srikarvar/fine_tuned_model_11

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/fine_tuned_model_11")
# Run inference
sentences = [
    'What is the time now?',
    'Current time',
    'Guide to starting a small business',
]
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

Binary Classification

• Dataset: pair-class-dev
• Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.9213
cosine_accuracy_threshold	0.8385
cosine_f1	0.9404
cosine_f1_threshold	0.8385
cosine_precision	0.9371
cosine_recall	0.9437
cosine_ap	0.9872
dot_accuracy	0.9213
dot_accuracy_threshold	0.8385
dot_f1	0.9404
dot_f1_threshold	0.8385
dot_precision	0.9371
dot_recall	0.9437
dot_ap	0.9872
manhattan_accuracy	0.9167
manhattan_accuracy_threshold	8.6584
manhattan_f1	0.9392
manhattan_f1_threshold	9.5941
manhattan_precision	0.9026
manhattan_recall	0.9789
manhattan_ap	0.9872
euclidean_accuracy	0.9213
euclidean_accuracy_threshold	0.5683
euclidean_f1	0.9404
euclidean_f1_threshold	0.5683
euclidean_precision	0.9371
euclidean_recall	0.9437
euclidean_ap	0.9872
max_accuracy	0.9213
max_accuracy_threshold	8.6584
max_f1	0.9404
max_f1_threshold	9.5941
max_precision	0.9371
max_recall	0.9789
max_ap	0.9872

Binary Classification

• Dataset: pair-class-test
• Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.9306
cosine_accuracy_threshold	0.857
cosine_f1	0.9485
cosine_f1_threshold	0.8532
cosine_precision	0.9262
cosine_recall	0.9718
cosine_ap	0.9898
dot_accuracy	0.9306
dot_accuracy_threshold	0.857
dot_f1	0.9485
dot_f1_threshold	0.8532
dot_precision	0.9262
dot_recall	0.9718
dot_ap	0.9898
manhattan_accuracy	0.9352
manhattan_accuracy_threshold	8.2998
manhattan_f1	0.9517
manhattan_f1_threshold	8.2998
manhattan_precision	0.9324
manhattan_recall	0.9718
manhattan_ap	0.9895
euclidean_accuracy	0.9306
euclidean_accuracy_threshold	0.5348
euclidean_f1	0.9485
euclidean_f1_threshold	0.5419
euclidean_precision	0.9262
euclidean_recall	0.9718
euclidean_ap	0.9898
max_accuracy	0.9352
max_accuracy_threshold	8.2998
max_f1	0.9517
max_f1_threshold	8.2998
max_precision	0.9324
max_recall	0.9718
max_ap	0.9898

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,936 training samples
• Columns: label, sentence1, and sentence2
• Approximate statistics based on the first 1000 samples:

  	label	sentence1	sentence2
  type	int	string	string
  details	0: ~35.30% 1: ~64.70%	min: 6 tokens mean: 16.19 tokens max: 98 tokens	min: 4 tokens mean: 15.75 tokens max: 98 tokens

• Samples:

  label	sentence1	sentence2
  1	How do I apply for a credit card?	How do I get a credit card?
  1	What is the function of a learning rate scheduler?	How does a learning rate scheduler optimize training?
  0	What is the speed of a rocket?	What is the speed of a jet plane?

• Loss: OnlineContrastiveLoss

Evaluation Dataset

Unnamed Dataset

• Size: 216 evaluation samples
• Columns: label, sentence1, and sentence2
• Approximate statistics based on the first 216 samples:

  	label	sentence1	sentence2
  type	int	string	string
  details	0: ~34.26% 1: ~65.74%	min: 6 tokens mean: 15.87 tokens max: 87 tokens	min: 4 tokens mean: 15.61 tokens max: 86 tokens

• Samples:

  label	sentence1	sentence2
  0	What is the freezing point of ethanol?	What is the boiling point of ethanol?
  0	Healthy habits	Unhealthy habits
  0	What is the difference between omnivores and herbivores?	What is the difference between omnivores, carnivores, and herbivores?

• Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• gradient_accumulation_steps: 2
• num_train_epochs: 4
• warmup_ratio: 0.1
• load_best_model_at_end: True
• optim: adamw_torch_fused
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 2
• eval_accumulation_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: 4
• 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: 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: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	pair-class-dev_max_ap	pair-class-test_max_ap
0	0	-	-	0.8705	-
0.3279	10	1.3831	-	-	-
0.6557	20	0.749	-	-	-
0.9836	30	0.5578	0.2991	0.9862	-
1.3115	40	0.3577	-	-	-
1.6393	50	0.2594	-	-	-
1.9672	60	0.2119	-	-	-
2.0	61	-	0.2753	0.9898	-
2.2951	70	0.17	-	-	-
2.6230	80	0.1126	-	-	-
2.9508	90	0.0538	-	-	-
2.9836	91	-	0.3222	0.9864	-
3.2787	100	0.1423	-	-	-
3.6066	110	0.066	-	-	-
3.9344	120	0.0486	0.3237	0.9872	0.9898

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.1.0
• Transformers: 4.41.2
• PyTorch: 2.1.2+cu121
• Accelerate: 0.34.2
• 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",
}