srikarvar/fine_tuned_model_17

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_17")
# Run inference
sentences = [
    'Who wrote the book "1984"?',
    'Who wrote the book "To Kill a Mockingbird"?',
    'What is the speed of light?',
]
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	0.9457
cosine_accuracy_threshold	0.8054
cosine_f1	0.9555
cosine_f1_threshold	0.8054
cosine_precision	0.92
cosine_recall	0.9938
cosine_ap	0.9701
dot_accuracy	0.9457
dot_accuracy_threshold	0.8054
dot_f1	0.9555
dot_f1_threshold	0.8054
dot_precision	0.92
dot_recall	0.9938
dot_ap	0.9701
manhattan_accuracy	0.9457
manhattan_accuracy_threshold	9.7874
manhattan_f1	0.9555
manhattan_f1_threshold	9.7874
manhattan_precision	0.92
manhattan_recall	0.9938
manhattan_ap	0.9698
euclidean_accuracy	0.9457
euclidean_accuracy_threshold	0.6239
euclidean_f1	0.9555
euclidean_f1_threshold	0.6239
euclidean_precision	0.92
euclidean_recall	0.9938
euclidean_ap	0.9701
max_accuracy	0.9457
max_accuracy_threshold	9.7874
max_f1	0.9555
max_f1_threshold	9.7874
max_precision	0.92
max_recall	0.9938
max_ap	0.9701

Binary Classification

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

Metric	Value
cosine_accuracy	0.9457
cosine_accuracy_threshold	0.8054
cosine_f1	0.9555
cosine_f1_threshold	0.8054
cosine_precision	0.92
cosine_recall	0.9938
cosine_ap	0.9701
dot_accuracy	0.9457
dot_accuracy_threshold	0.8054
dot_f1	0.9555
dot_f1_threshold	0.8054
dot_precision	0.92
dot_recall	0.9938
dot_ap	0.9701
manhattan_accuracy	0.9457
manhattan_accuracy_threshold	9.7874
manhattan_f1	0.9555
manhattan_f1_threshold	9.7874
manhattan_precision	0.92
manhattan_recall	0.9938
manhattan_ap	0.9698
euclidean_accuracy	0.9457
euclidean_accuracy_threshold	0.6239
euclidean_f1	0.9555
euclidean_f1_threshold	0.6239
euclidean_precision	0.92
euclidean_recall	0.9938
euclidean_ap	0.9701
max_accuracy	0.9457
max_accuracy_threshold	9.7874
max_f1	0.9555
max_f1_threshold	9.7874
max_precision	0.92
max_recall	0.9938
max_ap	0.9701

Training Details

Training Dataset

Unnamed Dataset

• Size: 2,752 training samples
• Columns: sentence2, label, and sentence1
• Approximate statistics based on the first 1000 samples:

  	sentence2	label	sentence1
  type	string	int	string
  details	min: 4 tokens mean: 10.14 tokens max: 22 tokens	0: ~49.00% 1: ~51.00%	min: 6 tokens mean: 10.77 tokens max: 22 tokens

• Samples:

  sentence2	label	sentence1
  What are the ingredients of pizza?	1	What are the ingredients of a pizza?
  What are the ingredients of a burger?	0	What are the ingredients of a pizza?
  How is photosynthesis carried out?	1	How does photosynthesis work?

• Loss: OnlineContrastiveLoss

Evaluation Dataset

Unnamed Dataset

• Size: 276 evaluation samples
• Columns: sentence2, label, and sentence1
• Approximate statistics based on the first 276 samples:

  	sentence2	label	sentence1
  type	string	int	string
  details	min: 5 tokens mean: 15.34 tokens max: 86 tokens	0: ~41.30% 1: ~58.70%	min: 6 tokens mean: 15.56 tokens max: 87 tokens

• Samples:

  sentence2	label	sentence1
  How is AI used to enhance cybersecurity?	0	What are the challenges of AI in cybersecurity?
  The SYSTEM log documentation can be accessed by clicking on the link which will take you to the main version.	1	You can find the SYSTEM log documentation on the main version. Click on the provided link to redirect to the main version of the documentation.
  Name the capital city of Italy	1	What is the capital of Italy?

• 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	-	-	0.7876	-
0.2326	10	1.5405	-	-	-
0.4651	20	1.0389	-	-	-
0.6977	30	1.2755	-	-	-
0.9302	40	0.7024	-	-	-
1.0	43	-	0.9673	0.9133	-
1.1512	50	0.7527	-	-	-
1.3837	60	0.6684	-	-	-
1.6163	70	0.7612	-	-	-
1.8488	80	0.7265	-	-	-
2.0116	87	-	0.4647	0.9534	-
2.0698	90	0.2986	-	-	-
2.3023	100	0.1964	-	-	-
2.5349	110	0.5834	-	-	-
2.7674	120	0.4893	-	-	-
3.0	130	0.1254	0.3544	0.9670	-
3.2209	140	0.278	-	-	-
3.4535	150	0.1805	-	-	-
3.6860	160	0.4525	-	-	-
3.9186	170	0.1885	-	-	-
3.9651	172	-	0.3396	0.9701	0.9701

• 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",
}