This is a sentence-transformers model finetuned from sentence-transformers/all-distilroberta-v1. 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: sentence-transformers/all-distilroberta-v1
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
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: RobertaModel 
  (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})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("hanwenzhu/all-distilroberta-v1-lr2e-4-bs1024-nneg3-ml-feb22")
# Run inference
sentences = [
    'Mathlib.Analysis.Convex.StoneSeparation#0',
    'Nat.le_of_lt',
    'AddCommMonoid.nat_isScalarTower',
]
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

Unnamed Dataset

Size: 5,702,228 training samples
Columns: state_name and premise_name
Approximate statistics based on the first 1000 samples:
state_name premise_name
type string string
details
min: 12 tokens
mean: 17.86 tokens
max: 23 tokens
min: 3 tokens
mean: 10.93 tokens
max: 36 tokens

Samples:

state_name	premise_name
`Mathlib.Topology.EMetricSpace.BoundedVariation#253`	`Set.union_empty`
`Mathlib.Topology.EMetricSpace.BoundedVariation#253`	`le_refl`
`Mathlib.Topology.EMetricSpace.BoundedVariation#253`	`le_of_le_of_eq`

Loss: loss.MaskedCachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

Unnamed Dataset

Size: 2,334 evaluation samples
Columns: state_name and premise_name
Approximate statistics based on the first 1000 samples:
state_name premise_name
type string string
details
min: 11 tokens
mean: 16.59 tokens
max: 24 tokens
min: 3 tokens
mean: 11.75 tokens
max: 32 tokens
Samples:
state_name premise_name
Mathlib.Algebra.Algebra.Operations#96 Submodule.le_pow_toAddSubmonoid
Mathlib.Algebra.Algebra.Operations#96 AddSubmonoid.pow_subset_pow
Mathlib.Algebra.Algebra.Operations#96 trans
Loss: loss.MaskedCachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 1024
per_device_eval_batch_size: 64
learning_rate: 0.0002
num_train_epochs: 1.0
lr_scheduler_type: cosine
warmup_ratio: 0.03
bf16: True
dataloader_num_workers: 4
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: 1024
per_device_eval_batch_size: 64
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: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1.0
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.03
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: 4
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
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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	loss
0.0018	10	6.4945	-
0.0036	20	5.9953	-
0.0054	30	5.6726	-
0.0072	40	5.489	-
0.0090	50	5.3066	-
0.0101	56	-	1.4491
0.0108	60	5.1176	-
0.0126	70	5.0673	-
0.0144	80	5.0028	-
0.0162	90	4.957	-
0.0180	100	4.8704	-
0.0198	110	4.8362	-
