Husain/tiktok_embedding

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: 512 tokens
• Output Dimensionality: 384 tokens
• Similarity Function: Cosine Similarity
• Language: en

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': 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})
)

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("Husain/ramdam_fingerprint_embedding_model")
# Run inference
sentences = [
    'A cat is on a robot.',
    'A man is eating bread.',
    'A woman is pouring eyes into a bowl.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

• Dataset: sts-dev
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9187
spearman_cosine	0.9276
pearson_manhattan	0.8991
spearman_manhattan	0.9321
pearson_euclidean	0.9015
spearman_euclidean	0.929
pearson_dot	0.8789
spearman_dot	0.8957
pearson_max	0.9187
spearman_max	0.9321

Training Details

Training Dataset

Unnamed Dataset

• Size: 101 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 101 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 7 tokens mean: 9.44 tokens max: 14 tokens	min: 3 tokens mean: 9.46 tokens max: 15 tokens	min: 0.1 mean: 0.66 max: 1.0

• Samples:

  sentence1	sentence2	score
  A plane is taking off.	An air plane is taking off.	1.0
  A man is playing a large flute.	A man is playing a flute.	0.76
  A man is spreading shreded cheese on a pizza.	A man is spreading shredded cheese on an uncooked pizza.	0.76

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

stsb

• Dataset: stsb at ab7a5ac
• Size: 1,500 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 6 tokens mean: 9.35 tokens max: 13 tokens	min: 7 tokens mean: 9.9 tokens max: 16 tokens	min: 0.0 mean: 0.39 max: 1.0

• Samples:

  sentence1	sentence2	score
  A woman is riding on a horse.	A man is turning over tables in anger.	0.0
  A man is screwing wood to a wall.	A man is giving a woman a massage.	0.04
  A girl is playing a flute.	A girl plays a wind instrument.	0.64

• Loss: CoSENTLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• learning_rate: 2e-05
• num_train_epochs: 10
• warmup_ratio: 0.1
• save_only_model: True
• seed: 33
• fp16: True
• load_best_model_at_end: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 8
• 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: 10
• 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: True
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 33
• 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
• eval_use_gather_object: False
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	loss	sts-dev_spearman_cosine
0.1538	2	4.4641	0.9366
0.3077	4	4.4652	0.9366
0.4615	6	4.4719	0.9366
0.6154	8	4.4903	0.9366
0.7692	10	4.5264	0.9373
0.9231	12	4.5954	0.9339
1.0769	14	4.6832	0.9328
1.2308	16	4.7534	0.9289
1.3846	18	4.8155	0.9281
1.5385	20	4.8788	0.9269
1.6923	22	4.9350	0.9272
1.8462	24	4.9789	0.9239
2.0	26	5.0132	0.9230
2.1538	28	5.0636	0.9237
2.3077	30	5.1068	0.9202
2.4615	32	5.1460	0.9172
2.6154	34	5.1602	0.9164
2.7692	36	5.1493	0.9210
2.9231	38	5.1399	0.9200
3.0769	40	5.1342	0.9235
3.2308	42	5.1413	0.9258
3.3846	44	5.1440	0.9271
3.5385	46	5.1583	0.9311
3.6923	48	5.1664	0.9293
3.8462	50	5.1682	0.9293
4.0	52	5.1617	0.9293
4.1538	54	5.1543	0.9293
4.3077	56	5.1480	0.9293
4.4615	58	5.1428	0.9291
4.6154	60	5.1292	0.9298
4.7692	62	5.1271	0.9276
4.9231	64	5.1133	0.9276
5.0769	66	5.0928	0.9270
5.2308	68	5.0874	0.9270
5.3846	70	5.0755	0.9270
5.5385	72	5.0665	0.9270
5.6923	74	5.0676	0.9293
5.8462	76	5.0747	0.9293
6.0	78	5.0647	0.9295
6.1538	80	5.0763	0.9273
6.3077	82	5.0832	0.9272
6.4615	84	5.0750	0.9289
6.6154	86	5.0547	0.9289
6.7692	88	5.0350	0.9308
6.9231	90	5.0221	0.9308
7.0769	92	5.0107	0.9308
7.2308	94	4.9967	0.9297
7.3846	96	4.9983	0.9297
7.5385	98	5.0026	0.9277
7.6923	100	5.0095	0.9277
7.8462	102	5.0102	0.9277
8.0	104	5.0055	0.9271
8.1538	106	5.0031	0.9271
8.3077	108	4.9976	0.9271
8.4615	110	4.9941	0.9271
8.6154	112	4.9856	0.9276
8.7692	114	4.9821	0.9276
8.9231	116	4.9782	0.9276
9.0769	118	4.9706	0.9276
9.2308	120	4.9646	0.9276
9.3846	122	4.9584	0.9276
9.5385	124	4.9537	0.9276
9.6923	126	4.9499	0.9276
9.8462	128	4.9485	0.9276
10.0	130	4.9463	0.9276

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.8.10
• Sentence Transformers: 3.1.0
• Transformers: 4.44.2
• PyTorch: 2.3.1+cu121
• Accelerate: 0.34.2
• Datasets: 3.0.0
• Tokenizers: 0.19.1

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}