This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. 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-mpnet-base-v2
Maximum Sequence Length: 384 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    '九月辛未太祖曾孙舒国公从式进封安定郡王',
    '九月初二太祖曾孙舒国公从式进封安定郡王',
    '杨难当在汉中大肆烧杀抢劫然后率众离开了汉中向西返回仇池留下赵温据守梁州又派他的魏兴太守薛健屯驻黄金山',
]
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: 756,057 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 4 tokens
mean: 20.76 tokens
max: 199 tokens
min: 4 tokens
mean: 31.27 tokens
max: 384 tokens

Samples:

anchor	positive
`虏怀兼弱之威挟广地之计强兵大众亲自凌殄旍鼓弥年矢石不息`	`魏人怀有兼并弱小的威严胸藏拓展土地的计谋强人的军队亲自出征侵逼消灭旌旗战鼓连年出动战事不停息`
`孟子曰以善服人者未有能服人者也以善养人然后能服天下`	`孟子说用自己的善良使人们服从的人没有能使人服从的用善良影响教导人们才能使天下的人们都信服`
`开庆初大元兵渡江理宗议迁都平江庆元后谏不可恐摇动民心乃止`	`开庆初年大元朝部队渡过长江理宗打算迁都到平江庆元皇后劝谏不可迁都深恐动摇民心理宗才作罢`

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

Evaluation Dataset

Unnamed Dataset

Size: 84,007 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 4 tokens
mean: 20.23 tokens
max: 138 tokens
min: 4 tokens
mean: 31.42 tokens
max: 384 tokens

Samples:

anchor	positive
`雒阳户五万二千八百三十九`	`雒阳有五万二千八百三十九户`
`拜南青州刺史在任有政绩`	`任南青州刺史很有政绩`
`第六品以下加不得服金钅奠绫锦锦绣七缘绮貂豽裘金叉环铒及以金校饰器物张绛帐`	`官位在第六品以下的官员再增加不得穿用金钿绫锦锦绣七缘绮貂钠皮衣金叉缳饵以及用金装饰的器物张绛帐等衣服物品`

