GozdeA/tennis-multi-return-categorizer-v1

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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-MiniLM-L6-v2
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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("GozdeA/tennis-multi-return-categorizer-v1")
# Run inference
sentences = [
    'What is the overall return for Sinner?',
    'Djokovic how many titles?',
    'Tell me about sets won for Sinner',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5331, 0.0219],
#         [0.5331, 1.0000, 0.1391],
#         [0.0219, 0.1391, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

• Size: 73,579 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 10.03 tokens max: 26 tokens	min: 4 tokens mean: 8.91 tokens max: 23 tokens	min: 4 tokens mean: 9.87 tokens max: 22 tokens

• Samples:

  anchor	positive	negative
  What is the start time for Swiatek?	Djokovic what court?	before the matchup
  What is the backhand this set for the player?	Djokovic key factors?	What about she's duration?
  the player how many titles?	Show me career titles	What about Sinner's games?

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

• Size: 18,395 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 9.93 tokens max: 23 tokens	min: 4 tokens mean: 8.89 tokens max: 20 tokens	min: 4 tokens mean: 9.91 tokens max: 20 tokens

• Samples:

  anchor	positive	negative
  How is Sinner's previous?	what venue	What about the player's fault?
  likely for Shelton?	likely for Nole?	title for Shelton?
  What is the who is she for Djokovic?	What's the who is she for Djokovic?	she who is projected?

