This is a sentence-transformers model finetuned from sucharush/e5_stem_finetuned. 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: sucharush/e5_stem_finetuned
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 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': 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("Stergios-Konstantinidis/MNLP_M3_tokenizer_tuned")
# Run inference
sentences = [
    '[{\'from\': \'human\', \'value\': \'Solve the following math problem step-by-step.\\nSimplify your answer as much as possible. Present your final answer as \\\\boxed{Your Answer}.\\nA pentagon is drawn by placing an isosceles right triangle on top of a square as pictured. What percent of the area of the pentagon is the area of the right triangle?\\n\\n[asy]\\nsize(50);\\ndraw((0,0)--(0,-1)--(1,-1)--(1,0)--(0,0)--(.5,.5)--(1,0));\\n[/asy]\'}, {\'from\': \'gpt\', \'value\': "Step 1:  We are given a pentagon formed by placing an isosceles right triangle on top of a square.\\nStep 2:  To find the percentage of the area of the pentagon that is the right triangle, we need to consider the areas of both shapes and then calculate the ratio of the triangle\'s area to the total pentagon\'s area.\\nStep 3:  Let\'s designate the leg length of the isosceles right triangle as $x$. This means the hypotenuse of the triangle has a length of $x\\\\sqrt{2}$.\\nStep 4:  Since the hypotenuse of the triangle is a side of the square, the area of the square is $(x\\\\sqrt{2})^2 = 2x^2$. \\nStep 5:  On the other hand, the area of the triangle is $(x)(x)/2 = x^2/2$. This is obtained using the standard formula for the area of a triangle, which is base times height divided by 2.\\nStep 6:  So, the area of the pentagon is the sum of the areas of the triangle and the square, which gives us $\\\\frac{x^2}{2} + 2x^2 = \\\\frac{5x^2}{2}$.\\nStep 7:  Now, to find the percentage of the pentagon\'s area that is inside the triangle, we calculate the ratio of the triangle\'s area to the total pentagon\'s area. This can be computed as $\\\\frac{x^2/2}{5x^2/2} =\\\\frac{x^2}{2}\\\\cdot \\\\frac{2}{5x^2} = \\\\frac15$.\\nStep 8:  Therefore, the percentage of the area of the pentagon that is the right triangle is $\\\\frac{1}{5}$, which is equivalent to $\\\\boxed{20\\\\%}$.\\nStep 9:  As an alternate solution, we can consider drawing the two diagonals of the square and observe the resulting geometric properties to arrive at the same conclusion.\\n\\nAnswer:\\n\\\\boxed{20\\\\%}"}]',
    '[{\'from\': \'human\', \'value\': \'Solve the following math problem step-by-step.\\nSimplify your answer as much as possible. Present your final answer as \\\\boxed{Your Answer}.\\nA pentagon is drawn by placing an isosceles right triangle on top of a square as pictured. What percent of the area of the pentagon is the area of the right triangle?\\n\\n[asy]\\nsize(50);\\ndraw((0,0)--(0,-1)--(1,-1)--(1,0)--(0,0)--(.5,.5)--(1,0));\\n[/asy]\'}, {\'from\': \'gpt\', \'value\': "Step 1:  We are given a pentagon formed by placing an isosceles right triangle on top of a square.\\nStep 2:  To find the percentage of the area of the pentagon that is the right triangle, we need to consider the areas of both shapes and then calculate the ratio of the triangle\'s area to the total pentagon\'s area.\\nStep 3:  Let\'s designate the leg length of the isosceles right triangle as $x$. This means the hypotenuse of the triangle has a length of $x\\\\sqrt{2}$.\\nStep 4:  Since the hypotenuse of the triangle is a side of the square, the area of the square is $(x\\\\sqrt{2})^2 = 2x^2$. \\nStep 5:  On the other hand, the area of the triangle is $(x)(x)/2 = x^2/2$. This is obtained using the standard formula for the area of a triangle, which is base times height divided by 2.\\nStep 6:  So, the area of the pentagon is the sum of the areas of the triangle and the square, which gives us $\\\\frac{x^2}{2} + 2x^2 = \\\\frac{5x^2}{2}$.\\nStep 7:  Now, to find the percentage of the pentagon\'s area that is inside the triangle, we calculate the ratio of the triangle\'s area to the total pentagon\'s area. This can be computed as $\\\\frac{x^2/2}{5x^2/2} =\\\\frac{x^2}{2}\\\\cdot \\\\frac{2}{5x^2} = \\\\frac15$.\\nStep 8:  Therefore, the percentage of the area of the pentagon that is the right triangle is $\\\\frac{1}{5}$, which is equivalent to $\\\\boxed{20\\\\%}$.\\nStep 9:  As an alternate solution, we can consider drawing the two diagonals of the square and observe the resulting geometric properties to arrive at the same conclusion.\\n\\nAnswer:\\n\\\\boxed{20\\\\%}"}]',
    'Question: where are the basilar membrane and the organ of corti located, Answer: Organ of Corti The organ of Corti is located in the scala media of the cochlea of the inner ear between the vestibular duct and the tympanic duct and is composed of mechanosensory cells, known as hair cells.[2] Strategically positioned on the basilar membrane of the organ of Corti are three rows of outer hair cells (OHCs) and one row of inner hair cells (IHCs).[4] Separating these hair cells are supporting cells: Deiters cells, also called phalangeal cells, which separate and support both the OHCs and the IHCs.[4]',
]
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: 113,450 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string int
details
min: 17 tokens
mean: 283.42 tokens
max: 512 tokens
min: 16 tokens
mean: 281.46 tokens
max: 512 tokens
0: ~80.00%
1: ~20.00%

