MPNet base trained on AllNLI triplets

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large-instruct on the json 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: intfloat/multilingual-e5-large-instruct
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json
Language: en
License: apache-2.0

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: XLMRobertaModel 
  (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})
  (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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("deadf00d/multilingual-e5-mnrl-outcomes")
# Run inference
sentences = [
    'Notre Dame @ Houston Spread Betting Notre Dame 12.5',
    'Notre Dame @ Houston Spread Alternate - NCAAB Live Betting Notre Dame +12.5',
    'TCU @ Santa Clara Spread Alternate - NCAAB Live Betting Santa Clara +1.5',
]
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

Binary Classification

Dataset: sts-dev
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.7131
cosine_accuracy_threshold	1.0
cosine_f1	0.8309
cosine_f1_threshold	1.0
cosine_precision	0.7107
cosine_recall	1.0
cosine_ap	0.7049
cosine_mcc	0.0

Training Details

Training Dataset

json

Dataset: json
Size: 43,344,766 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 8 tokens mean: 18.78 tokens max: 38 tokens	min: 7 tokens mean: 18.71 tokens max: 35 tokens	min: 9 tokens mean: 19.44 tokens max: 35 tokens

Samples:

anchor	positive	negative
`Rutgers @ Michigan State Spread Live Betting Michigan State +14.5`	`Rutgers @ Michigan State Alternate Spread Michigan State (+14.5)`	`UConn Huskies vs. Dayton Flyers 1st Half Total Over 69.5`
`Radford @ St. Francis (PA) Spread Betting St. Francis (PA) 6.5`	`Radford vs St. Francis (PA) Red Flash Point Spread Including Overtime St. Francis (PA) Red Flash +6.5`	`Radford @ Chicago State O/U Live Betting Under 113.5`
`George Mason @ James Madison Spread Live Betting George Mason -4.5`	`George Mason @ James Madison Spread Alternate - NCAAB Live Betting George Mason -4.5`	`Sam Houston @ UNC Wilmington O/U Over 151`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

json

Dataset: json
Size: 2,281,303 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 8 tokens mean: 19.05 tokens max: 36 tokens	min: 10 tokens mean: 18.75 tokens max: 33 tokens	min: 10 tokens mean: 19.15 tokens max: 35 tokens

Samples:

anchor	positive	negative
`Houston @ BYU Any Time Touchdown Scorer Jake Retzlaff (BYU)`	`Houston @ BYU Any Time Touchdown Scorer Jake Retzlaff (BYU)`	`Barcelona v Brest Player To Have 2 Or More Shots On Target Robert Lewandowski`
`Canisius @ Robert Morris Spread Live Betting Canisius +9.5`	`Canisius Golden Griffins vs. Robert Morris Colonials Spread CAN +9.5`	`Paris St-Germain vs Nantes Nantes: Team Total Goals Live Betting Under 1.5`
`Oklahoma at Arizona Total Points Under 153.5`	`Oklahoma at (24) Arizona Total Points Under 153.5`	`Winnipeg Jets @ Vegas Golden Knights Tomas Hertl Total Goals Under 0.5`