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• learning_rate: 2e-05
• num_train_epochs: 15
• warmup_ratio: 0.1
• fp16: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 8
• 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: 2e-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: 15
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• 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
• 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}
• 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}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• 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
• hub_revision: None
• 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: no
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: True
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0109	50	4.5343
0.0217	100	4.1503
0.0326	150	4.2094
0.0435	200	3.7119
0.0544	250	3.4992
0.0652	300	3.2812
0.0761	350	2.875
0.0870	400	2.6036
0.0978	450	2.3237
0.1087	500	2.0771
0.1196	550	2.0357
0.1305	600	1.9121
0.1413	650	1.722
0.1522	700	1.6555
0.1631	750	1.5444
0.1740	800	1.6782
0.1848	850	1.4761
0.1957	900	1.4483
0.2066	950	1.3928
0.2174	1000	1.3547
0.2283	1050	1.2807
0.2392	1100	1.214
0.2501	1150	1.2233
0.2609	1200	1.1758
0.2718	1250	1.2455
0.2827	1300	1.1887
0.2935	1350	1.0793
0.3044	1400	1.1442
0.3153	1450	1.0647
0.3262	1500	1.127
0.3370	1550	1.0336
0.3479	1600	0.9882
0.3588	1650	1.084
0.3696	1700	0.9635
0.3805	1750	1.0175
0.3914	1800	1.0337
0.4023	1850	0.9214
0.4131	1900	0.8977
0.4240	1950	0.8724
0.4349	2000	0.9128
0.4457	2050	0.8351
0.4566	2100	0.8709
0.4675	2150	0.8714
0.4784	2200	0.8228
0.4892	2250	0.8768
0.5001	2300	0.8204
0.5110	2350	0.7917
0.5219	2400	0.8571
0.5327	2450	0.7727
0.5436	2500	0.7949
0.5545	2550	0.7218
0.5653	2600	0.7796
0.5762	2650	0.7779
0.5871	2700	0.708
0.5980	2750	0.6822
0.6088	2800	0.7267
0.6197	2850	0.7874
0.6306	2900	0.7183
0.6414	2950	0.7872
0.6523	3000	0.6798
0.6632	3050	0.6589
0.6741	3100	0.7869
0.6849	3150	0.7458
0.6958	3200	0.6518
0.7067	3250	0.6666
0.7175	3300	0.7073
0.7284	3350	0.6737
0.7393	3400	0.6933
0.7502	3450	0.6869
0.7610	3500	0.6713
0.7719	3550	0.6525
0.7828	3600	0.6384
0.7937	3650	0.6467
0.8045	3700	0.5862
0.8154	3750	0.5869
0.8263	3800	0.6548
0.8371	3850	0.6605
0.8480	3900	0.639
0.8589	3950	0.5724
0.8698	4000	0.5488
0.8806	4050	0.6698
0.8915	4100	0.6038
0.9024	4150	0.5981
0.9132	4200	0.6082
0.9241	4250	0.6197
0.9350	4300	0.5462
0.9459	4350	0.6771
0.9567	4400	0.5428
0.9676	4450	0.6265
0.9785	4500	0.5621
0.9893	4550	0.5917
1.0002	4600	0.5517
1.0111	4650	0.5733
1.0220	4700	0.5907
1.0328	4750	0.51
1.0437	4800	0.5592
1.0546	4850	0.5688
1.0654	4900	0.5571
1.0763	4950	0.531
1.0872	5000	0.5062
1.0981	5050	0.5626
1.1089	5100	0.55
1.1198	5150	0.5727
1.1307	5200	0.5253
1.1416	5250	0.5174
1.1524	5300	0.5883
1.1633	5350	0.5333
1.1742	5400	0.5204
1.1850	5450	0.4964
1.1959	5500	0.5192
1.2068	5550	0.5264
1.2177	5600	0.5388
1.2285	5650	0.5505
1.2394	5700	0.5008
1.2503	5750	0.4952
1.2611	5800	0.5656
1.2720	5850	0.5574
1.2829	5900	0.4516
1.2938	5950	0.5438
1.3046	6000	0.48
1.3155	6050	0.5645
1.3264	6100	0.5652
1.3372	6150	0.4538
1.3481	6200	0.522
1.3590	6250	0.5153
1.3699	6300	0.5149
1.3807	6350	0.606
1.3916	6400	0.5366
1.4025	6450	0.4846
1.4134	6500	0.5462
1.4242	6550	0.4505
1.4351	6600	0.4648
1.4460	6650	0.5468
1.4568	6700	0.4822
1.4677	6750	0.5271
1.4786	6800	0.5222
1.4895	6850	0.4843
1.5003	6900	0.4755
1.5112	6950	0.5517
1.5221	7000	0.4793
1.5329	7050	0.5232
1.5438	7100	0.5481
1.5547	7150	0.5477
1.5656	7200	0.5007
1.5764	7250	0.4048
1.5873	7300	0.5295
1.5982	7350	0.4564
1.6090	7400	0.5618
1.6199	7450	0.5855
1.6308	7500	0.5319
1.6417	7550	0.5128
1.6525	7600	0.4669
1.6634	7650	0.4961