Samples:

sentence_0	sentence_1	label
Question: where does rasin in the sun take place, Answer: A Raisin in the Sun A Raisin in the Sun is a play by Lorraine Hansberry that debuted on Broadway in 1959.[1] The title comes from the poem "Harlem" (also known as "A Dream Deferred"[2]) by Langston Hughes. The story tells of a black family's experiences in the Washington Park Subdivision of Chicago's Woodlawn neighborhood as they attempt to "better" themselves with an insurance payout following the death of the father. The New York Drama Critics' Circle named it the best play of 1959.	Question: where does rasin in the sun take place, Answer: A Raisin in the Sun A Raisin in the Sun is a play by Lorraine Hansberry that debuted on Broadway in 1959.[1] The title comes from the poem "Harlem" (also known as "A Dream Deferred"[2]) by Langston Hughes. The story tells of a black family's experiences in the Washington Park Subdivision of Chicago's Woodlawn neighborhood as they attempt to "better" themselves with an insurance payout following the death of the father. The New York Drama Critics' Circle named it the best play of 1959.	`1`
Question: when does the movie midnight sun come out, Answer: Midnight Sun (2018 film) Midnight Sun is a 2018 American romantic drama film directed by Scott Speer and written by Eric Kirsten, based on the 2006 Japanese film of the same name. The film stars Bella Thorne, Patrick Schwarzenegger, and Rob Riggle, and follows a teenage girl with the disease xeroderma pigmentosum, which prevents her from going out into sunlight. When she meets a boy, she struggles to decide whether to tell him about her condition or pretend to live a normal life. Principal photography began on October 12, 2015 in Vancouver, British Columbia, Canada. The film was released in the United States on March 23, 2018.	Question: who sang the song unchained melody in the movie ghost, Answer: Unchained Melody "Unchained Melody" reappeared on the Billboard charts in 1990 after The Righteous Brothers' recording was used in the box office blockbuster film Ghost. Two versions charted in the US that year â€“ the original and a new recording. According to Medley, he was interested in having the original recording released due to the renewed interest in the song, but was told that there were licensing issues. Although Hatfield was no longer in the same condition vocally as when he first recorded the song, they decided to re-record the song for Curb Records.[44] The re-recorded version was released as both a cassette single[45] and a CD single.[46] It received minimal airplay but recorded excellent sales, peaking at number 19. The re-recorded version was certified Platinum by the RIAA on January 10, 1991,[47] and received a Grammy Award nomination.[48]	`0`
`Question: where is the o2 arena in london located, Answer: The O2 Arena The O2 Arena (temporarily the sponsor neutral "North Greenwich Arena", during the 2012 Summer Olympics and 2012 Summer Paralympics), is a multi purpose indoor arena located in the centre of The O2 entertainment complex on the Greenwich Peninsula in south east London.`	`[{'from': 'human', 'value': "Explain how Archimedes' Principle enables objects to float in water."}, {'from': 'gpt', 'value': "Archimedes' Principle states that the buoyant force acting on an object submerged in water is equal to the weight of the water displaced by the object. If the buoyant force exceeds the weight of the object, it will float."}]`	`0`