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: 850
per_device_eval_batch_size: 850
learning_rate: 0.0002
num_train_epochs: 1
warmup_ratio: 0.1
fp16: 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: 850
per_device_eval_batch_size: 850
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
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: 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}
tp_size: 0
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: None
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: False
eval_use_gather_object: False
average_tokens_across_devices: 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_cosine_ap
-1	-1	-	-	1.0
0.0002	10	5.6952	-	-
0.0004	20	5.5552	-	-
0.0006	30	4.9085	-	-
0.0008	40	3.8005	-	-
0.0010	50	2.3846	-	-
0.0012	60	0.9869	-	-
0.0014	70	0.2934	-	-
0.0016	80	0.1336	-	-
0.0018	90	0.0987	-	-
0.0020	100	0.0817	-	-
0.0022	110	0.071	-	-
0.0024	120	0.0635	-	-
0.0025	130	0.0594	-	-
0.0027	140	0.0572	-	-
0.0029	150	0.0536	-	-
0.0031	160	0.0532	-	-
0.0033	170	0.0518	-	-
0.0035	180	0.0554	-	-
0.0037	190	0.0497	-	-
0.0039	200	0.0465	-	-
0.0041	210	0.047	-	-
0.0043	220	0.049	-	-
0.0045	230	0.05	-	-
0.0047	240	0.048	-	-
0.0049	250	0.0442	-	-
0.0051	260	0.0443	-	-
0.0053	270	0.0411	-	-
0.0055	280	0.046	-	-
0.0057	290	0.0432	-	-
0.0059	300	0.0428	-	-
0.0061	310	0.0396	-	-
0.0063	320	0.0402	-	-
0.0065	330	0.0402	-	-
0.0067	340	0.0382	-	-
0.0069	350	0.04	-	-
0.0071	360	0.0436	-	-
0.0073	370	0.0397	-	-
0.0075	380	0.0384	-	-
0.0076	390	0.0416	-	-
0.0078	400	0.0374	-	-
0.0080	410	0.0384	-	-
0.0082	420	0.0375	-	-
0.0084	430	0.0353	-	-
0.0086	440	0.038	-	-
0.0088	450	0.0341	-	-
0.0090	460	0.0373	-	-
0.0092	470	0.0337	-	-
0.0094	480	0.0374	-	-
0.0096	490	0.035	-	-
0.0098	500	0.0327	-	-
0.0100	510	0.0342	-	-
0.0102	520	0.0373	-	-
0.0104	530	0.0361	-	-
0.0106	540	0.032	-	-
0.0108	550	0.0348	-	-
0.0110	560	0.0352	-	-
0.0112	570	0.0314	-	-
0.0114	580	0.0339	-	-
0.0116	590	0.0292	-	-
0.0118	600	0.0314	-	-
0.0120	610	0.0351	-	-
0.0122	620	0.0326	-	-
0.0124	630	0.0338	-	-
0.0126	640	0.0338	-	-
0.0127	650	0.0347	-	-
0.0129	660	0.0304	-	-
0.0131	670	0.0314	-	-
0.0133	680	0.0326	-	-
0.0135	690	0.0341	-	-
0.0137	700	0.0299	-	-
0.0139	710	0.0315	-	-
0.0141	720	0.0325	-	-
0.0143	730	0.0324	-	-
0.0145	740	0.031	-	-
0.0147	750	0.0303	-	-
0.0149	760	0.03	-	-
0.0151	770	0.0329	-	-
0.0153	780	0.0325	-	-
0.0155	790	0.0341	-	-
0.0157	800	0.0284	-	-
0.0159	810	0.0309	-	-
0.0161	820	0.0295	-	-
0.0163	830	0.0325	-	-
0.0165	840	0.0326	-	-
0.0167	850	0.0365	-	-
0.0169	860	0.0289	-	-
0.0171	870	0.0318	-	-
0.0173	880	0.0297	-	-
0.0175	890	0.028	-	-
0.0176	900	0.0303	-	-
0.0178	910	0.0346	-	-
