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srikarvar
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fine_tuned_model_14

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:2836
loss:OnlineContrastiveLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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metadata
base_model: intfloat/multilingual-e5-small
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - dot_accuracy
  - dot_accuracy_threshold
  - dot_f1
  - dot_f1_threshold
  - dot_precision
  - dot_recall
  - dot_ap
  - manhattan_accuracy
  - manhattan_accuracy_threshold
  - manhattan_f1
  - manhattan_f1_threshold
  - manhattan_precision
  - manhattan_recall
  - manhattan_ap
  - euclidean_accuracy
  - euclidean_accuracy_threshold
  - euclidean_f1
  - euclidean_f1_threshold
  - euclidean_precision
  - euclidean_recall
  - euclidean_ap
  - max_accuracy
  - max_accuracy_threshold
  - max_f1
  - max_f1_threshold
  - max_precision
  - max_recall
  - max_ap
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:2836
  - loss:OnlineContrastiveLoss
widget:
  - source_sentence: No, it doesn't exist in version 5.3.1.
    sentences:
      - >-
        The `from_dictionary` function requires the following:

        - `data` (Union[dict, Mapping]): A collection of keys linked to values
        or Python objects.

        - `schema` (Schema, optional): If not given, it will be determined from
        the Mapping values.

        - `metadata` (Union[dict, Mapping], optional): Optional metadata for the
        schema (if inferred).
      - Stages of photosynthesis
      - Version 5.3.1 does not contain it.
  - source_sentence: How to make homemade ice cream?
    sentences:
      - Recipe for making ice cream at home
      - >-
        How will abolishing Rs. 500 and Rs. 1000 notes affect the real estate
        businesses in India?
      - How many people live in Japan?
  - source_sentence: Best books on World War II
    sentences:
      - How do I go about getting a visa?
      - What steps are involved in performing market analysis?
      - Top literature about World War II
  - source_sentence: What is the benefit of going Walking every morning?
    sentences:
      - What are the top workouts for losing weight?
      - How large is Japan?
      - >-
        Bollywood industry doesn't encourage outsiders? For ex outsiders may get
        one or at max two chances whereas star kids get multiple chances to
        perform?
  - source_sentence: >-
      The purpose of the training guide is to provide tutorials, how-to guides,
      and conceptual guides for working with AI models.
    sentences:
      - Steps to roast a turkey
      - >-
        The goal of the training guide is to offer tutorials, how-to
        instructions, and conceptual guidance for utilizing AI models.
      - Who was the first person to fly across the Atlantic?
model-index:
  - name: SentenceTransformer based on intfloat/multilingual-e5-small
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: pair class dev
          type: pair-class-dev
        metrics:
          - type: cosine_accuracy
            value: 0.8639240506329114
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.8522839546203613
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.8853333333333334
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.8417313098907471
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9021739130434783
            name: Cosine Precision
          - type: cosine_recall
            value: 0.8691099476439791
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9514746651949948
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.8639240506329114
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 0.8522839546203613
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.8853333333333334
            name: Dot F1
          - type: dot_f1_threshold
            value: 0.8417313098907471
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.9021739130434783
            name: Dot Precision
          - type: dot_recall
            value: 0.8691099476439791
            name: Dot Recall
          - type: dot_ap
            value: 0.9514746651949948
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.8670886075949367
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 8.227925300598145
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.8877005347593583
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 8.646421432495117
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.907103825136612
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.8691099476439791
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.9520439027006086
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.8639240506329114
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 0.5435356497764587
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.8853333333333334
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 0.5626147985458374
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.9021739130434783
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.8691099476439791
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.9514724841898053
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.8670886075949367
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 8.227925300598145
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.8877005347593583
            name: Max F1
          - type: max_f1_threshold
            value: 8.646421432495117
            name: Max F1 Threshold
          - type: max_precision
            value: 0.907103825136612
            name: Max Precision
          - type: max_recall
            value: 0.8691099476439791
            name: Max Recall
          - type: max_ap
            value: 0.9520439027006086
            name: Max Ap
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: pair class test
          type: pair-class-test
        metrics:
          - type: cosine_accuracy
            value: 0.870253164556962
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.8251076936721802
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.8935064935064936
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.8084052801132202
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.8865979381443299
            name: Cosine Precision
          - type: cosine_recall
            value: 0.900523560209424
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9546600352559002
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.870253164556962
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 0.8251076936721802
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.8935064935064936
            name: Dot F1
          - type: dot_f1_threshold
            value: 0.808405339717865
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.8865979381443299
            name: Dot Precision
          - type: dot_recall
            value: 0.900523560209424
            name: Dot Recall
          - type: dot_ap
            value: 0.9546600352559002
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.870253164556962
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 9.181171417236328
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.8912466843501327
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 9.181171417236328
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.9032258064516129
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.8795811518324608
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.9546014712222561
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.870253164556962
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 0.591425895690918
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.8935064935064936
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 0.6190224885940552
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.8865979381443299
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.900523560209424
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.9546600352559002
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.870253164556962
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 9.181171417236328
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.8935064935064936
            name: Max F1
          - type: max_f1_threshold
            value: 9.181171417236328
            name: Max F1 Threshold
          - type: max_precision
            value: 0.9032258064516129
            name: Max Precision
          - type: max_recall
            value: 0.900523560209424
            name: Max Recall
          - type: max_ap
            value: 0.9546600352559002
            name: Max Ap

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

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

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

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",
}