	-
22.0	440	9.3939	0.7927
22.5	450	9.4592	-
23.0	460	9.3345	0.8245
23.5	470	11.6999	-
24.0	480	10.3611	1.0454
24.5	490	9.6768	-
25.0	500	9.5426	0.8532
25.5	510	9.3401	-
26.0	520	9.277	0.8440
26.5	530	9.4937	-
27.0	540	9.3295	0.8681
27.5	550	9.6017	-
28.0	560	11.6154	2.0569
28.5	570	10.1968	-
29.0	580	9.3231	0.9162
29.5	590	9.4204	-
30.0	600	9.1739	0.7456
30.5	610	9.1929	-
31.0	620	9.3143	0.7656
31.5	630	9.3395	-
32.0	640	9.1229	0.9317
32.5	650	9.573	-
33.0	660	9.0695	0.7822
33.5	670	8.802	-
34.0	680	8.8818	0.6566
34.5	690	9.151	-
35.0	700	9.048	0.7099
35.5	710	8.8638	-
36.0	720	8.7823	0.6307
36.5	730	8.7942	-
37.0	740	8.7314	0.7347
37.5	750	8.8099	-
38.0	760	8.7011	0.6218
38.5	770	8.6497	-
39.0	780	8.7962	0.6777
39.5	790	8.6928	-
40.0	800	8.8479	0.6393
40.5	810	8.6913	-
41.0	820	8.7366	0.6592
41.5	830	8.6731	-
42.0	840	8.6979	0.5460
42.5	850	8.7302	-
43.0	860	8.7753	0.5943
43.5	870	8.6553	-
44.0	880	8.5671	0.6461
44.5	890	8.6556	-
45.0	900	8.5739	0.6140
45.5	910	8.5112	-
46.0	920	8.524	0.6342
46.5	930	8.634	-
47.0	940	8.4882	0.5895
47.5	950	8.6335	-
48.0	960	8.4624	0.5184
48.5	970	8.6237	-
49.0	980	8.8367	0.5342
49.5	990	8.5192	-
50.0	1000	8.5021	0.5561
50.5	1010	8.4785	-
51.0	1020	8.3295	0.5013
51.5	1030	8.4542	-
52.0	1040	9.1433	0.6288
52.5	1050	8.6808	-
53.0	1060	8.596	0.5271
53.5	1070	8.439	-
54.0	1080	8.2933	0.5352
54.5	1090	8.4107	-
55.0	1100	8.2689	0.4544
55.5	1110	8.5022	-
56.0	1120	10.4796	0.7063
56.5	1130	8.8195	-
57.0	1140	8.6344	0.5321
57.5	1150	8.5043	-
58.0	1160	8.2681	0.4399
58.5	1170	8.1574	-
59.0	1180	8.2343	0.5245
59.5	1190	8.4271	-
60.0	1200	8.1909	0.4833
60.5	1210	8.1747	-
61.0	1220	8.046	0.4364
61.5	1230	9.2156	-
62.0	1240	8.0486	0.3970
62.5	1250	8.115	-
63.0	1260	8.1234	0.4158
63.5	1270	8.0361	-
64.0	1280	7.9846	0.4087
64.5	1290	8.0144	-
65.0	1300	7.9437	0.4088
65.5	1310	9.3975	-
66.0	1320	8.0125	0.4050
66.5	1330	8.0092	-
67.0	1340	8.0379	0.4736
67.5	1350	8.0598	-
68.0	1360	7.9807	0.3962
68.5	1370	8.1957	-
69.0	1380	7.8546	0.4044
69.5	1390	7.9461	-
70.0	1400	7.9233	0.4166
70.5	1410	7.9779	-
71.0	1420	7.8192	0.3930
71.5	1430	7.9091	-
72.0	1440	7.8167	0.4077
72.5	1450	7.9041	-
73.0	1460	7.7646	0.3514
73.5	1470	7.848	-
74.0	1480	7.7369	0.3591
74.5	1490	8.2404	-
75.0	1500	7.7233	0.3435
75.5	1510	7.7716	-
76.0	1520	7.7292	0.3469
76.5	1530	7.9558	-
77.0	1540	7.6599	0.3352
77.5	1550	7.7658	-
78.0	1560	7.6396	0.3181
78.5	1570	7.7419	-
79.0	1580	7.6574	0.3223
79.5	1590	7.719	-
80.0	1600	7.7039	0.3405
80.5	1610	7.7925	-
81.0	1620	7.5967	0.3156
81.5	1630	7.7416	-
82.0	1640	7.5751	0.3381
82.5	1650	7.6652	-
83.0	1660	7.7724	0.3296
83.5	1670	7.8028	-
84.0	1680	7.5706	0.3234
84.5	1690	7.6578	-
85.0	1700	7.4778	0.3102
85.5	1710	7.5892	-
86.0	1720	7.5219	0.3082
86.5	1730	7.6192	-