0.0201	112	-	1.2623
0.0215	120	4.7757	-
0.0233	130	4.6646	-
0.0251	140	4.6617	-
0.0269	150	4.6957	-
0.0287	160	4.5359	-
0.0302	168	-	1.2172
0.0305	170	4.5352	-
0.0323	180	4.4969	-
0.0341	190	4.484	-
0.0359	200	4.4936	-
0.0377	210	4.3855	-
0.0395	220	4.3338	-
0.0402	224	-	1.2096
0.0413	230	4.3023	-
0.0431	240	4.3158	-
0.0449	250	4.291	-
0.0467	260	4.2303	-
0.0485	270	4.2196	-
0.0503	280	4.237	1.1234
0.0521	290	4.2183	-
0.0539	300	4.1804	-
0.0557	310	4.1496	-
0.0575	320	4.1086	-
0.0593	330	4.0588	-
0.0603	336	-	0.9823
0.0611	340	4.0566	-
0.0628	350	4.0886	-
0.0646	360	4.126	-
0.0664	370	3.9956	-
0.0682	380	4.0245	-
0.0700	390	4.0398	-
0.0704	392	-	0.9728
0.0718	400	3.9756	-
0.0736	410	4.0221	-
0.0754	420	3.977	-
0.0772	430	3.8922	-
0.0790	440	3.9496	-
0.0804	448	-	0.9045
0.0808	450	3.8841	-
0.0826	460	3.8596	-
0.0844	470	3.8682	-
0.0862	480	3.8671	-
0.0880	490	3.829	-
0.0898	500	3.7833	-
0.0905	504	-	0.8283
0.0916	510	3.7498	-
0.0934	520	3.8393	-
0.0952	530	3.7889	-
0.0970	540	3.798	-
0.0988	550	3.7653	-
0.1006	560	3.7703	0.8599
0.1024	570	3.7121	-
0.1041	580	3.7443	-
0.1059	590	3.7417	-
0.1077	600	3.6768	-
0.1095	610	3.6305	-
0.1106	616	-	0.8878
0.1113	620	3.6516	-
0.1131	630	3.6454	-
0.1149	640	3.6808	-
0.1167	650	3.662	-
0.1185	660	3.6466	-
0.1203	670	3.5734	-
0.1207	672	-	0.8875
0.1221	680	3.5605	-
0.1239	690	3.6263	-
0.1257	700	3.6224	-
0.1275	710	3.5387	-
0.1293	720	3.5355	-
0.1307	728	-	0.8349
0.1311	730	3.5723	-
0.1329	740	3.5018	-
0.1347	750	3.4456	-
0.1365	760	3.4415	-
0.1383	770	3.4535	-
0.1401	780	3.4423	-
0.1408	784	-	0.8113
0.1419	790	3.5116	-
0.1437	800	3.4681	-
0.1454	810	3.4181	-
0.1472	820	3.4289	-
0.1490	830	3.4553	-
0.1508	840	3.4506	0.8186
0.1526	850	3.4006	-
0.1544	860	3.4412	-
0.1562	870	3.3971	-
0.1580	880	3.3829	-
0.1598	890	3.4066	-
0.1609	896	-	0.7989
0.1616	900	3.4174	-
0.1634	910	3.3869	-
0.1652	920	3.3616	-
0.1670	930	3.3639	-
0.1688	940	3.3353	-
0.1706	950	3.3401	-
0.1709	952	-	0.7703
0.1724	960	3.3322	-
0.1742	970	3.3129	-
0.1760	980	3.3336	-
0.1778	990	3.2899	-
0.1796	1000	3.3012	-
0.1810	1008	-	0.7533
0.1814	1010	3.2885	-
0.1832	1020	3.2861	-
0.1850	1030	3.2935	-
0.1867	1040	3.3401	-
0.1885	1050	3.3192	-
0.1903	1060	3.306	-
0.1911	1064	-	0.7385
0.1921	1070	3.2599	-
0.1939	1080	3.1642	-
0.1957	1090	3.2544	-
0.1975	1100	3.1976	-
0.1993	1110	3.1664	-
0.2011	1120	3.1119	0.7099
0.2029	1130	3.1349	-
0.2047	1140	3.2138	-
0.2065	1150	3.2007	-
0.2083	1160	3.1433	-
0.2101	1170	3.1061	-
0.2112	1176	-	0.7260
0.2119	1180	3.1275	-
0.2137	1190	3.1019	-
0.2155	1200	3.1205	-
0.2173	1210	3.0568	-
0.2191	1220	3.1019	-
0.2209	1230	3.1172	-
0.2212	1232	-	0.7232
0.2227	1240	3.0902	-
0.2245	1250	3.0309	-
0.2263	1260	3.0369	-
0.2280	1270	3.0152	-
0.2298	1280	3.0631	-
0.2313	1288	-	0.6834