0.0180	920	0.0321	-	-
0.0182	930	0.0303	-	-
0.0184	940	0.0335	-	-
0.0186	950	0.0314	-	-
0.0188	960	0.0339	-	-
0.0190	970	0.0304	-	-
0.0192	980	0.0315	-	-
0.0194	990	0.0301	-	-
0.0196	1000	0.0295	0.0280	1.0
0.0198	1010	0.0314	-	-
0.0200	1020	0.0303	-	-
0.0202	1030	0.0306	-	-
0.0204	1040	0.0277	-	-
0.0206	1050	0.0284	-	-
0.0208	1060	0.0326	-	-
0.0210	1070	0.0339	-	-
0.0212	1080	0.03	-	-
0.0214	1090	0.0278	-	-
0.0216	1100	0.0313	-	-
0.0218	1110	0.0293	-	-
0.0220	1120	0.0299	-	-
0.0222	1130	0.0276	-	-
0.0224	1140	0.032	-	-
0.0226	1150	0.0311	-	-
0.0227	1160	0.0324	-	-
0.0229	1170	0.0319	-	-
0.0231	1180	0.0315	-	-
0.0233	1190	0.0329	-	-
0.0235	1200	0.0357	-	-
0.0237	1210	0.0313	-	-
0.0239	1220	0.031	-	-
0.0241	1230	0.0335	-	-
0.0243	1240	0.0276	-	-
0.0245	1250	0.0292	-	-
0.0247	1260	0.0294	-	-
0.0249	1270	0.031	-	-
0.0251	1280	0.0294	-	-
0.0253	1290	0.0288	-	-
0.0255	1300	0.0286	-	-
0.0257	1310	0.0308	-	-
0.0259	1320	0.0344	-	-
0.0261	1330	0.0343	-	-
0.0263	1340	0.0297	-	-
0.0265	1350	0.0295	-	-
0.0267	1360	0.0315	-	-
0.0269	1370	0.0295	-	-
0.0271	1380	0.0299	-	-
0.0273	1390	0.0319	-	-
0.0275	1400	0.0304	-	-
0.0277	1410	0.0308	-	-
0.0278	1420	0.0305	-	-
0.0280	1430	0.0302	-	-
0.0282	1440	0.0369	-	-
0.0284	1450	0.0296	-	-
0.0286	1460	0.0329	-	-
0.0288	1470	0.0319	-	-
0.0290	1480	0.0327	-	-
0.0292	1490	0.0319	-	-
0.0294	1500	0.0311	-	-
0.0296	1510	0.0268	-	-
0.0298	1520	0.0276	-	-
0.0300	1530	0.032	-	-
0.0302	1540	0.0314	-	-
0.0304	1550	0.0287	-	-
0.0306	1560	0.03	-	-
0.0308	1570	0.0284	-	-
0.0310	1580	0.032	-	-
0.0312	1590	0.0305	-	-
0.0314	1600	0.029	-	-
0.0316	1610	0.0281	-	-
0.0318	1620	0.0287	-	-
0.0320	1630	0.0304	-	-
0.0322	1640	0.0309	-	-
0.0324	1650	0.0313	-	-
0.0326	1660	0.0346	-	-
0.0327	1670	0.0359	-	-
0.0329	1680	0.0319	-	-
0.0331	1690	0.0302	-	-
0.0333	1700	0.0271	-	-
0.0335	1710	0.0311	-	-
0.0337	1720	0.0298	-	-
0.0339	1730	0.0294	-	-
0.0341	1740	0.0309	-	-
0.0343	1750	0.0276	-	-
0.0345	1760	0.0296	-	-
0.0347	1770	0.0322	-	-
0.0349	1780	0.0319	-	-
0.0351	1790	0.0319	-	-
0.0353	1800	0.0327	-	-
0.0355	1810	0.0299	-	-
0.0357	1820	0.0324	-	-
0.0359	1830	0.0346	-	-
0.0361	1840	0.0328	-	-
0.0363	1850	0.0337	-	-
0.0365	1860	0.0308	-	-
0.0367	1870	0.0499	-	-
0.0369	1880	0.4384	-	-
0.0371	1890	2.0937	-	-
0.0373	1900	0.0382	-	-
0.0375	1910	0.0346	-	-
0.0377	1920	0.0633	-	-
0.0378	1930	0.0306	-	-
0.0380	1940	0.0344	-	-
0.0382	1950	0.0333	-	-
0.0384	1960	0.0374	-	-
0.0386	1970	0.0305	-	-
0.0388	1980	0.0857	-	-
0.0390	1990	0.0372	-	-
0.0392	2000	0.03	0.0273	1.0
0.0394	2010	0.0305	-	-
0.0396	2020	0.0302	-	-
0.0398	2030	0.032	-	-
0.0400	2040	0.0317	-	-
0.0402	2050	0.0289	-	-
0.0404	2060	0.0349	-	-
0.0406	2070	0.0313	-	-