87.0	1740	7.4826	0.2976
87.5	1750	7.59	-
88.0	1760	7.5426	0.3277
88.5	1770	7.6051	-
89.0	1780	7.5011	0.3018
89.5	1790	7.5256	-
90.0	1800	7.4843	0.2801
90.5	1810	8.2916	-
91.0	1820	7.5131	0.2787
91.5	1830	7.5412	-
92.0	1840	7.4474	0.2869
92.5	1850	7.5188	-
93.0	1860	7.5668	0.3125
93.5	1870	7.6351	-
94.0	1880	7.4227	0.2705
94.5	1890	7.527	-
95.0	1900	7.5069	0.2784
95.5	1910	7.5677	-
96.0	1920	7.3898	0.3111
96.5	1930	7.5176	-
97.0	1940	7.3906	0.2911
97.5	1950	7.486	-
98.0	1960	7.3852	0.2820
98.5	1970	7.4445	-
99.0	1980	7.397	0.2863
99.5	1990	7.4387	-
100.0	2000	7.3404	0.2763
100.5	2010	7.4415	-
101.0	2020	7.3133	0.2585
101.5	2030	7.4206	-
102.0	2040	7.3097	0.2755
102.5	2050	7.4239	-
103.0	2060	7.3216	0.2632
103.5	2070	7.3893	-
104.0	2080	7.3105	0.2475
104.5	2090	7.3758	-
105.0	2100	7.2963	0.2660
105.5	2110	7.3421	-
106.0	2120	7.3074	0.2573
106.5	2130	7.345	-
107.0	2140	7.2885	0.2607
107.5	2150	7.4301	-
108.0	2160	7.3196	0.2817
108.5	2170	7.9997	-
109.0	2180	7.2407	0.2456
109.5	2190	7.3354	-
110.0	2200	7.2453	0.2424
110.5	2210	7.3321	-
111.0	2220	7.2925	0.2641
111.5	2230	7.3301	-
112.0	2240	7.3245	0.2444
112.5	2250	7.339	-
113.0	2260	7.2265	0.2422
113.5	2270	7.2919	-
114.0	2280	7.2328	0.2401
114.5	2290	7.2449	-
115.0	2300	7.2363	0.2330
115.5	2310	7.3134	-
116.0	2320	7.1926	0.2388
116.5	2330	7.3273	-
117.0	2340	7.1976	0.2220
117.5	2350	7.5078	-
118.0	2360	7.2046	0.2310
118.5	2370	7.3248	-
119.0	2380	7.4819	0.2409
119.5	2390	7.2883	-
120.0	2400	7.2372	0.2270
120.5	2410	7.2622	-
121.0	2420	7.2086	0.2474
121.5	2430	7.2437	-
122.0	2440	7.2942	0.2358
122.5	2450	7.3028	-
123.0	2460	7.1942	0.2194
123.5	2470	7.315	-
124.0	2480	7.2456	0.2418
124.5	2490	7.2401	-
125.0	2500	7.1893	0.2260
125.5	2510	7.2411	-
126.0	2520	7.1649	0.2167
126.5	2530	7.2444	-
127.0	2540	7.1287	0.2230
127.5	2550	7.2422	-
128.0	2560	7.246	0.2248
128.5	2570	7.2329	-
129.0	2580	7.3102	0.2295
129.5	2590	7.255	-
130.0	2600	7.1417	0.2366
130.5	2610	7.2328	-
131.0	2620	7.1106	0.2286
131.5	2630	7.1831	-
132.0	2640	7.1466	0.2385
132.5	2650	7.1947	-
133.0	2660	7.2067	0.2316
133.5	2670	7.26	-
134.0	2680	7.1126	0.2173
134.5	2690	7.2109	-
135.0	2700	7.1089	0.2162
135.5	2710	7.1891	-
136.0	2720	7.1266	0.2195
136.5	2730	7.242	-
137.0	2740	7.1126	0.2263
137.5	2750	7.1689	-
138.0	2760	7.1714	0.2216
138.5	2770	7.1858	-
139.0	2780	7.0593	0.2225
139.5	2790	7.188	-
140.0	2800	7.0789	0.2085
140.5	2810	7.1903	-
141.0	2820	7.0341	0.2143
141.5	2830	7.1918	-
142.0	2840	7.0656	0.2200
142.5	2850	7.2065	-
143.0	2860	7.2527	0.2311
143.5	2870	7.2303	-
144.0	2880	7.074	0.2322
144.5	2890	7.18	-
145.0	2900	7.119	0.2383
145.5	2910	7.2381	-
146.0	2920	7.0839	0.2093
146.5	2930	7.1693	-
147.0	2940	7.1101	0.2369
147.5	2950	7.1654	-
148.0	2960	7.0496	0.2148