Loss: MultipleNegativesRankingLoss 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: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: 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: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
learning_rate: 5e-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: False
fp16: True
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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	loss
0.0021	100	0.6475	-
0.0042	200	0.5193	-
0.0063	300	0.4132	-
0.0085	400	0.3981	-
0.0106	500	0.4032	-
0.0127	600	0.3627	-
0.0148	700	0.3821	-
0.0169	800	0.3767	-
0.0190	900	0.3731	-
0.0212	1000	0.3744	-
0.0233	1100	0.3115	-
0.0254	1200	0.3998	-
0.0275	1300	0.3103	-
0.0296	1400	0.3251	-
0.0317	1500	0.2833	-
0.0339	1600	0.3335	-
0.0360	1700	0.3281	-
0.0381	1800	0.423	-
0.0402	1900	0.3687	-
0.0423	2000	0.3452	-
0.0444	2100	0.8643	-
0.0466	2200	0.4279	-
0.0487	2300	0.4188	-
0.0508	2400	0.3676	-
0.0529	2500	0.3279	-
0.0550	2600	0.3415	-
0.0571	2700	1.5834	-
0.0593	2800	2.7778	-
0.0614	2900	2.7734	-
0.0635	3000	2.7732	-
0.0656	3100	2.7751	-
0.0677	3200	2.7731	-
0.0698	3300	2.773	-
0.0720	3400	2.7727	-
0.0741	3500	2.7534	-
0.0762	3600	2.2219	-
0.0783	3700	0.5137	-
0.0804	3800	0.4143	-
0.0825	3900	0.4002	-
0.0846	4000	0.368	-
0.0868	4100	0.3879	-
0.0889	4200	0.3519	-
0.0910	4300	0.364	-
0.0931	4400	0.3618	-
0.0952	4500	0.3545	-
0.0973	4600	0.379	-
0.0995	4700	0.3837	-
0.1016	4800	0.3553	-
0.1037	4900	0.3519	-
0.1058	5000	0.3416	0.3487
0.1079	5100	0.3763	-
0.1100	5200	0.3748	-
0.1122	5300	0.3564	-
0.1143	5400	0.336	-
0.1164	5500	0.3601	-
0.1185	5600	0.3521	-
0.1206	5700	0.376	-
0.1227	5800	0.3011	-
0.1249	5900	0.345	-
0.1270	6000	0.3211	-
0.1291	6100	0.3673	-
0.1312	6200	0.3762	-
0.1333	6300	0.3562	-
0.1354	6400	0.2761	-
0.1376	6500	0.3186	-
0.1397	6600	0.3582	-
0.1418	6700	0.3454	-
0.1439	6800	0.3429	-
0.1460	6900	0.2932	-
0.1481	7000	0.3357	-
0.1503	7100	0.2979	-
0.1524	7200	0.313	-
0.1545	7300	0.3364	-
0.1566	7400	0.3459	-
0.1587	7500	0.279	-
0.1608	7600	0.3274	-
0.1629	7700	0.3367	-
0.1651	7800	0.2935	-
0.1672	7900	0.3415	-
0.1693	8000	0.2838	-
0.1714	8100	0.2667	-
0.1735	8200	0.3051	-
0.1756	8300	0.3197	-
0.1778	8400	0.3086	-
0.1799	8500	0.3186	-
0.1820	8600	0.3063	-
0.1841	8700	0.2967	-
0.1862	8800	0.3069	-
0.1883	8900	0.3391	-
0.1905	9000	0.335	-
0.1926	9100	0.3115	-
0.1947	9200	0.3214	-
0.1968	9300	0.278	-
0.1989	9400	0.2833	-
0.2010	9500	0.303	-
0.2032	9600	0.3238	-
0.2053	9700	0.2622	-
0.2074	9800	0.3295	-
0.2095	9900	0.2699	-
0.2116	10000	0.2426	0.2962
0.2137	10100	0.262	-
0.2159	10200	0.3199	-
0.2180	10300	0.3677	-
0.2201	10400	0.2423	-
0.2222	10500	0.3446	-
0.2243	10600	0.3002	-
0.2264	10700	0.2863	-
0.2286	10800	0.2692	-
0.2307	10900	0.3157	-
0.2328	11000	0.3172	-
0.2349	11100	0.3622	-
0.2370	11200	0.3019	-
0.2391	11300	0.2789	-