0.2316	1290	3.0995	-
0.2334	1300	3.0935	-
0.2352	1310	3.0539	-
0.2370	1320	3.0385	-
0.2388	1330	3.0614	-
0.2406	1340	3.0869	-
0.2413	1344	-	0.7055
0.2424	1350	3.0854	-
0.2442	1360	3.0363	-
0.2460	1370	3.0643	-
0.2478	1380	3.0698	-
0.2496	1390	3.0005	-
0.2514	1400	2.9856	0.6682
0.2532	1410	3.0242	-
0.2550	1420	3.0012	-
0.2568	1430	3.0131	-
0.2586	1440	3.0069	-
0.2604	1450	2.9781	-
0.2614	1456	-	0.6871
0.2622	1460	2.9552	-
0.2640	1470	2.9734	-
0.2658	1480	2.9974	-
0.2676	1490	2.9739	-
0.2693	1500	2.9154	-
0.2711	1510	2.9461	-
0.2715	1512	-	0.6957
0.2729	1520	2.8891	-
0.2747	1530	2.9345	-
0.2765	1540	2.9421	-
0.2783	1550	2.9024	-
0.2801	1560	2.9436	-
0.2816	1568	-	0.6855
0.2819	1570	2.9584	-
0.2837	1580	2.9022	-
0.2855	1590	2.8767	-
0.2873	1600	2.9197	-
0.2891	1610	2.8995	-
0.2909	1620	2.8613	-
0.2916	1624	-	0.6869
0.2927	1630	2.8522	-
0.2945	1640	2.8988	-
0.2963	1650	2.8307	-
0.2981	1660	2.8281	-
0.2999	1670	2.835	-
0.3017	1680	2.8305	0.6352
0.3035	1690	2.8139	-
0.3053	1700	2.8655	-
0.3071	1710	2.8651	-
0.3089	1720	2.8026	-
0.3106	1730	2.7712	-
0.3117	1736	-	0.6213
0.3124	1740	2.8073	-
0.3142	1750	2.7572	-
0.3160	1760	2.7446	-
0.3178	1770	2.7955	-
0.3196	1780	2.7745	-
0.3214	1790	2.7254	-
0.3218	1792	-	0.6358
0.3232	1800	2.7719	-
0.3250	1810	2.7386	-
0.3268	1820	2.705	-
0.3286	1830	2.7102	-
0.3304	1840	2.7694	-
0.3318	1848	-	0.6394
0.3322	1850	2.7433	-
0.3340	1860	2.6986	-
0.3358	1870	2.7005	-
0.3376	1880	2.6814	-
0.3394	1890	2.6811	-
0.3412	1900	2.7303	-
0.3419	1904	-	0.6303
0.3430	1910	2.7674	-
0.3448	1920	2.7573	-
0.3466	1930	2.7488	-
0.3484	1940	2.7408	-
0.3502	1950	2.6989	-
0.3519	1960	2.7066	0.6180
0.3537	1970	2.707	-
0.3555	1980	2.6932	-
0.3573	1990	2.7165	-
0.3591	2000	2.6938	-
0.3609	2010	2.7207	-
0.3620	2016	-	0.5906
0.3627	2020	2.7456	-
0.3645	2030	2.714	-
0.3663	2040	2.6607	-
0.3681	2050	2.6659	-
0.3699	2060	2.6621	-
0.3717	2070	2.6872	-
0.3721	2072	-	0.5879
0.3735	2080	2.6439	-
0.3753	2090	2.6849	-
0.3771	2100	2.6518	-
0.3789	2110	2.5955	-
0.3807	2120	2.6138	-
0.3821	2128	-	0.5945
0.3825	2130	2.5803	-
0.3843	2140	2.6437	-
0.3861	2150	2.6264	-
0.3879	2160	2.5644	-
0.3897	2170	2.5971	-
0.3915	2180	2.52	-
0.3922	2184	-	0.5953
0.3932	2190	2.5523	-
0.3950	2200	2.599	-
0.3968	2210	2.5832	-
0.3986	2220	2.6254	-
0.4004	2230	2.5838	-
0.4022	2240	2.5737	0.5751
0.4040	2250	2.5663	-
0.4058	2260	2.6058	-
0.4076	2270	2.5968	-
0.4094	2280	2.5784	-
0.4112	2290	2.5363	-
0.4123	2296	-	0.5810
0.4130	2300	2.5149	-
0.4148	2310	2.558	-
0.4166	2320	2.5614	-
0.4184	2330	2.5482	-
0.4202	2340	2.5458	-
0.4220	2350	2.5281	-
0.4223	2352	-	0.5673
0.4238	2360	2.5617	-
0.4256	2370	2.5337	-
0.4274	2380	2.5321	-
0.4292	2390	2.5506	-
0.4310	2400	2.5214	-
0.4324	2408	-	0.5650
0.4328	2410	2.5245	-
0.4345	2420	2.5047	-
0.4363	2430	2.5719	-
