srikarvar/fine_tuned_model_8

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_8")
# Run inference
sentences = [
    'Practical guides are available to assist you in achieving specific goals and addressing real-world challenges with the framework.',
    'Yes, there are practical guides to help you achieve specific objectives and solve real-world problems with the framework.',
    'How to bake cookies?',
]
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	0.9183
cosine_accuracy_threshold	0.8421
cosine_f1	0.9208
cosine_f1_threshold	0.8421
cosine_precision	0.9037
cosine_recall	0.9385
cosine_ap	0.9453
dot_accuracy	0.9183
dot_accuracy_threshold	0.8421
dot_f1	0.9208
dot_f1_threshold	0.8421
dot_precision	0.9037
dot_recall	0.9385
dot_ap	0.9453
manhattan_accuracy	0.9183
manhattan_accuracy_threshold	8.5071
manhattan_f1	0.9195
manhattan_f1_threshold	8.6426
manhattan_precision	0.916
manhattan_recall	0.9231
manhattan_ap	0.9454
euclidean_accuracy	0.9183
euclidean_accuracy_threshold	0.5619
euclidean_f1	0.9208
euclidean_f1_threshold	0.5619
euclidean_precision	0.9037
euclidean_recall	0.9385
euclidean_ap	0.9453
max_accuracy	0.9183
max_accuracy_threshold	8.5071
max_f1	0.9208
max_f1_threshold	8.6426
max_precision	0.916
max_recall	0.9385
max_ap	0.9454

Binary Classification

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

Metric	Value
cosine_accuracy	0.9183
cosine_accuracy_threshold	0.8421
cosine_f1	0.9208
cosine_f1_threshold	0.8421
cosine_precision	0.9037
cosine_recall	0.9385
cosine_ap	0.9453
dot_accuracy	0.9183
dot_accuracy_threshold	0.8421
dot_f1	0.9208
dot_f1_threshold	0.8421
dot_precision	0.9037
dot_recall	0.9385
dot_ap	0.9453
manhattan_accuracy	0.9183
manhattan_accuracy_threshold	8.5071
manhattan_f1	0.9195
manhattan_f1_threshold	8.6426
manhattan_precision	0.916
manhattan_recall	0.9231
manhattan_ap	0.9454
euclidean_accuracy	0.9183
euclidean_accuracy_threshold	0.5619
euclidean_f1	0.9208
euclidean_f1_threshold	0.5619
euclidean_precision	0.9037
euclidean_recall	0.9385
euclidean_ap	0.9453
max_accuracy	0.9183
max_accuracy_threshold	8.5071
max_f1	0.9208
max_f1_threshold	8.6426
max_precision	0.916
max_recall	0.9385
max_ap	0.9454

Training Details

Training Dataset

Unnamed Dataset

• Size: 2,305 training samples
• Columns: sentence2, sentence1, and label
• Approximate statistics based on the first 1000 samples:

  	sentence2	sentence1	label
  type	string	string	int
  details	min: 5 tokens mean: 13.74 tokens max: 54 tokens	min: 6 tokens mean: 14.13 tokens max: 66 tokens	0: ~43.00% 1: ~57.00%

• Samples:

  sentence2	sentence1	label
  What are the components of a computer?	How does a computer work?	0
  You have the option to create your own personal blog with the help of Blogging Platforms.	Yes, you can start your own personal blog using Blogging Platforms.	1
  It provides the layout of the data and its components.	It returns the structure of the data and its fields.	1

• Loss: OnlineContrastiveLoss

Evaluation Dataset

Unnamed Dataset

• Size: 257 evaluation samples
• Columns: sentence2, sentence1, and label
• Approximate statistics based on the first 257 samples:

  	sentence2	sentence1	label
  type	string	string	int
  details	min: 4 tokens mean: 14.92 tokens max: 50 tokens	min: 6 tokens mean: 14.84 tokens max: 51 tokens	0: ~49.42% 1: ~50.58%

• Samples:

  sentence2	sentence1	label
  What is the speed of sound in air?	What is the speed of light in a vacuum?	0
  Steps to fix a leaking faucet	How to repair a leaking faucet?	1
  Total bones in an adult human	How many bones are in the human body?	1

• 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	-	-	0.7947	-
0.2740	10	1.6052	-	-	-
0.5479	20	0.8914	-	-	-
0.8219	30	0.8434	-	-	-
0.9863	36	-	0.6144	0.9366	-
1.0959	40	0.7351	-	-	-
1.3699	50	0.5016	-	-	-
1.6438	60	0.3754	-	-	-
1.9178	70	0.3364	-	-	-
2.0	73	-	0.5985	0.9396	-
2.1918	80	0.3456	-	-	-
2.4658	90	0.1953	-	-	-
2.7397	100	0.1186	-	-	-
2.9863	109	-	0.5853	0.9455	-
3.0137	110	0.1622	-	-	-
3.2877	120	0.1863	-	-	-
3.5616	130	0.0906	-	-	-
3.8356	140	0.1035	-	-	-
3.9452	144	-	0.5461	0.9454	0.9454

• 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",
}