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SentenceTransformer based on osllmai/bge-large_osllmai_V1.6

This is a sentence-transformers model finetuned from osllmai/bge-large_osllmai_V1.6. 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: osllmai/bge-large_osllmai_V1.6
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 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': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Early this year, there was a buzz on Motorola inviting T-Mobile Moto X owners to take part in a soak test for possible future update. Motorola seemed skeptical in disclosing facts at that point of time but since Moto G was recently upgraded to Android 4.4.2; enthusiasts anticipated the same for T-Mobile Moto X. And it turned out to be true.\r\nNews Update\r\nThis T-Mobile version of Moto X is now receiving the upgrade which is a file size of 147.6 MB. The Android 4.4.2 is the latest version of KitKat that includes all the goodies from the earlier installments, plus a few additions. The good news is, Motorola has customized the whole package and made a few tweaks into the update. The Software Version bumped to 161.44.25 and the notable changes are listed as below:\r\n- It added substantial support for services like printing photos, Google Docs, Gmail messages and other such content via Wi-Fi, Bluetooth and hosted services such as HP ePrinters and Google Cloud Print.\r\n- It fixed all the bugs identified during the preliminary runs, including the ones that caused a few users to experience short battery life after upgrading to KitKat.\r\n- Another bug that caused delays in synchronizing email services like Microsoft Exchange was resolved, thus adding to the convenience of the user.\r\nThis is a noteworthy upgrade, considering the fact that bugs and errors were fixed. Mobile addicts across the world will rejoice, for they can experience the smartness of Android KitKat flawlessly in their devices. This is significant development in terms of update.\r\nThis variant is an unlocked GSM device so chances are, you can use it on networks of other service providers. In all probability, the update should not be affected and the installation should hardly take much time. The T-Mobile Moto X Android update is now available for manual download. It is accessible in the following sequential way:\r\n- Click on Settings\r\n- Click on About Phone\r\n- Click on System Updates\r\n- Click on Download\r\nRecommendations\r\nFor ensuring a successful installation, it is highly recommended to install this update with at least 50% battery and a strong connectivity; preferably Wi-Fi. Follow the notification message and select download-> once the download is over, select Install-> Once the installation is over, and the phone will automatically restart. This marks the completion of the installation process. The phone is now updated to 161.44.25 – This build is same as the soak test.\r\nThis upgrade is free in the carrier network and Motorola and Google has collaborated for a back up service for those in trouble. In case of distress, a user can contact them through the Moto X web interface and avail the service. There is still no news on other carrier variants of this update but we can safely hope that it will roll out very soon. Though the upgrade doesn’t appeal in terms of version number but it is definitely significant for users to live with the latest KitKat.',
    'What are some of the notable changes in the T-Mobile Moto X update?',
    'What are the common features of the Quince Hand Lotion as described by the reviewers?',
]
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]

Training Details

Training Dataset

Unnamed Dataset

• Size: 9,598 training samples
• Columns: positive and anchor
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	min: 232 tokens mean: 493.76 tokens max: 512 tokens	min: 11 tokens mean: 26.8 tokens max: 67 tokens

• Samples:

  positive	anchor
  Caption: Tasmanian berry grower Nic Hansen showing Macau chef Antimo Merone around his property as part of export engagement activities. THE RISE and rise of the Australian strawberry, raspberry and blackberry industries has seen the sectors redouble their international trade focus, with the release of a dedicated export plan to grow their global presence over the next 10 years. Driven by significant grower input, the Berry Export Summary 2028 maps the sectors’ current position, where they want to be, high-opportunity markets and next steps. Hort Innovation trade manager Jenny Van de Meeberg said the value and volume of raspberry and blackberry exports rose by 100 per cent between 2016 and 2017. She said the Australian strawberry industry experienced similar success with an almost 30 per cent rise in export volume and a 26 per cent rise in value to $32.6M over the same period. “Australian berry sectors are in a firm position at the moment,” she said. “Production, adoption of protected ...	What is the Berry Export Summary 2028 and what is its purpose?
  RWSN Collaborations Southern Africa Self-supply Study Review of Self-supply and its support services in African countries A lady in Zimbabwe proudly shows off her onions - watered from her self-supply well © 2015 André Olschewski • Skat Project starts: 2015 Project finished: 2016 Collaborators & Partners:. Project Description UNICEF and Skat have collaborated on a). Perspectives Reach and benefits: - Self-supply is practised by millions of rural households in Sub-Sahara Africa as well as in Europe, USA and other areas of the world. - Benefits reported from having access to Self-supply water sources include convenience, less time spent for fetching water and access to more and better quality water. In some areas, Self-supply sources offer important added values such as water for productive use, income generation, family safety and improved food security. - Sustainability of services from Self-supply is high as there is strong ownership by people investing in own sources. - As Self-suppl...	What are some of the benefits reported from having access to Self-supply water sources?
  All Android applications categories Description Coolands for Twitter is a revolutionary twitter client. It has many unique features, gives you the best mobile twitter experience you never imagined before. The first unique feature is Real-Time. You can’t find any refresh button in this app, because you absolutely don’t need to. Every time you open it, you’ll get the latest tweets and while you’re reading, you’ll get incoming tweets in Real-Time. So if your friend mentioned you, you can reply instantly. The second unique feature is Avatar Indicator. Avatar Indicator is small avatars showed on the title bar to indicate that you’ve got new message/tweet/mention. Since it’s real-time, you’ll keep getting incoming tweets while you’re reading your older timeline, Avatar-Indicator will let you know who’s tweet you’ve just got, and decide whether to check it out right away. The third unique feature is Direct Link I think it is obviously the most intuitive and convenient way to open a link. When...	What are the unique features of the Coolands for Twitter app?