0.4381	2440	2.512	-
0.4399	2450	2.5076	-
0.4417	2460	2.4517	-
0.4424	2464	-	0.5772
0.4435	2470	2.4911	-
0.4453	2480	2.5638	-
0.4471	2490	2.5349	-
0.4489	2500	2.4961	-
0.4507	2510	2.5169	-
0.4525	2520	2.489	0.5655
0.4543	2530	2.475	-
0.4561	2540	2.4378	-
0.4579	2550	2.4252	-
0.4597	2560	2.4448	-
0.4615	2570	2.4596	-
0.4626	2576	-	0.5544
0.4633	2580	2.4811	-
0.4651	2590	2.4459	-
0.4669	2600	2.4261	-
0.4687	2610	2.4214	-
0.4705	2620	2.4528	-
0.4723	2630	2.4374	-
0.4726	2632	-	0.5336
0.4741	2640	2.4585	-
0.4758	2650	2.4529	-
0.4776	2660	2.4205	-
0.4794	2670	2.441	-
0.4812	2680	2.4654	-
0.4827	2688	-	0.5314
0.4830	2690	2.4535	-
0.4848	2700	2.5085	-
0.4866	2710	2.4725	-
0.4884	2720	2.4655	-
0.4902	2730	2.4137	-
0.4920	2740	2.4172	-
0.4927	2744	-	0.5352
0.4938	2750	2.434	-
0.4956	2760	2.4489	-
0.4974	2770	2.4448	-
0.4992	2780	2.3979	-
0.5010	2790	2.4251	-
0.5028	2800	2.3996	0.5313
0.5046	2810	2.4467	-
0.5064	2820	2.4338	-
0.5082	2830	2.4386	-
0.5100	2840	2.3813	-
0.5118	2850	2.4149	-
0.5128	2856	-	0.5261
0.5136	2860	2.3822	-
0.5154	2870	2.407	-
0.5171	2880	2.3406	-
0.5189	2890	2.3845	-
0.5207	2900	2.3176	-
0.5225	2910	2.3554	-
0.5229	2912	-	0.5172
0.5243	2920	2.3905	-
0.5261	2930	2.3994	-
0.5279	2940	2.4004	-
0.5297	2950	2.3499	-
0.5315	2960	2.3758	-
0.5330	2968	-	0.5340
0.5333	2970	2.3644	-
0.5351	2980	2.3288	-
0.5369	2990	2.3504	-
0.5387	3000	2.2991	-
0.5405	3010	2.3471	-
0.5423	3020	2.3408	-
0.5430	3024	-	0.5077
0.5441	3030	2.3881	-
0.5459	3040	2.3398	-
0.5477	3050	2.2963	-
0.5495	3060	2.3344	-
0.5513	3070	2.3268	-
0.5531	3080	2.3197	0.5025
0.5549	3090	2.3667	-
0.5567	3100	2.3655	-
0.5584	3110	2.3295	-
0.5602	3120	2.3238	-
0.5620	3130	2.3336	-
0.5631	3136	-	0.4885
0.5638	3140	2.3408	-
0.5656	3150	2.3371	-
0.5674	3160	2.3419	-
0.5692	3170	2.2884	-
0.5710	3180	2.2972	-
0.5728	3190	2.2571	-
0.5732	3192	-	0.4772
0.5746	3200	2.2741	-
0.5764	3210	2.3012	-
0.5782	3220	2.3374	-
0.5800	3230	2.2804	-
0.5818	3240	2.2674	-
0.5832	3248	-	0.5104
0.5836	3250	2.277	-
0.5854	3260	2.288	-
0.5872	3270	2.2677	-
0.5890	3280	2.2935	-
0.5908	3290	2.2697	-
0.5926	3300	2.2595	-
0.5933	3304	-	0.4893
0.5944	3310	2.2754	-
0.5962	3320	2.2544	-
0.5980	3330	2.2816	-
0.5997	3340	2.2192	-
0.6015	3350	2.2841	-
0.6033	3360	2.2807	0.4862
0.6051	3370	2.2228	-
0.6069	3380	2.2437	-
0.6087	3390	2.2494	-
0.6105	3400	2.2715	-
0.6123	3410	2.2578	-
0.6134	3416	-	0.4820
0.6141	3420	2.2393	-
0.6159	3430	2.272	-
0.6177	3440	2.24	-
0.6195	3450	2.2612	-
0.6213	3460	2.2369	-
0.6231	3470	2.251	-
0.6235	3472	-	0.4637
0.6249	3480	2.1808	-
0.6267	3490	2.2178	-
0.6285	3500	2.2261	-
0.6303	3510	2.1946	-
0.6321	3520	2.167	-
0.6335	3528	-	0.4657
0.6339	3530	2.1794	-
0.6357	3540	2.1646	-
0.6375	3550	2.2539	-
0.6393	3560	2.2163	-
0.6410	3570	2.2402	-