0.2412	11400	0.2872	-
0.2434	11500	0.2823	-
0.2455	11600	0.3017	-
0.2476	11700	0.2573	-
0.2497	11800	0.3104	-
0.2518	11900	0.2857	-
0.2539	12000	0.2898	-
0.2561	12100	0.2389	-
0.2582	12200	0.3137	-
0.2603	12300	0.3029	-
0.2624	12400	0.2894	-
0.2645	12500	0.2665	-
0.2666	12600	0.2705	-
0.2688	12700	0.2673	-
0.2709	12800	0.248	-
0.2730	12900	0.2417	-
0.2751	13000	0.2852	-
0.2772	13100	0.2619	-
0.2793	13200	0.3157	-
0.2815	13300	0.2464	-
0.2836	13400	0.2837	-
0.2857	13500	0.3202	-
0.2878	13600	0.2618	-
0.2899	13700	0.2823	-
0.2920	13800	0.2634	-
0.2942	13900	0.2747	-
0.2963	14000	0.2835	-
0.2984	14100	0.2594	-
0.3005	14200	0.2744	-
0.3026	14300	0.2722	-
0.3047	14400	0.2514	-
0.3069	14500	0.2809	-
0.3090	14600	0.2949	-
0.3111	14700	0.2687	-
0.3132	14800	0.3	-
0.3153	14900	0.2684	-
0.3174	15000	0.2894	0.2790
0.3195	15100	0.2676	-
0.3217	15200	0.2519	-
0.3238	15300	0.2698	-
0.3259	15400	0.2898	-
0.3280	15500	0.2359	-
0.3301	15600	0.2866	-
0.3322	15700	0.3098	-
0.3344	15800	0.2809	-
0.3365	15900	0.3081	-
0.3386	16000	0.266	-
0.3407	16100	0.2523	-
0.3428	16200	0.3215	-
0.3449	16300	0.2883	-
0.3471	16400	0.2897	-
0.3492	16500	0.3174	-
0.3513	16600	0.2878	-
0.3534	16700	0.267	-
0.3555	16800	0.2452	-
0.3576	16900	0.2429	-
0.3598	17000	0.2178	-
0.3619	17100	0.2798	-
0.3640	17200	0.2367	-
0.3661	17300	0.2554	-
0.3682	17400	0.2883	-
0.3703	17500	0.2567	-
0.3725	17600	0.27	-
0.3746	17700	0.2837	-
0.3767	17800	0.2783	-
0.3788	17900	0.2517	-
0.3809	18000	0.2545	-
0.3830	18100	0.2632	-
0.3852	18200	0.2074	-
0.3873	18300	0.2276	-
0.3894	18400	0.3022	-
0.3915	18500	0.2381	-
0.3936	18600	0.2552	-
0.3957	18700	0.2579	-
0.3978	18800	0.2655	-
0.4000	18900	0.252	-
0.4021	19000	0.2876	-
0.4042	19100	0.2037	-
0.4063	19200	0.251	-
0.4084	19300	0.2588	-
0.4105	19400	0.201	-
0.4127	19500	0.2828	-
0.4148	19600	0.2637	-
0.4169	19700	0.3233	-
0.4190	19800	0.2475	-
0.4211	19900	0.2618	-
0.4232	20000	0.3272	0.2519
0.4254	20100	0.3074	-
0.4275	20200	0.2994	-
0.4296	20300	0.2624	-
0.4317	20400	0.2389	-
0.4338	20500	0.2809	-
0.4359	20600	0.2659	-
0.4381	20700	0.2508	-
0.4402	20800	0.2542	-
0.4423	20900	0.2525	-
0.4444	21000	0.257	-
0.4465	21100	0.2242	-
0.4486	21200	0.2307	-
0.4508	21300	0.2721	-
0.4529	21400	0.2489	-
0.4550	21500	0.2933	-
0.4571	21600	0.2448	-
0.4592	21700	0.2619	-
0.4613	21800	0.2488	-
0.4635	21900	0.2411	-
0.4656	22000	0.2964	-
0.4677	22100	0.2062	-
0.4698	22200	0.2665	-
0.4719	22300	0.263	-
0.4740	22400	0.2418	-
0.4762	22500	0.2879	-
0.4783	22600	0.2406	-
0.4804	22700	0.2448	-
0.4825	22800	0.243	-
0.4846	22900	0.2863	-
0.4867	23000	0.2833	-
0.4888	23100	0.2784	-
0.4910	23200	0.2789	-
0.4931	23300	0.2495	-
0.4952	23400	0.2872	-
0.4973	23500	0.2487	-
0.4994	23600	0.2669	-
0.5015	23700	0.2748	-
0.5037	23800	0.246	-
0.5058	23900	0.2512	-
0.5079	24000	0.222	-
0.5100	24100	0.2662	-