148.5	2970	7.1968	-
149.0	2980	7.0191	0.2135
149.5	2990	7.1794	-
150.0	3000	7.0124	0.2216
150.5	3010	7.1553	-
151.0	3020	7.0414	0.2020
151.5	3030	7.1327	-
152.0	3040	7.0216	0.2151
152.5	3050	7.1254	-
153.0	3060	7.0144	0.2033
153.5	3070	7.1022	-
154.0	3080	7.0298	0.2037
154.5	3090	7.1334	-
155.0	3100	7.0056	0.2100
155.5	3110	7.0965	-
156.0	3120	7.0699	0.2045
156.5	3130	7.1156	-
157.0	3140	6.9935	0.2085
157.5	3150	7.0845	-
158.0	3160	7.0256	0.2076
158.5	3170	7.1178	-
159.0	3180	7.0032	0.2058
159.5	3190	7.1193	-
160.0	3200	6.957	0.2035
160.5	3210	7.1136	-
161.0	3220	6.969	0.2037
161.5	3230	7.0857	-
162.0	3240	6.9946	0.1987
162.5	3250	7.0948	-
163.0	3260	6.9614	0.2008
163.5	3270	7.0602	-
164.0	3280	6.9612	0.2068
164.5	3290	7.0695	-
165.0	3300	6.9702	0.1915
165.5	3310	7.0311	-
166.0	3320	6.99	0.2022
166.5	3330	7.0896	-
167.0	3340	6.9511	0.1950
167.5	3350	7.0873	-
168.0	3360	6.9398	0.2014
168.5	3370	7.0878	-
169.0	3380	6.9356	0.1872
169.5	3390	7.0618	-
170.0	3400	6.9482	0.2062
170.5	3410	7.0647	-
171.0	3420	6.948	0.1966
171.5	3430	7.0498	-
172.0	3440	6.9648	0.2134
172.5	3450	7.0034	-
173.0	3460	7.0078	0.1867
173.5	3470	7.0408	-
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913.5	18270	6.6516	-
914.0	18280	6.5176	0.1427
914.5	18290	6.634	-
915.0	18300	6.5408	0.1432
915.5	18310	6.6562	-
916.0	18320	6.5153	0.1426
916.5	18330	6.6703	-
917.0	18340	6.4909	0.1420
917.5	18350	6.6384	-
918.0	18360	6.5318	0.1424
918.5	18370	6.6547	-
919.0	18380	6.5159	0.1425
919.5	18390	6.6552	-
920.0	18400	6.5146	0.1432
920.5	18410	6.6266	-
921.0	18420	6.5488	0.1434
921.5	18430	6.6748	-
922.0	18440	6.488	0.1424
922.5	18450	6.6401	-
923.0	18460	6.5877	0.1423
923.5	18470	6.6176	-
924.0	18480	6.5658	0.1419
924.5	18490	6.6554	-
925.0	18500	6.5092	0.1418
925.5	18510	6.6467	-
926.0	18520	6.5175	0.1417
926.5	18530	6.6331	-
927.0	18540	6.5243	0.1429
927.5	18550	6.6506	-
928.0	18560	6.5224	0.1424
928.5	18570	6.629	-
929.0	18580	6.5414	0.1419
929.5	18590	6.64	-
930.0	18600	6.524	0.1416
930.5	18610	6.6545	-
931.0	18620	6.5045	0.1412
931.5	18630	6.6301	-
932.0	18640	6.5334	0.1418
932.5	18650	6.6212	-
933.0	18660	6.5498	0.1421
933.5	18670	6.6474	-
934.0	18680	6.5237	0.1420
934.5	18690	6.6375	-
935.0	18700	6.5411	0.1420
935.5	18710	6.6381	-
936.0	18720	6.5304	0.1421
936.5	18730	6.6373	-
937.0	18740	6.5234	0.1418
937.5	18750	6.6376	-
938.0	18760	6.5256	0.1418
938.5	18770	6.6576	-
939.0	18780	6.5094	0.1421
939.5	18790	6.677	-
940.0	18800	6.4781	0.1425
940.5	18810	6.6221	-
941.0	18820	6.5426	0.1422
941.5	18830	6.6554	-
942.0	18840	6.5103	0.1414
942.5	18850	6.6403	-
943.0	18860	6.5243	0.1419
943.5	18870	6.6798	-
944.0	18880	6.4754	0.1421
944.5	18890	6.628	-
945.0	18900	6.5264	0.1420
945.5	18910	6.6421	-
946.0	18920	6.5289	0.1423