1.6743	7700	0.4905
1.6851	7750	0.4959
1.6960	7800	0.4981
1.7069	7850	0.4973
1.7178	7900	0.5029
1.7286	7950	0.5397
1.7395	8000	0.4351
1.7504	8050	0.4897
1.7613	8100	0.4901
1.7721	8150	0.501
1.7830	8200	0.4701
1.7939	8250	0.4508
1.8047	8300	0.4612
1.8156	8350	0.5318
1.8265	8400	0.4846
1.8374	8450	0.4965
1.8482	8500	0.4872
1.8591	8550	0.4902
1.8700	8600	0.4552
1.8808	8650	0.4687
1.8917	8700	0.4839
1.9026	8750	0.4549
1.9135	8800	0.445
1.9243	8850	0.436
1.9352	8900	0.4577
1.9461	8950	0.4301
1.9569	9000	0.5138
1.9678	9050	0.5057
1.9787	9100	0.4725
1.9896	9150	0.4283
2.0004	9200	0.4934
2.0113	9250	0.5033
2.0222	9300	0.4393
2.0331	9350	0.451
2.0439	9400	0.439
2.0548	9450	0.4064
2.0657	9500	0.4708
2.0765	9550	0.4132
2.0874	9600	0.4464
2.0983	9650	0.4531
2.1092	9700	0.4429
2.1200	9750	0.4251
2.1309	9800	0.45
2.1418	9850	0.4252
2.1526	9900	0.424
2.1635	9950	0.4899
2.1744	10000	0.4602
2.1853	10050	0.4976
2.1961	10100	0.4161
2.2070	10150	0.4652
2.2179	10200	0.444
2.2287	10250	0.472
2.2396	10300	0.4657
2.2505	10350	0.4483
2.2614	10400	0.5059
2.2722	10450	0.4887
2.2831	10500	0.4583
2.2940	10550	0.4551
2.3048	10600	0.4353
2.3157	10650	0.4883
2.3266	10700	0.4683
2.3375	10750	0.4461
2.3483	10800	0.4323
2.3592	10850	0.4779
2.3701	10900	0.3794
2.3810	10950	0.4247
2.3918	11000	0.4223
2.4027	11050	0.4325
2.4136	11100	0.3852
2.4244	11150	0.4424
2.4353	11200	0.4614
2.4462	11250	0.5371
2.4571	11300	0.4411
2.4679	11350	0.4248
2.4788	11400	0.4675
2.4897	11450	0.4442
2.5005	11500	0.4382
2.5114	11550	0.45
2.5223	11600	0.3965
2.5332	11650	0.4243
2.5440	11700	0.5324
2.5549	11750	0.4558
2.5658	11800	0.4677
2.5766	11850	0.4307
2.5875	11900	0.4344
2.5984	11950	0.4066
2.6093	12000	0.4063
2.6201	12050	0.4823
2.6310	12100	0.4009
2.6419	12150	0.3996
2.6528	12200	0.4401
2.6636	12250	0.4244
2.6745	12300	0.4074
2.6854	12350	0.4391
2.6962	12400	0.4452
2.7071	12450	0.4893
2.7180	12500	0.4644
2.7289	12550	0.4626
2.7397	12600	0.4329
2.7506	12650	0.4706
2.7615	12700	0.4076
2.7723	12750	0.4258
2.7832	12800	0.4746
2.7941	12850	0.4445
2.8050	12900	0.3991
2.8158	12950	0.4463
2.8267	13000	0.5408
2.8376	13050	0.4755
2.8484	13100	0.4352
2.8593	13150	0.4397
2.8702	13200	0.4313
2.8811	13250	0.4292
2.8919	13300	0.4706
2.9028	13350	0.44
2.9137	13400	0.4608
2.9245	13450	0.4115
2.9354	13500	0.4301
2.9463	13550	0.3949
2.9572	13600	0.5413
2.9680	13650	0.4923
2.9789	13700	0.4789
2.9898	13750	0.4517
3.0007	13800	0.4442
3.0115	13850	0.4024
3.0224	13900	0.4693
3.0333	13950	0.3928
3.0441	14000	0.4171
3.0550	14050	0.4563
3.0659	14100	0.4822
3.0768	14150	0.3919
3.0876	14200	0.4311
3.0985	14250	0.4678
3.1094	14300	0.4385
3.1202	14350	0.4603
3.1311	14400	0.3592
3.1420	14450	0.4371
3.1529	14500	0.4543
3.1637	14550	0.4129
3.1746	14600	0.482
3.1855	14650	0.4003
3.1963	14700	0.4369
3.2072	14750	0.4284
3.2181	14800	0.4054
3.2290	14850	0.4646
3.2398	14900	0.4694
3.2507	14950	0.4373
3.2616	15000	0.4242
3.2725	15050	0.3831
3.2833	15100	0.4368
3.2942	15150	0.3969
3.3051	15200	0.4054
3.3159	15250	0.4599
3.3268	15300	0.4339
3.3377	15350	0.4139
3.3486	15400	0.3776
3.3594	15450	0.382
3.3703	15500	0.3721
3.3812	15550	0.4027
3.3920	15600	0.4055
3.4029	15650	0.4425
3.4138	15700	0.4547
3.4247	15750	0.4262
3.4355	15800	0.4254
3.4464	15850	0.4351
3.4573	15900	0.4512
3.4681	15950	0.4176
3.4790	16000	0.4309
3.4899	16050	0.4769
3.5008	16100	0.4066
3.5116	16150	0.4299
3.5225	16200	0.4656
3.5334	16250	0.3952
3.5442	16300	0.4916
3.5551	16350	0.4299
3.5660	16400	0.4113
3.5769	16450	0.3327
3.5877	16500	0.3846
3.5986	16550	0.4026
3.6095	16600	0.4467
3.6204	16650	0.4034
3.6312	16700	0.4372
3.6421	16750	0.3998
3.6530	16800	0.4125
3.6638	16850	0.4402
3.6747	16900	0.4505
3.6856	16950	0.4204
3.6965	17000	0.4321
3.7073	17050	0.4538
