srikarvar/fine_tuned_model_14

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_14")
# Run inference
sentences = [
    'The purpose of the training guide is to provide tutorials, how-to guides, and conceptual guides for working with AI models.',
    'The goal of the training guide is to offer tutorials, how-to instructions, and conceptual guidance for utilizing AI models.',
    'Steps to roast a turkey',
]
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	0.8639
cosine_accuracy_threshold	0.8523
cosine_f1	0.8853
cosine_f1_threshold	0.8417
cosine_precision	0.9022
cosine_recall	0.8691
cosine_ap	0.9515
dot_accuracy	0.8639
dot_accuracy_threshold	0.8523
dot_f1	0.8853
dot_f1_threshold	0.8417
dot_precision	0.9022
dot_recall	0.8691
dot_ap	0.9515
manhattan_accuracy	0.8671
manhattan_accuracy_threshold	8.2279
manhattan_f1	0.8877
manhattan_f1_threshold	8.6464
manhattan_precision	0.9071
manhattan_recall	0.8691
manhattan_ap	0.952
euclidean_accuracy	0.8639
euclidean_accuracy_threshold	0.5435
euclidean_f1	0.8853
euclidean_f1_threshold	0.5626
euclidean_precision	0.9022
euclidean_recall	0.8691
euclidean_ap	0.9515
max_accuracy	0.8671
max_accuracy_threshold	8.2279
max_f1	0.8877
max_f1_threshold	8.6464
max_precision	0.9071
max_recall	0.8691
max_ap	0.952

Binary Classification

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

Metric	Value
cosine_accuracy	0.8703
cosine_accuracy_threshold	0.8251
cosine_f1	0.8935
cosine_f1_threshold	0.8084
cosine_precision	0.8866
cosine_recall	0.9005
cosine_ap	0.9547
dot_accuracy	0.8703
dot_accuracy_threshold	0.8251
dot_f1	0.8935
dot_f1_threshold	0.8084
dot_precision	0.8866
dot_recall	0.9005
dot_ap	0.9547
manhattan_accuracy	0.8703
manhattan_accuracy_threshold	9.1812
manhattan_f1	0.8912
manhattan_f1_threshold	9.1812
manhattan_precision	0.9032
manhattan_recall	0.8796
manhattan_ap	0.9546
euclidean_accuracy	0.8703
euclidean_accuracy_threshold	0.5914
euclidean_f1	0.8935
euclidean_f1_threshold	0.619
euclidean_precision	0.8866
euclidean_recall	0.9005
euclidean_ap	0.9547
max_accuracy	0.8703
max_accuracy_threshold	9.1812
max_f1	0.8935
max_f1_threshold	9.1812
max_precision	0.9032
max_recall	0.9005
max_ap	0.9547

Training Details

Training Dataset

Unnamed Dataset

• Size: 2,836 training samples
• Columns: sentence1, label, and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	label	sentence2
  type	string	int	string
  details	min: 6 tokens mean: 15.88 tokens max: 66 tokens	0: ~45.70% 1: ~54.30%	min: 5 tokens mean: 15.82 tokens max: 63 tokens

• Samples:

  sentence1	label	sentence2
  What are the symptoms of diabetes?	1	What are the indicators of diabetes?
  What is the speed of light?	1	At what speed does light travel?
  Eager inventory processing loads the entire inventory list immediately and returns it, while lazy inventory processing applies the processing steps on-the-fly when browsing through the list.	1	Inventory processing that is done eagerly loads the entire inventory right away and provides the result, whereas lazy inventory processing performs the operations as it goes through the list.

• Loss: OnlineContrastiveLoss

Evaluation Dataset

Unnamed Dataset

• Size: 316 evaluation samples
• Columns: sentence1, label, and sentence2
• Approximate statistics based on the first 316 samples:

  	sentence1	label	sentence2
  type	string	int	string
  details	min: 6 tokens mean: 16.37 tokens max: 98 tokens	0: ~39.56% 1: ~60.44%	min: 4 tokens mean: 15.89 tokens max: 98 tokens

• Samples:

  sentence1	label	sentence2
  How many planets are in the solar system?	1	Number of planets in the solar system
  What are the symptoms of pneumonia?	0	What are the symptoms of bronchitis?
  What is the boiling point of sulfur?	0	What is the melting point of sulfur?

• 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: 6
• 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: 6
• 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	-	-	0.8066	-
0.2247	10	1.6271	-	-	-
0.4494	20	1.0316	-	-	-
0.6742	30	0.7502	-	-	-
0.8989	40	0.691	-	-	-
0.9888	44	-	0.7641	0.9368	-
1.1236	50	0.732	-	-	-
1.3483	60	0.532	-	-	-
1.5730	70	0.389	-	-	-
1.7978	80	0.2507	-	-	-
2.0	89	-	0.6496	0.9516	-
2.0225	90	0.4147	-	-	-
2.2472	100	0.2523	-	-	-
2.4719	110	0.1588	-	-	-
2.6966	120	0.1168	-	-	-
2.9213	130	0.1793	-	-	-
2.9888	133	-	0.6431	0.9547	-
3.1461	140	0.2062	-	-	-
3.3708	150	0.109	-	-	-
3.5955	160	0.0631	-	-	-
3.8202	170	0.0588	-	-	-
4.0	178	-	0.6676	0.9512	-
4.0449	180	0.1865	-	-	-
4.2697	190	0.0303	-	-	-
4.4944	200	0.0301	-	-	-
4.7191	210	0.0416	-	-	-
4.9438	220	0.028	-	-	-
4.9888	222	-	0.6770	0.9518	-
5.1685	230	0.0604	-	-	-
5.3933	240	0.0129	-	-	-
5.6180	250	0.0747	-	-	-
5.8427	260	0.0069	-	-	-
5.9326	264	-	0.6755	0.9520	0.9547

• 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",
}