946.5	18930	6.655	-
947.0	18940	6.499	0.1423
947.5	18950	6.5927	-
948.0	18960	6.5678	0.1417
948.5	18970	6.6396	-
949.0	18980	6.521	0.1416
949.5	18990	6.6451	-
950.0	19000	6.5195	0.1417
950.5	19010	6.6447	-
951.0	19020	6.5005	0.1419
951.5	19030	6.6581	-
952.0	19040	6.4967	0.1422
952.5	19050	6.6417	-
953.0	19060	6.5209	0.1421
953.5	19070	6.6699	-
954.0	19080	6.4906	0.1419
954.5	19090	6.6265	-
955.0	19100	6.536	0.1416
955.5	19110	6.6597	-
956.0	19120	6.5016	0.1416
956.5	19130	6.6171	-
957.0	19140	6.5512	0.1421
957.5	19150	6.6283	-
958.0	19160	6.5425	0.1424
958.5	19170	6.6215	-
959.0	19180	6.5428	0.1423
959.5	19190	6.6224	-
960.0	19200	6.5303	0.1419
960.5	19210	6.6533	-
961.0	19220	6.5168	0.1418
961.5	19230	6.6242	-
962.0	19240	6.5355	0.1419
962.5	19250	6.6604	-
963.0	19260	6.5008	0.1422
963.5	19270	6.6302	-
964.0	19280	6.5365	0.1421
964.5	19290	6.6376	-
965.0	19300	6.5251	0.1422
965.5	19310	6.6578	-
966.0	19320	6.5047	0.1423
966.5	19330	6.6523	-
967.0	19340	6.5042	0.1421
967.5	19350	6.651	-
968.0	19360	6.5131	0.1421
968.5	19370	6.6427	-
969.0	19380	6.5068	0.1420
969.5	19390	6.6183	-
970.0	19400	6.5354	0.1422
970.5	19410	6.6318	-
971.0	19420	6.5234	0.1418
971.5	19430	6.6334	-
972.0	19440	6.5221	0.1419
972.5	19450	6.6977	-
973.0	19460	6.4547	0.1418
973.5	19470	6.6555	-
974.0	19480	6.5042	0.1417
974.5	19490	6.6425	-
975.0	19500	6.5108	0.1417
975.5	19510	6.6746	-
976.0	19520	6.4847	0.1416
976.5	19530	6.6338	-
977.0	19540	6.5262	0.1418
977.5	19550	6.6403	-
978.0	19560	6.5154	0.1419
978.5	19570	6.6241	-
979.0	19580	6.5296	0.1419
979.5	19590	6.6129	-
980.0	19600	6.5483	0.1419
980.5	19610	6.6524	-
981.0	19620	6.5031	0.1418
981.5	19630	6.671	-
982.0	19640	6.4815	0.1420
982.5	19650	6.6277	-
983.0	19660	6.5349	0.1419
983.5	19670	6.6249	-
984.0	19680	6.5264	0.1420
984.5	19690	6.6389	-
985.0	19700	6.5089	0.1419
985.5	19710	6.6438	-
986.0	19720	6.5033	0.1419
986.5	19730	6.6322	-
987.0	19740	6.5293	0.1419
987.5	19750	6.6392	-
988.0	19760	6.5203	0.1418
988.5	19770	6.6184	-
989.0	19780	6.5453	0.1418
989.5	19790	6.6453	-
990.0	19800	6.5069	0.1420
990.5	19810	6.6452	-
991.0	19820	6.513	0.1420
991.5	19830	6.6694	-
992.0	19840	6.4836	0.1420
992.5	19850	6.625	-
993.0	19860	6.5284	0.1419
993.5	19870	6.6546	-
994.0	19880	6.502	0.1419
994.5	19890	6.628	-
995.0	19900	6.5196	0.1419
995.5	19910	6.6541	-
996.0	19920	6.497	0.1419
996.5	19930	6.6336	-
997.0	19940	6.5223	0.1419
997.5	19950	6.6349	-
998.0	19960	6.5133	0.1420
998.5	19970	6.6675	-
999.0	19980	6.482	0.1420
999.5	19990	6.6569	-
1000.0	20000	6.491	0.1419

Framework Versions

Python: 3.10.15
Sentence Transformers: 3.2.1
Transformers: 4.45.2
PyTorch: 2.4.0+cu121
Accelerate: 1.1.1
Datasets: 3.0.1
Tokenizers: 0.20.1

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}