3.7182	17100	0.4095
3.7291	17150	0.4361
3.7399	17200	0.3658
3.7508	17250	0.4158
3.7617	17300	0.4394
3.7726	17350	0.4329
3.7834	17400	0.4599
3.7943	17450	0.4091
3.8052	17500	0.404
3.8160	17550	0.4532
3.8269	17600	0.4591
3.8378	17650	0.4178
3.8487	17700	0.4236
3.8595	17750	0.4122
3.8704	17800	0.404
3.8813	17850	0.4057
3.8922	17900	0.4169
3.9030	17950	0.4668
3.9139	18000	0.4186
3.9248	18050	0.3874
3.9356	18100	0.4644
3.9465	18150	0.3788
3.9574	18200	0.4308
3.9683	18250	0.4466
3.9791	18300	0.434
3.9900	18350	0.4317
4.0009	18400	0.3846
4.0117	18450	0.4284
4.0226	18500	0.3853
4.0335	18550	0.4083
4.0444	18600	0.3601
4.0552	18650	0.4309
4.0661	18700	0.4503
4.0770	18750	0.3978
4.0878	18800	0.4455
4.0987	18850	0.4662
4.1096	18900	0.3975
4.1205	18950	0.388
4.1313	19000	0.4246
4.1422	19050	0.3963
4.1531	19100	0.38
4.1639	19150	0.3699
4.1748	19200	0.4176
4.1857	19250	0.4139
4.1966	19300	0.439
4.2074	19350	0.4259
4.2183	19400	0.4135
4.2292	19450	0.4516
4.2401	19500	0.3861
4.2509	19550	0.3929
4.2618	19600	0.3653
4.2727	19650	0.4113
4.2835	19700	0.422
4.2944	19750	0.3864
4.3053	19800	0.4171
4.3162	19850	0.4439
4.3270	19900	0.369
4.3379	19950	0.3967
4.3488	20000	0.423
4.3596	20050	0.402
4.3705	20100	0.4588
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10.7523	49450	0.3852
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10.7741	49550	0.3784
10.7850	49600	0.3926
10.7958	49650	0.3469
10.8067	49700	0.3381
10.8176	49750	0.3771
10.8284	49800	0.4124
10.8393	49850	0.3118
10.8502	49900	0.3945
10.8611	49950	0.4245
10.8719	50000	0.3823
10.8828	50050	0.3773
10.8937	50100	0.4237
10.9045	50150	0.4174
10.9154	50200	0.3625
10.9263	50250	0.4409
10.9372	50300	0.3334
10.9480	50350	0.3782
10.9589	50400	0.3437
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10.9806	50500	0.3625
10.9915	50550	0.3544
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11.4264	52550	0.3971
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11.4590	52700	0.3455
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11.5025	52900	0.3997
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11.5242	53000	0.3563
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11.5460	53100	0.3628
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11.5786	53250	0.3493
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14.3944	66200	0.3675
14.4053	66250	0.3535
14.4162	66300	0.388
14.4270	66350	0.3759
14.4379	66400	0.3682
14.4488	66450	0.3214
14.4597	66500	0.3846
14.4705	66550	0.3921
14.4814	66600	0.3948
14.4923	66650	0.3609
14.5032	66700	0.3709
14.5140	66750	0.4154
14.5249	66800	0.3576
14.5358	66850	0.3397
14.5466	66900	0.3459
14.5575	66950	0.3908
14.5684	67000	0.4457
14.5793	67050	0.3727
14.5901	67100	0.312
14.6010	67150	0.3946
14.6119	67200	0.3626
14.6227	67250	0.3639
14.6336	67300	0.3261
14.6445	67350	0.3779
14.6554	67400	0.393
14.6662	67450	0.3687
14.6771	67500	0.3772
14.6880	67550	0.3954
14.6988	67600	0.3565
14.7097	67650	0.375
14.7206	67700	0.3815
14.7315	67750	0.3516
14.7423	67800	0.4417
14.7532	67850	0.4151
14.7641	67900	0.4171
14.7750	67950	0.3401
14.7858	68000	0.351
14.7967	68050	0.4041
14.8076	68100	0.3528
14.8184	68150	0.3397
14.8293	68200	0.3897
14.8402	68250	0.3917
14.8511	68300	0.3718
14.8619	68350	0.3762
14.8728	68400	0.3391
14.8837	68450	0.3603
14.8945	68500	0.3861
14.9054	68550	0.3658
14.9163	68600	0.3146
14.9272	68650	0.4093
14.9380	68700	0.363
14.9489	68750	0.3835
14.9598	68800	0.3347
14.9706	68850	0.3562
14.9815	68900	0.3594
14.9924	68950	0.3612

Framework Versions

• Python: 3.12.12
• Sentence Transformers: 5.0.0
• Transformers: 4.57.6
• PyTorch: 2.10.0+cu128
• Accelerate: 1.13.0
• Datasets: 4.0.0
• Tokenizers: 0.22.2

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}
}