0.6428	3580	2.1637	-
0.6436	3584	-	0.4676
0.6446	3590	2.1718	-
0.6464	3600	2.1778	-
0.6482	3610	2.2156	-
0.6500	3620	2.2267	-
0.6518	3630	2.2506	-
0.6536	3640	2.1913	0.4698
0.6554	3650	2.2207	-
0.6572	3660	2.1914	-
0.6590	3670	2.2358	-
0.6608	3680	2.213	-
0.6626	3690	2.2178	-
0.6637	3696	-	0.4671
0.6644	3700	2.2003	-
0.6662	3710	2.1846	-
0.6680	3720	2.2418	-
0.6698	3730	2.1752	-
0.6716	3740	2.2026	-
0.6734	3750	2.2094	-
0.6737	3752	-	0.4506
0.6752	3760	2.198	-
0.6770	3770	2.1714	-
0.6788	3780	2.2162	-
0.6806	3790	2.1964	-
0.6823	3800	2.1827	-
0.6838	3808	-	0.4698
0.6841	3810	2.1884	-
0.6859	3820	2.1562	-
0.6877	3830	2.1502	-
0.6895	3840	2.1936	-
0.6913	3850	2.1785	-
0.6931	3860	2.1587	-
0.6938	3864	-	0.4495
0.6949	3870	2.196	-
0.6967	3880	2.1883	-
0.6985	3890	2.1452	-
0.7003	3900	2.1749	-
0.7021	3910	2.219	-
0.7039	3920	2.1916	0.4399
0.7057	3930	2.1197	-
0.7075	3940	2.1504	-
0.7093	3950	2.1144	-
0.7111	3960	2.1299	-
0.7129	3970	2.1704	-
0.7140	3976	-	0.4442
0.7147	3980	2.1874	-
0.7165	3990	2.1853	-
0.7183	4000	2.1954	-
0.7201	4010	2.1971	-
0.7219	4020	2.1675	-
0.7236	4030	2.1777	-
0.7240	4032	-	0.4404
0.7254	4040	2.1521	-
0.7272	4050	2.1615	-
0.7290	4060	2.1736	-
0.7308	4070	2.1394	-
0.7326	4080	2.1352	-
0.7341	4088	-	0.4352
0.7344	4090	2.1618	-
0.7362	4100	2.1351	-
0.7380	4110	2.1216	-
0.7398	4120	2.0994	-
0.7416	4130	2.1209	-
0.7434	4140	2.1436	-
0.7441	4144	-	0.4337
0.7452	4150	2.1139	-
0.7470	4160	2.119	-
0.7488	4170	2.1159	-
0.7506	4180	2.1019	-
0.7524	4190	2.1614	-
0.7542	4200	2.1301	0.4413
0.7560	4210	2.1316	-
0.7578	4220	2.1273	-
0.7596	4230	2.0352	-
0.7614	4240	2.0996	-
0.7632	4250	2.1295	-
0.7642	4256	-	0.4348
0.7649	4260	2.0968	-
0.7667	4270	2.0778	-
0.7685	4280	2.1248	-
0.7703	4290	2.0838	-
0.7721	4300	2.0912	-
0.7739	4310	2.0775	-
0.7743	4312	-	0.4441
0.7757	4320	2.1257	-
0.7775	4330	2.1134	-
0.7793	4340	2.0975	-
0.7811	4350	2.1004	-
0.7829	4360	2.1172	-
0.7843	4368	-	0.4406
0.7847	4370	2.0906	-
0.7865	4380	2.0822	-
0.7883	4390	2.0881	-
0.7901	4400	2.1305	-
0.7919	4410	2.1207	-
0.7937	4420	2.0894	-
0.7944	4424	-	0.4353
0.7955	4430	2.1046	-
0.7973	4440	2.1255	-
0.7991	4450	2.1023	-
0.8009	4460	2.0824	-
0.8027	4470	2.0778	-
0.8045	4480	2.1155	0.4315
0.8062	4490	2.0992	-
0.8080	4500	2.0829	-
0.8098	4510	2.1144	-
0.8116	4520	2.0977	-
0.8134	4530	2.1148	-
0.8145	4536	-	0.4289
0.8152	4540	2.1267	-
0.8170	4550	2.106	-
0.8188	4560	2.0573	-
0.8206	4570	2.0376	-
0.8224	4580	2.1084	-
0.8242	4590	2.0774	-
0.8246	4592	-	0.4270
0.8260	4600	2.1035	-
0.8278	4610	2.1295	-
0.8296	4620	2.1035	-
0.8314	4630	2.118	-
0.8332	4640	2.0951	-
0.8346	4648	-	0.4235
0.8350	4650	2.092	-
0.8368	4660	2.1229	-
0.8386	4670	2.1432	-
0.8404	4680	2.1285	-
0.8422	4690	2.1056	-
0.8440	4700	2.0699	-
0.8447	4704	-	0.4192
0.8458	4710	2.0441	-