Loss: ContrastiveTensionLoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 10
per_device_eval_batch_size: 10
num_train_epochs: 10
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 10
per_device_eval_batch_size: 10
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: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 10
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: 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: 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
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: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0441	500	10.4083
0.0881	1000	7.1707
0.1322	1500	5.1666
0.1763	2000	5.2289
0.2204	2500	5.1515
0.2644	3000	5.0737
0.3085	3500	5.0824
0.3526	4000	5.0569
0.3967	4500	5.2043
0.4407	5000	5.1293
0.4848	5500	5.1169
0.5289	6000	5.0743
0.5729	6500	5.1535
0.6170	7000	5.2208
0.6611	7500	5.1847
0.7052	8000	5.1545
0.7492	8500	5.0944
0.7933	9000	5.0662
0.8374	9500	5.1083
0.8814	10000	5.102
0.9255	10500	5.2018
0.9696	11000	5.1479
1.0137	11500	5.1877
1.0577	12000	5.2397
1.1018	12500	5.1657
1.1459	13000	5.2276
1.1900	13500	5.2295
1.2340	14000	5.1096
1.2781	14500	5.0877
1.3222	15000	5.0776
1.3662	15500	5.0957
1.4103	16000	5.1655
1.4544	16500	5.0537
1.4985	17000	5.0836
1.5425	17500	5.1495
1.5866	18000	5.0776
1.6307	18500	5.1215
1.6747	19000	5.1614
1.7188	19500	5.0195
1.7629	20000	5.0855
1.8070	20500	5.2153
1.8510	21000	5.1569
1.8951	21500	5.1213
1.9392	22000	5.1694
1.9833	22500	5.1414
2.0273	23000	5.1314
2.0714	23500	5.2454
2.1155	24000	5.1414
2.1595	24500	5.1194
2.2036	25000	5.1115
2.2477	25500	5.0393
2.2918	26000	5.1154
2.3358	26500	5.1414
2.3799	27000	5.0794
2.4240	27500	5.1013
2.4680	28000	5.1873
2.5121	28500	5.1034
2.5562	29000	5.1593
2.6003	29500	5.1933
2.6443	30000	5.2192
2.6884	30500	5.1233
2.7325	31000	5.1033
2.7766	31500	5.1408
2.8206	32000	5.1233
2.8647	32500	5.1153
2.9088	33000	5.0795
2.9528	33500	5.1792
2.9969	34000	5.1233
3.0410	34500	5.0852
3.0851	35000	5.0653
3.1291	35500	5.0973
3.1732	36000	5.1212
3.2173	36500	5.1046
3.2613	37000	5.1193
3.3054	37500	5.1632
3.3495	38000	5.1932
3.3936	38500	5.0894
3.4376	39000	5.1493
3.4817	39500	5.1812
3.5258	40000	5.1612
3.5699	40500	5.0972
3.6139	41000	5.0872
3.6580	41500	5.2375
3.7021	42000	5.1652
3.7461	42500	5.1273
3.7902	43000	5.0695
3.8343	43500	5.1557
3.8784	44000	5.0697
3.9224	44500	5.1635
3.9665	45000	5.2095
4.0106	45500	5.0936
4.0546	46000	5.1435
4.0987	46500	5.1635
4.1428	47000	5.1415
4.1869	47500	5.1634
4.2309	48000	5.1595
4.2750	48500	5.0775
4.3191	49000	5.0976
4.3632	49500	5.1135
4.4072	50000	5.0877
4.4513	50500	5.1934
4.4954	51000	5.0915
4.5394	51500	5.1295
4.5835	52000	5.0994
4.6276	52500	5.1235
4.6717	53000	5.1435
4.7157	53500	5.1335
4.7598	54000	5.1735
4.8039	54500	5.0914
4.8480	55000	5.1673
4.8920	55500	5.1834
4.9361	56000	5.1434
4.9802	56500	5.1713
5.0242	57000	5.1034
5.0683	57500	5.0615
5.1124	58000	5.0835
5.1565	58500	5.0654
5.2005	59000	5.1813