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

• Size: 500 evaluation samples
• Columns: positive and anchor
• Approximate statistics based on the first 500 samples:

  	positive	anchor
  type	string	string
  details	min: 247 tokens mean: 492.12 tokens max: 512 tokens	min: 11 tokens mean: 27.28 tokens max: 54 tokens

• Samples:

  positive	anchor
  Perhaps Not such a Good Idea
  I have found trying to run a blog is very time-consuming, and there are other calls on my time. I think it has been demonstrated that if enough people are unable to self-moderate, the nuggets of interest are swamped by the rubbish. Sadly I agree with Mark Frank's assessment. I had hoped more thread topics would be proposed nothing has been suggested by anyone for a while.
  My personal view is that, considering DaveScot's generally perceived blog persona, I have to admit that he hasn't been (on this site) quite the unmitigated disaster predicted. John Davison, on the other hand has conformed perfectly to predictions, which is a shame, but his choice.
  I am happy to let things run for a while, but would like to hear from anyone who has a suggestion for a thread topic. Post here or in the suggestions thread
  23 comments:
  How about an "ID: show me the research" thread?
  OK Rich, put some meat on the bones and I'll paste it.
  Of course I have. I have no respec...	What is the author's personal view on DaveScot's blog persona?
  Age reduction Academic atmosphere Beef tendon bottom Straight buckle low-heel cowhide Lefu shoes Mary Jane shoes Spring and summer Women's shoes 0.73
  ins Chaopai shoes Women's Shoes Academic atmosphere Versatile Graffiti Frenulum gym shoes Harajuku leisure time Hip hop jointly skate shoes
  Air force one Men's shoes Low Gang summer skate shoes student Korean version Versatile leisure time gym shoes female Reflection Little white shoes
  autumn Clover ozweego Daddy shoes Jackson Yi Same men and women Reflection motion Running shoes EE6999
  Retro Britain Square head Frenulum Color matching motion Casual shoes 2021 new pattern Versatile Flat bottom Elastic band Little white shoes female
  Thick bottom British style Small leather shoes Women's shoes 2021 new pattern Big square head Spring and Autumn Lefu Autumn shoes black Single shoes
  U.S.A quality goods Jeffrey Campbell temperament crude high-heeled dollskill Buckles Low top shoes female widow
  quality goods Clover ozweego Black Warrior D...	What type of shoes are mentioned as being suitable for both men and women?
  I just started a new blog on my ultralight gear. My gear list in all it's glory is located on: each item of gear, I'm writing an in-depth review for the item and how we have used it. Would love to get feedback and the site and our gear and/or comments from people on how we can fine tune.Currently my wifes pack is 7.5 lbs base weight, and mine is 10.5 lbs.Thanks!-Brett
  Edited by brettmarl on 09/09/2006 15:59:48 MDT.
  Brett, Your BLOG looks good.You should put the size of your items where their is one such as pants, shoes, jacket etc. There is a golf like "handicap' for anyone that wears larger then size medium or size 9.5 shoe. Sure.I think you might recheck some of your math. Not sure but some totals look low. Don't trust the posted weightof gear, weigh it yourself if you haven't.Why is your pack list so heavy?
  I agree, nice looking blog. Bill is right on listing the sizes, other than that....looks great!
  Brett - nice list, and nice format!(One small typo: it currently says "Cloudbu...	What are the base weights of the blogger's and his wife's packs?

