srikarvar/multilingual-e5-small-triplet-final-1

SentenceTransformer based on intfloat/multilingual-e5-small

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-small. 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: intfloat/multilingual-e5-small
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 tokens
• 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': 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("srikarvar/multilingual-e5-small-triplet-final-1")
# Run inference
sentences = [
    'What is the capital city of Canada?',
    'What is the capital of Canada?',
    'What is the capital city of Australia?',
]
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

Triplet

• Dataset: triplet-validation
• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9672
dot_accuracy	0.0328
manhattan_accuracy	0.9672
euclidean_accuracy	0.9672
max_accuracy	0.9672

Training Details

Training Dataset

Unnamed Dataset

• Size: 546 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 10.78 tokens max: 22 tokens	min: 4 tokens mean: 9.52 tokens max: 19 tokens	min: 6 tokens mean: 10.75 tokens max: 22 tokens

• Samples:

  anchor	positive	negative
  What is the capital of Brazil?	Capital city of Brazil	What is the capital of Argentina?
  How do I install Python on my computer?	How do I set up Python on my PC?	How do I uninstall Python on my computer?
  How do I apply for a credit card?	How do I get a credit card?	How do I cancel a credit card?

• Loss: TripletLoss with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 0.7
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 61 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 10.66 tokens max: 16 tokens	min: 5 tokens mean: 9.43 tokens max: 14 tokens	min: 6 tokens mean: 10.54 tokens max: 17 tokens

• Samples:

  anchor	positive	negative
  How to create a podcast?	Steps to start a podcast	How to create a vlog?
  How many states are there in the USA?	Total number of states in the United States	How many provinces are there in Canada?
  What is the population of India?	How many people live in India?	What is the population of China?

• Loss: TripletLoss with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 0.7
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• gradient_accumulation_steps: 2
• learning_rate: 5e-06
• weight_decay: 0.01
• num_train_epochs: 20
• lr_scheduler_type: cosine
• warmup_steps: 50
• load_best_model_at_end: True
• optim: adamw_torch_fused

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 2
• eval_accumulation_steps: None
• learning_rate: 5e-06
• weight_decay: 0.01
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 20
• max_steps: -1
• lr_scheduler_type: cosine
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 50
• 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: 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_fused
• 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
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	triplet-validation_max_accuracy
0.5714	10	0.6735	-	-
0.9714	17	-	0.6198	-
1.1429	20	0.6596	-	-
1.7143	30	0.6357	-	-
2.0	35	-	0.5494	-
2.2857	40	0.596	-	-
2.8571	50	0.5587	-	-
2.9714	52	-	0.4479	-
3.4286	60	0.5265	-	-
4.0	70	0.4703	0.3363	-
4.5714	80	0.4269	-	-
4.9714	87	-	0.2414	-
5.1429	90	0.3725	-	-
5.7143	100	0.3438	-	-
6.0	105	-	0.1711	-
6.2857	110	0.3058	-	-
6.8571	120	0.2478	-	-
6.9714	122	-	0.1365	-
7.4286	130	0.2147	-	-
8.0	140	0.1971	0.1224	-
8.5714	150	0.1946	-	-
8.9714	157	-	0.1111	-
9.1429	160	0.1516	-	-
9.7143	170	0.1663	-	-
10.0	175	-	0.1049	-
10.2857	180	0.1534	-	-
10.8571	190	0.1684	-	-
10.9714	192	-	0.1027	-
11.4286	200	0.1422	-	-
12.0	210	0.1354	0.1007	-
12.5714	220	0.1407	-	-
12.9714	227	-	0.0990	-
13.1429	230	0.154	-	-
13.7143	240	0.1359	-	-
14.0	245	-	0.0975	-
14.2857	250	0.1397	-	-
14.8571	260	0.1389	-	-
14.9714	262	-	0.0969	-
15.4286	270	0.15	-	-
16.0	280	0.1273	0.0966	-
16.5714	290	0.1318	-	-
16.9714	297	-	0.0966	-
17.1429	300	0.1276	-	-
17.7143	310	0.1381	-	-
18.0	315	-	0.0966	-
18.2857	320	0.1284	-	-
18.8571	330	0.1394	-	-
18.9714	332	-	0.0965	-
19.4286	340	0.1407	0.0965	0.9672

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.1.2+cu121
• Accelerate: 0.32.1
• Datasets: 2.19.1
• 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",
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification}, 
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}