0.0408	2080	0.0309	-	-
0.0410	2090	0.0303	-	-
0.0412	2100	0.038	-	-
0.0414	2110	0.0332	-	-
0.0416	2120	0.031	-	-
0.0418	2130	0.0327	-	-
0.0420	2140	0.0318	-	-
0.0422	2150	0.0287	-	-
0.0424	2160	0.0342	-	-
0.0426	2170	0.0314	-	-
0.0428	2180	0.031	-	-
0.0429	2190	0.0315	-	-
0.0431	2200	0.0283	-	-
0.0433	2210	0.0293	-	-
0.0435	2220	0.0281	-	-
0.0437	2230	0.0294	-	-
0.0439	2240	0.0317	-	-
0.0441	2250	0.0306	-	-
0.0443	2260	0.0357	-	-
0.0445	2270	0.0325	-	-
0.0447	2280	0.0316	-	-
0.0449	2290	0.034	-	-
0.0451	2300	0.033	-	-
0.0453	2310	0.028	-	-
0.0455	2320	0.0318	-	-
0.0457	2330	0.0341	-	-
0.0459	2340	0.0322	-	-
0.0461	2350	0.0318	-	-
0.0463	2360	0.0351	-	-
0.0465	2370	0.0326	-	-
0.0467	2380	0.033	-	-
0.0469	2390	0.033	-	-
0.0471	2400	0.0362	-	-
0.0473	2410	0.0345	-	-
0.0475	2420	0.0342	-	-
0.0477	2430	0.0328	-	-
0.0478	2440	0.033	-	-
0.0480	2450	0.0342	-	-
0.0482	2460	0.0332	-	-
0.0484	2470	0.0335	-	-
0.0486	2480	0.0316	-	-
0.0488	2490	0.0297	-	-
0.0490	2500	0.037	-	-
0.0492	2510	0.0316	-	-
0.0494	2520	0.0411	-	-
0.0496	2530	0.0353	-	-
0.0498	2540	0.0307	-	-
0.0500	2550	0.0323	-	-
0.0502	2560	0.0354	-	-
0.0504	2570	0.0331	-	-
0.0506	2580	0.0309	-	-
0.0508	2590	0.0301	-	-
0.0510	2600	0.0312	-	-
0.0512	2610	0.0335	-	-
0.0514	2620	0.0361	-	-
0.0516	2630	0.0304	-	-
0.0518	2640	0.0357	-	-
0.0520	2650	0.0304	-	-
0.0522	2660	0.037	-	-
0.0524	2670	0.034	-	-
0.0526	2680	0.0314	-	-
0.0528	2690	0.0312	-	-
0.0529	2700	0.0302	-	-
0.0531	2710	0.0335	-	-
0.0533	2720	0.0322	-	-
0.0535	2730	0.0342	-	-
0.0537	2740	0.0365	-	-
0.0539	2750	0.0305	-	-
0.0541	2760	0.03	-	-
0.0543	2770	0.0303	-	-
0.0545	2780	0.0372	-	-
0.0547	2790	0.0353	-	-
0.0549	2800	0.0313	-	-
0.0551	2810	0.0318	-	-
0.0553	2820	0.0314	-	-
0.0555	2830	0.0283	-	-
0.0557	2840	0.0312	-	-
0.0559	2850	0.0329	-	-
0.0561	2860	0.0312	-	-
0.0563	2870	0.031	-	-
0.0565	2880	0.028	-	-
0.0567	2890	0.0314	-	-
0.0569	2900	0.0342	-	-
0.0571	2910	0.0304	-	-
0.0573	2920	0.0313	-	-
0.0575	2930	0.0297	-	-
0.0577	2940	0.0323	-	-
0.0578	2950	0.0327	-	-
0.0580	2960	0.0309	-	-
0.0582	2970	0.0364	-	-
0.0584	2980	0.0303	-	-
0.0586	2990	0.0295	-	-
0.0588	3000	0.0283	0.0271	1.0
0.0590	3010	0.0339	-	-
0.0592	3020	0.0292	-	-
0.0594	3030	0.0321	-	-
0.0596	3040	0.0285	-	-
0.0598	3050	0.0306	-	-
0.0600	3060	0.033	-	-
0.0602	3070	0.0264	-	-
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0.1569	8000	7.4385	7.4369	0.7049
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0.1896	9670	7.4391	-	-
0.1898	9680	7.4397	-	-
0.1900	9690	7.4392	-	-
0.1902	9700	7.4387	-	-
0.1904	9710	7.4396	-	-
0.1906	9720	7.4392	-	-
0.1908	9730	7.4393	-	-
0.1910	9740	7.4394	-	-
0.1912	9750	7.4397	-	-
0.1914	9760	7.4394	-	-
0.1916	9770	7.4393	-	-