• 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: 10
• per_device_eval_batch_size: 10
• num_train_epochs: 30
• warmup_ratio: 0.1
• bf16: 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: 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: 1.0
• num_train_epochs: 30
• 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: 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: 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
• 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

Step	Training Loss	Validation Loss
100	No log	0.077932
200	No log	0.060976
300	No log	0.069869
400	No log	0.049215
500	0.129900	0.050496
600	0.129900	0.054146
700	0.129900	0.058882
800	0.129900	0.059746
900	0.129900	0.063642
1000	0.102800	0.075839
1100	0.102800	0.062941
1200	0.102800	0.066200
1300	0.102800	0.090931
1400	0.102800	0.109871
1500	0.091000	0.107309
1600	0.091000	0.113213
1700	0.091000	0.098840
1800	0.091000	0.117776
1900	0.091000	0.099684
2000	0.114700	0.099308
2100	0.114700	0.095418
2200	0.114700	0.121110
2300	0.114700	0.103965
2400	0.114700	0.151238
2500	0.083200	0.169087
2600	0.083200	0.164672
2700	0.083200	0.196103
2800	0.083200	0.200677
2900	0.083200	0.145300
3000	0.115000	0.171855
3100	0.115000	0.192693
3200	0.115000	0.206315
3300	0.115000	0.154698
3400	0.115000	0.133345
3500	0.095400	0.163068
3600	0.095400	0.211607
3700	0.095400	0.181887
3800	0.095400	0.179725
3900	0.095400	0.164818
4000	0.082300	0.207933
4100	0.082300	0.197499
4200	0.082300	0.248613
4300	0.082300	0.177535
4400	0.082300	0.170462
4500	0.054900	0.229082
4600	0.054900	0.206981
4700	0.054900	0.205012
4800	0.054900	0.183472
4900	0.054900	0.174745
5000	0.051800	0.211212
5100	0.051800	0.202875
5200	0.051800	0.209989
5300	0.051800	0.217802
5400	0.051800	0.207275
5500	0.036300	0.166451
5600	0.036300	0.185850
5700	0.036300	0.241831
5800	0.036300	0.220541
5900	0.036300	0.174495
6000	0.030500	0.204093
6100	0.030500	0.230805
6200	0.030500	0.234512
6300	0.030500	0.180759
6400	0.030500	0.210566
6500	0.022100	0.207234
6600	0.022100	0.231294
6700	0.022100	0.217805
6800	0.022100	0.196253
6900	0.022100	0.232458
7000	0.019100	0.206829
7100	0.019100	0.278143
7200	0.019100	0.233788
7300	0.019100	0.207704
7400	0.019100	0.193656
7500	0.018500	0.221518
7600	0.018500	0.185376
7700	0.018500	0.212624
7800	0.018500	0.243344
7900	0.018500	0.249413
8000	0.015200	0.189084
8100	0.015200	0.215418
8200	0.015200	0.175985
8300	0.015200	0.192763
8400	0.015200	0.276208
8500	0.013100	0.219308
8600	0.013100	0.252156
8700	0.013100	0.257007
8800	0.013100	0.285778
8900	0.013100	0.244496
9000	0.010900	0.251443
9100	0.010900	0.254057
9200	0.010900	0.249420
9300	0.010900	0.259040
9400	0.010900	0.216327
9500	0.011700	0.227780
9600	0.011700	0.250833
9700	0.011700	0.278442
9800	0.011700	0.265544
9900	0.011700	0.272136
10000	010000	0.208672
10100 0.010000	0.218282
10200	0.010000	0.173614
10300	0.010000	0.239000
10400	0.010000	0.211169
10500	0.007900	0.223127
10600	0.007900	0.193511
10700	0.007900	0.224293
10800	0.007900	0.276138
10900	0.007900	0.212863
11000	0.007600	0.168702
11100	0.007600	0.244286
11200	0.007600	0.242311
11300	0.007600	0.292155
11400	0.007600	0.224811
11500	0.007400	0.212932
11600	0.007400	0.232738
11700	0.007400	0.237509
11800	0.007400	0.269904
11900	0.007400	0.247419
12000	0.005600	0.227091
12100	0.005600	0.240582
12200	0.005600	0.288775
12300	0.005600	0.317762
12400	0.005600	0.289674
12500	0.006800	0.269095
12600	0.006800	0.280439
12700	0.006800	0.270056
12800	0.006800	0.286388
12900	0.006800	0.310388
13000	0.005300	0.295646
13100	0.005300	0.247113
13200	0.005300	0.253593
13300	0.005300	0.237059
13400	0.005300	0.235936
13500	0.004900	0.246602
13600	0.004900	0.270857
13700	0.004900	0.237764
13800	0.004900	0.225527
13900	0.004900	0.257233
14000	0.003400	0.242815