5.2446	59500	5.2133
5.2887	60000	5.2273
5.3327	60500	5.1313
5.3768	61000	5.1415
5.4209	61500	5.2432
5.4650	62000	5.0854
5.5090	62500	5.1415
5.5531	63000	5.0834
5.5972	63500	5.1294
5.6413	64000	5.1234
5.6853	64500	5.1913
5.7294	65000	5.1793
5.7735	65500	5.0614
5.8175	66000	5.1014
5.8616	66500	5.0354
5.9057	67000	5.1913
5.9498	67500	5.0614
5.9938	68000	5.2453
6.0379	68500	5.0913
6.0820	69000	5.1135
6.1260	69500	5.1374
6.1701	70000	5.0933
6.2142	70500	5.0134
6.2583	71000	5.0435
6.3023	71500	5.2412
6.3464	72000	5.0754
6.3905	72500	5.1933
6.4346	73000	5.2932
6.4786	73500	5.0515
6.5227	74000	5.2073
6.5668	74500	5.1312
6.6108	75000	5.1332
6.6549	75500	5.0615
6.6990	76000	5.1873
6.7431	76500	5.0654
6.7871	77000	5.1333
6.8312	77500	5.1593
6.8753	78000	5.1234
6.9193	78500	5.2012
6.9634	79000	5.1513
7.0075	79500	5.1153
7.0516	80000	5.0414
7.0956	80500	5.2034
7.1397	81000	5.1073
7.1838	81500	5.1753
7.2279	82000	5.1233
7.2719	82500	5.1293
7.3160	83000	5.1492
7.3601	83500	5.2392
7.4041	84000	5.0033
7.4482	84500	5.1912
7.4923	85000	5.1673
7.5364	85500	5.1932
7.5804	86000	5.1393
7.6245	86500	5.1653
7.6686	87000	5.1232
7.7126	87500	5.0993
7.7567	88000	5.1313
7.8008	88500	5.0695
7.8449	89000	5.1493
7.8889	89500	5.1733
7.9330	90000	5.1233
7.9771	90500	5.1292
8.0212	91000	5.0254
8.0652	91500	5.2092
8.1093	92000	5.1313
8.1534	92500	5.0834
8.1974	93000	5.1032
8.2415	93500	5.0653
8.2856	94000	5.1832
8.3297	94500	5.1752
8.3737	95000	5.0752
8.4178	95500	5.1593
8.4619	96000	5.1633
8.5059	96500	5.0794
8.5500	97000	5.1991
8.5941	97500	5.0952
8.6382	98000	5.1153
8.6822	98500	5.1192
8.7263	99000	5.1053
8.7704	99500	5.2213
8.8145	100000	5.1571
8.8585	100500	5.1292
8.9026	101000	5.1531
8.9467	101500	5.2251
8.9907	102000	5.0733
9.0348	102500	5.1313
9.0789	103000	5.1973
9.1230	103500	5.1133
9.1670	104000	5.1813
9.2111	104500	5.2472
9.2552	105000	5.1352
9.2993	105500	5.0373
9.3433	106000	5.1791
9.3874	106500	5.1891
9.4315	107000	5.0832
9.4755	107500	5.1632
9.5196	108000	5.1012
9.5637	108500	5.0354
9.6078	109000	5.2112
9.6518	109500	5.1212
9.6959	110000	5.1212
9.7400	110500	5.1612
9.7840	111000	5.1613
9.8281	111500	5.1072
9.8722	112000	5.0852
9.9163	112500	5.0234
9.9603	113000	5.1033

Framework Versions

Python: 3.12.8
Sentence Transformers: 3.4.1
Transformers: 4.52.4
PyTorch: 2.6.0+cu126
Accelerate: 1.3.0
Datasets: 3.2.0
Tokenizers: 0.21.0

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",
}

ContrastiveTensionLoss

bibtex

@inproceedings{carlsson2021semantic,
    title={Semantic Re-tuning with Contrastive Tension},
    author={Fredrik Carlsson and Amaru Cuba Gyllensten and Evangelia Gogoulou and Erik Ylip{"a}{"a} Hellqvist and Magnus Sahlgren},
    booktitle={International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=Ov_sMNau-PF}
}

	sentence_0	sentence_1	label
type	string	string	int
details	min: 17 tokens mean: 283.42 tokens max: 512 tokens	min: 16 tokens mean: 281.46 tokens max: 512 tokens	0: ~80.00% 1: ~20.00%