0.1918	9780	7.439	-	-
0.1920	9790	7.4391	-	-
0.1922	9800	7.4392	-	-
0.1924	9810	7.4389	-	-
0.1926	9820	7.4393	-	-
0.1928	9830	7.4395	-	-
0.1930	9840	7.4395	-	-
0.1932	9850	7.4397	-	-
0.1934	9860	7.4394	-	-
0.1936	9870	7.4394	-	-
0.1937	9880	7.4392	-	-
0.1939	9890	7.4394	-	-
0.1941	9900	7.4396	-	-
0.1943	9910	7.4393	-	-
0.1945	9920	7.4395	-	-
0.1947	9930	7.4389	-	-
0.1949	9940	7.4389	-	-
0.1951	9950	7.4395	-	-
0.1953	9960	7.4392	-	-
0.1955	9970	7.4396	-	-
0.1957	9980	7.4402	-	-
0.1959	9990	7.4402	-	-
0.1961	10000	7.4397	7.4369	0.7049
0.1963	10010	7.4396	-	-
0.1965	10020	7.4393	-	-
0.1967	10030	7.4392	-	-
0.1969	10040	7.439	-	-
0.1971	10050	7.4396	-	-
0.1973	10060	7.4394	-	-
0.1975	10070	7.4391	-	-
0.1977	10080	7.4391	-	-
0.1979	10090	7.4396	-	-
0.1981	10100	7.4392	-	-
0.1983	10110	7.4397	-	-
0.1985	10120	7.4393	-	-
0.1987	10130	7.4392	-	-
0.1988	10140	7.439	-	-
0.1990	10150	7.4394	-	-
0.1992	10160	7.4391	-	-
0.1994	10170	7.439	-	-
0.1996	10180	7.4389	-	-
0.1998	10190	7.4392	-	-
0.2000	10200	7.439	-	-
0.2002	10210	7.439	-	-
0.2004	10220	7.4391	-	-
0.2006	10230	7.4387	-	-
0.2008	10240	7.4399	-	-
0.2010	10250	7.4395	-	-
0.2012	10260	7.4395	-	-
0.2014	10270	7.4387	-	-
0.2016	10280	7.439	-	-
0.2018	10290	7.4394	-	-
0.2020	10300	7.4394	-	-
0.2022	10310	7.4393	-	-
0.2024	10320	7.439	-	-
0.2026	10330	7.4394	-	-
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0.2353	12000	7.4385	7.4369	0.7049
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0.8587	43790	7.4386	-	-
0.8589	43800	7.4385	-	-
0.8591	43810	7.4385	-	-
0.8593	43820	7.4385	-	-
0.8595	43830	7.4386	-	-
0.8597	43840	7.4385	-	-
0.8599	43850	7.4385	-	-
0.8601	43860	7.4385	-	-
0.8603	43870	7.4385	-	-
0.8605	43880	7.4385	-	-
0.8607	43890	7.4386	-	-
0.8609	43900	7.4385	-	-
0.8611	43910	7.4385	-	-
0.8613	43920	7.4385	-	-
0.8615	43930	7.4385	-	-
0.8617	43940	7.4386	-	-
0.8619	43950	7.4385	-	-
0.8621	43960	7.4384	-	-
0.8623	43970	7.4387	-	-
0.8625	43980	7.4386	-	-
0.8627	43990	7.4386	-	-
0.8628	44000	7.4385	-	-

Framework Versions

Python: 3.11.10
Sentence Transformers: 4.0.1
Transformers: 4.50.3
PyTorch: 2.5.1+cu124
Accelerate: 1.5.2
Datasets: 3.5.0
Tokenizers: 0.21.1

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",
}

MultipleNegativesRankingLoss

@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}
}

deadf00d
/

multilingual-e5-mnrl-outcomes

MPNet base trained on AllNLI triplets

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Binary Classification

Training Details

Training Dataset

json

Evaluation Dataset

json

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss

Model tree for deadf00d/multilingual-e5-mnrl-outcomes

Evaluation results