14100	0.003400	0.285892
14200	0.003400	0.239855
14300	0.003400	0.265451
14400	0.003400	0.243612
14500	0.002500	0.225554
14600	0.002500	0.236780
14700	0.002500	0.245257
14800	0.002500	0.238916
14900	0.002500	0.258283
15000	0.002800	0.215534
15100	0.002800	0.187369
15200	0.002800	0.234369
15300	0.002800	0.213728
15400	0.002800	0.281290
15500	0.003800	0.241194
15600	0.003800	0.273098
15700	0.003800	0.266355
15800	0.003800	0.276067
15900	0.003800	0.256065
16000	0.002400	0.317020
16100	0.002400	0.230963
16200	0.002400	0.280397
16300	0.002400	0.267816
16400	0.002400	0.278919
16500	0.002100	0.242398
16600	0.002100	0.260493
16700	0.002100	0.306106
16800	0.002100	0.262822
16900	0.002100	0.249681
17000	0.003300	0.271645
17100	0.003300	0.269828
17200	0.003300	0.267099
17300	0.003300	0.249909
17400	0.003300	0.291281
17500	0.003000	0.268641
17600	0.003000	0.256109
17700	0.003000	0.275269
17800	0.003000	0.259269
17900	0.003000	0.269805
18000	0.002300	0.221830
18100	0.002300	0.245713
18200	0.002300	0.234901
18300	0.002300	0.279927
18400	0.002300	0.295232
18500	0.001800	0.233305
18600	0.001800	0.261527
18700	0.001800	0.268007
18800	0.001800	0.219146
18900	0.001800	0.263330
19000	0.001900	0.227705
19100	0.001900	0.261595
19200	0.001900	0.238764
19300	0.001900	0.229096
19400	0.001900	0.232315
19500	0.001300	0.222621
19600	0.001300	0.240302
19700	0.001300	0.241781
19800	0.001300	0.269149
19900	0.001300	0.219018
20000	0.001400	0.248050
20100	0.001400	0.229688
20200	0.001400	0.239501
20300	0.001400	0.268694
20400	0.001400	0.274840
20500	0.001300	0.248240
20600	0.001300	0.257988
20700	0.001300	0.250519
20800	0.001300	0.245723
20900	0.001300	0.261572
21000	0.001200	0.246133
21100	0.001200	0.229774
21200	0.001200	0.208827
21300	0.001200	0.243179
21400	0.001200	0.240906
21500	0.000600	0.239661
21600	0.000600	0.244203
21700	0.000600	0.261082
21800	0.000600	0.241161
21900	0.000600	0.281254
22000	0.001600	0.289732
22100	0.001600	0.250815
22200	0.001600	0.274771
22300	0.001600	0.246459
22400	0.001600	0.224530
22500	0.001200	0.257518
22600	0.001200	0.254099
22700	0.001200	0.264190
22800	0.001200	0.272964
22900	0.001200	0.281039
23000	0.000900	0.294881
23100	0.000900	0.264351
23200	0.000900	0.283872
23300	0.000900	0.284469
23400	0.000900	0.243716
23500	0.001200	0.252970
23600	0.001200	0.235496
23700	0.001200	0.246921
23800	0.001200	0.259446
23900	0.001200	0.256852
24000	0.000600	0.239791
24100	0.000600	0.251410
24200	0.000600	0.253499
24300	0.000600	0.216110
24400	0.000600	0.228419
24500	0.000500	0.231425
24600	0.000500	0.222847
24700	0.000500	0.233325
24800	0.000500	0.230961
24900	0.000500	0.223775
25000	0.001500	0.229884
25100	0.001500	0.224879
25200	0.001500	0.215886
25300	0.001500	0.229122
25400	0.001500	0.243697
25500	0.000900	0.245515
25600	0.000900	0.232157
25700	0.000900	0.237162
25800	0.000900	0.244510
25900	0.000900	0.248212
26000	0.000400	0.239014
26100	0.000400	0.244146
26200	0.000400	0.228095
26300	0.000400	0.230700
26400	0.000400	0.227404
26500	0.000300	0.232739
26600	0.000300	0.246335
26700	0.000300	0.241653
26800	0.000300	0.248361
26900	0.000300	0.252419
27000	0.000500	0.249153
27100	0.000500	0.246641
27200	0.000500	0.237843
27300	0.000500	0.237281
27400	0.000500	0.235281
27500	0.000700	0.220346
27600	0.000700	0.220535
27700	0.000700	0.219589
27800	0.000700	0.222483
27900	0.000700	0.225659
28000	0.000100	0.228401
28100	0.000100	0.227247
28200	0.000100	0.232755
28300	0.000100	0.232288
28400	0.000100	0.232112
28500	0.000700	0.231037
28600	0.000700	0.231302
28700	0.000700	0.231330
28800	0.000700	0.230988

Framework Versions

• Python: 3.10.16
• Sentence Transformers: 3.4.1
• Transformers: 4.49.0
• PyTorch: 2.6.0+cu126
• Accelerate: 1.4.0
• Datasets: 3.3.2
• Tokenizers: 0.21.0

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