0.5121	24200	0.2238	-
0.5142	24300	0.2399	-
0.5164	24400	0.2595	-
0.5185	24500	0.3002	-
0.5206	24600	0.2553	-
0.5227	24700	0.226	-
0.5248	24800	0.2823	-
0.5269	24900	0.2737	-
0.5291	25000	0.2237	0.2492
0.5312	25100	0.2642	-
0.5333	25200	0.2486	-
0.5354	25300	0.2527	-
0.5375	25400	0.2363	-
0.5396	25500	0.2443	-
0.5418	25600	0.2485	-
0.5439	25700	0.2434	-
0.5460	25800	0.2631	-
0.5481	25900	0.284	-
0.5502	26000	0.217	-
0.5523	26100	0.2246	-
0.5545	26200	0.2614	-
0.5566	26300	0.2722	-
0.5587	26400	0.2114	-
0.5608	26500	0.2623	-
0.5629	26600	0.2475	-
0.5650	26700	0.2449	-
0.5671	26800	0.2423	-
0.5693	26900	0.2435	-
0.5714	27000	0.2446	-
0.5735	27100	0.2248	-
0.5756	27200	0.2159	-
0.5777	27300	0.2415	-
0.5798	27400	0.2257	-
0.5820	27500	0.2775	-
0.5841	27600	0.2533	-
0.5862	27700	0.2893	-
0.5883	27800	0.2095	-
0.5904	27900	0.2156	-
0.5925	28000	0.2315	-
0.5947	28100	0.2865	-
0.5968	28200	0.262	-
0.5989	28300	0.2506	-
0.6010	28400	0.2472	-
0.6031	28500	0.2395	-
0.6052	28600	0.2269	-
0.6074	28700	0.2639	-
0.6095	28800	0.2674	-
0.6116	28900	0.2521	-
0.6137	29000	0.2553	-
0.6158	29100	0.2526	-
0.6179	29200	0.231	-
0.6201	29300	0.2622	-
0.6222	29400	0.237	-
0.6243	29500	0.2475	-
0.6264	29600	0.2435	-
0.6285	29700	0.2109	-
0.6306	29800	0.2376	-
0.6328	29900	0.2202	-
0.6349	30000	0.2147	0.2370
0.6370	30100	0.2306	-
0.6391	30200	0.2249	-
0.6412	30300	0.3027	-
0.6433	30400	0.2115	-
0.6454	30500	0.2597	-
0.6476	30600	0.2483	-
0.6497	30700	0.2719	-
0.6518	30800	0.2162	-
0.6539	30900	0.2947	-
0.6560	31000	0.2144	-
0.6581	31100	0.2391	-
0.6603	31200	0.2572	-
0.6624	31300	0.1977	-
0.6645	31400	0.2678	-
0.6666	31500	0.2353	-
0.6687	31600	0.1911	-
0.6708	31700	0.2844	-
0.6730	31800	0.2689	-
0.6751	31900	0.2491	-
0.6772	32000	0.2259	-
0.6793	32100	0.2248	-
0.6814	32200	0.2462	-
0.6835	32300	0.2135	-
0.6857	32400	0.2085	-
0.6878	32500	0.227	-
0.6899	32600	0.2488	-
0.6920	32700	0.2614	-
0.6941	32800	0.2274	-
0.6962	32900	0.2389	-
0.6984	33000	0.2573	-
0.7005	33100	0.245	-
0.7026	33200	0.21	-
0.7047	33300	0.2196	-
0.7068	33400	0.2218	-
0.7089	33500	0.2092	-
0.7111	33600	0.2526	-
0.7132	33700	0.2275	-
0.7153	33800	0.2622	-
0.7174	33900	0.2469	-
0.7195	34000	0.2157	-
0.7216	34100	0.2326	-
0.7237	34200	0.268	-
0.7259	34300	0.2628	-
0.7280	34400	0.2503	-
0.7301	34500	0.2101	-
0.7322	34600	0.237	-
0.7343	34700	0.233	-
0.7364	34800	0.2077	-
0.7386	34900	0.259	-
0.7407	35000	0.2312	0.2284
0.7428	35100	0.287	-
0.7449	35200	0.2278	-
0.7470	35300	0.2618	-
0.7491	35400	0.2298	-
0.7513	35500	0.195	-
0.7534	35600	0.2248	-
0.7555	35700	0.2234	-
0.7576	35800	0.2218	-
0.7597	35900	0.2002	-
0.7618	36000	0.2158	-
0.7640	36100	0.1919	-
0.7661	36200	0.2972	-
0.7682	36300	0.2665	-
0.7703	36400	0.2114	-
0.7724	36500	0.1879	-
0.7745	36600	0.2137	-
0.7767	36700	0.2847	-
0.7788	36800	0.2372	-
0.7809	36900	0.2058	-
0.7830	37000	0.2205	-