0.8475	4720	2.0788	-
0.8493	4730	2.0375	-
0.8511	4740	2.0502	-
0.8529	4750	2.1166	-
0.8547	4760	2.0791	0.4196
0.8565	4770	2.0894	-
0.8583	4780	2.1094	-
0.8601	4790	2.0677	-
0.8619	4800	2.0168	-
0.8637	4810	1.972	-
0.8648	4816	-	0.4219
0.8655	4820	2.0104	-
0.8673	4830	1.7668	-
0.8691	4840	1.6258	-
0.8709	4850	1.779	-
0.8727	4860	1.7525	-
0.8745	4870	1.9056	-
0.8748	4872	-	0.4488
0.8763	4880	1.8157	-
0.8781	4890	1.893	-
0.8799	4900	1.9266	-
0.8817	4910	1.8851	-
0.8835	4920	1.9342	-
0.8849	4928	-	0.5160
0.8853	4930	1.8377	-
0.8871	4940	1.873	-
0.8888	4950	1.8302	-
0.8906	4960	1.9123	-
0.8924	4970	1.8605	-
0.8942	4980	1.878	-
0.8950	4984	-	0.5648
0.8960	4990	1.8416	-
0.8978	5000	1.9061	-
0.8996	5010	1.8084	-
0.9014	5020	1.8982	-
0.9032	5030	1.9167	-
0.9050	5040	1.8795	0.6082
0.9068	5050	1.9449	-
0.9086	5060	1.956	-
0.9104	5070	1.8469	-
0.9122	5080	1.8858	-
0.9140	5090	1.8	-
0.9151	5096	-	0.6452
0.9158	5100	1.7873	-
0.9176	5110	1.7998	-
0.9194	5120	1.9032	-
0.9212	5130	1.8753	-
0.9230	5140	1.8959	-
0.9248	5150	1.7677	-
0.9251	5152	-	0.6645
0.9266	5160	1.8726	-
0.9284	5170	1.8311	-
0.9301	5180	1.8198	-
0.9319	5190	1.8422	-
0.9337	5200	1.8419	-
0.9352	5208	-	0.6856
0.9355	5210	1.7987	-
0.9373	5220	1.8164	-
0.9391	5230	1.7429	-
0.9409	5240	1.8444	-
0.9427	5250	1.8373	-
0.9445	5260	1.7414	-
0.9452	5264	-	0.7004
0.9463	5270	1.8996	-
0.9481	5280	1.821	-
0.9499	5290	1.8124	-
0.9517	5300	1.7433	-
0.9535	5310	1.8208	-
0.9553	5320	1.826	0.7103
0.9571	5330	1.8108	-
0.9589	5340	1.8068	-
0.9607	5350	1.8513	-
0.9625	5360	1.8312	-
0.9643	5370	1.8248	-
0.9653	5376	-	0.7145
0.9661	5380	1.8556	-
0.9679	5390	1.8554	-
0.9697	5400	1.7885	-
0.9714	5410	1.7767	-
0.9732	5420	1.8356	-
0.9750	5430	1.7998	-
0.9754	5432	-	0.7178
0.9768	5440	1.8958	-
0.9786	5450	1.8307	-
0.9804	5460	1.7892	-
0.9822	5470	1.823	-
0.9840	5480	1.8135	-
0.9855	5488	-	0.7201
0.9858	5490	1.7887	-
0.9876	5500	1.8096	-
0.9894	5510	1.8686	-
0.9912	5520	1.8398	-
0.9930	5530	1.9189	-
0.9948	5540	1.689	-
0.9955	5544	-	0.7204
0.9966	5550	1.8621	-
0.9984	5560	1.8037	-

Framework Versions

Python: 3.11.8
Sentence Transformers: 3.1.1
Transformers: 4.45.1
PyTorch: 2.4.0+cu121
Accelerate: 0.34.2
Datasets: 3.0.0
Tokenizers: 0.20.0

Citation

BibTeX

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MaskedCachedMultipleNegativesRankingLoss

bibtex

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

	state_name	premise_name
type	string	string
details	min: 12 tokens mean: 17.86 tokens max: 23 tokens	min: 3 tokens mean: 10.93 tokens max: 36 tokens

state_name	premise_name
`Mathlib.Algebra.Algebra.Operations#96`	`Submodule.le_pow_toAddSubmonoid`
`Mathlib.Algebra.Algebra.Operations#96`	`AddSubmonoid.pow_subset_pow`
`Mathlib.Algebra.Algebra.Operations#96`	`trans`