0.7851	37100	0.2012	-
0.7872	37200	0.2057	-
0.7894	37300	0.1932	-
0.7915	37400	0.2261	-
0.7936	37500	0.2633	-
0.7957	37600	0.1558	-
0.7978	37700	0.2064	-
0.7999	37800	0.2166	-
0.8020	37900	0.2249	-
0.8042	38000	0.2626	-
0.8063	38100	0.1945	-
0.8084	38200	0.2611	-
0.8105	38300	0.199	-
0.8126	38400	0.2004	-
0.8147	38500	0.2506	-
0.8169	38600	0.1722	-
0.8190	38700	0.1959	-
0.8211	38800	0.2505	-
0.8232	38900	0.2343	-
0.8253	39000	0.2353	-
0.8274	39100	0.22	-
0.8296	39200	0.2089	-
0.8317	39300	0.2416	-
0.8338	39400	0.1916	-
0.8359	39500	0.2387	-
0.8380	39600	0.2475	-
0.8401	39700	0.2189	-
0.8423	39800	0.2141	-
0.8444	39900	0.2008	-
0.8465	40000	0.2489	0.2253
0.8486	40100	0.2258	-
0.8507	40200	0.2341	-
0.8528	40300	0.2377	-
0.8550	40400	0.194	-
0.8571	40500	0.2144	-
0.8592	40600	0.2605	-
0.8613	40700	0.2517	-
0.8634	40800	0.2044	-
0.8655	40900	0.2259	-
0.8677	41000	0.2141	-
0.8698	41100	0.1895	-
0.8719	41200	0.2361	-
0.8740	41300	0.1978	-
0.8761	41400	0.2089	-
0.8782	41500	0.2258	-
0.8803	41600	0.2368	-
0.8825	41700	0.2473	-
0.8846	41800	0.2185	-
0.8867	41900	0.212	-
0.8888	42000	0.2469	-
0.8909	42100	0.1817	-
0.8930	42200	0.1884	-
0.8952	42300	0.207	-
0.8973	42400	0.2422	-
0.8994	42500	0.2606	-
0.9015	42600	0.2266	-
0.9036	42700	0.2103	-
0.9057	42800	0.2712	-
0.9079	42900	0.1944	-
0.9100	43000	0.2003	-
0.9121	43100	0.1991	-
0.9142	43200	0.2129	-
0.9163	43300	0.2465	-
0.9184	43400	0.1764	-
0.9206	43500	0.2365	-
0.9227	43600	0.2054	-
0.9248	43700	0.2551	-
0.9269	43800	0.2322	-
0.9290	43900	0.2213	-
0.9311	44000	0.1962	-
0.9333	44100	0.1988	-
0.9354	44200	0.1982	-
0.9375	44300	0.2193	-
0.9396	44400	0.2378	-
0.9417	44500	0.2244	-
0.9438	44600	0.2296	-
0.9460	44700	0.2446	-
0.9481	44800	0.2206	-
0.9502	44900	0.1815	-
0.9523	45000	0.2385	0.22
0.9544	45100	0.2106	-
0.9565	45200	0.1929	-
0.9586	45300	0.181	-
0.9608	45400	0.1908	-
0.9629	45500	0.1926	-
0.9650	45600	0.1922	-
0.9671	45700	0.2003	-
0.9692	45800	0.2377	-
0.9713	45900	0.2069	-
0.9735	46000	0.2024	-
0.9756	46100	0.1795	-
0.9777	46200	0.2372	-
0.9798	46300	0.2135	-
0.9819	46400	0.2396	-
0.9840	46500	0.2295	-
0.9862	46600	0.2235	-
0.9883	46700	0.2427	-
0.9904	46800	0.2145	-
0.9925	46900	0.2231	-
0.9946	47000	0.2401	-
0.9967	47100	0.1764	-
0.9989	47200	0.1943	-

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.4
Sentence Transformers: 3.1.0.dev0
Transformers: 4.42.4
PyTorch: 2.3.1+cpu
Accelerate: 0.32.1
Datasets: 2.20.0
Tokenizers: 0.19.1

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",
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	anchor	positive
type	string	string
details	min: 4 tokens mean: 20.76 tokens max: 199 tokens	min: 4 tokens mean: 31.27 tokens max: 384 tokens

RinaChen/GuWen-all-mpnet-base-v2-new

SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss

RinaChen/
GuWen-all-mpnet-base-v2-new