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srikarvar
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fine_tuned_model_11

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1936
loss:OnlineContrastiveLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
fine_tuned_model_11 / README.md
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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:1936
  - loss:OnlineContrastiveLoss
widget:
  - source_sentence: What are the symptoms of COVID-19?
    sentences:
      - How to identify COVID-19?
      - What is the process for booking a dinner table?
      - >-
        It is not necessary to include specific fields in a financial report;
        nevertheless, it is beneficial to add pertinent financial metrics to
        help investors gauge the company's condition.
  - source_sentence: How to apply for a scholarship?
    sentences:
      - Steps to apply for a scholarship
      - Advantages of practicing meditation
      - >-
        When `ignore_metadata` is set to `True`, all metadata and attributes are
        stripped from the file prior to processing.
  - source_sentence: How to write a novel?
    sentences:
      - How to write a short story?
      - Who wrote 'Macbeth'?
      - How to reset a phone
  - source_sentence: >-
      You can wrap the project in `job.utils.data.JobLoader` and create a
      collate function to collate the tasks into batches.
    sentences:
      - Steps to prepare a steak
      - How many people live in Germany?
      - >-
        You can use `job.utils.data.JobLoader` to encapsulate the project and
        define a collate function to group the tasks into batches.
  - source_sentence: What is the time now?
    sentences:
      - How to cook a chicken?
      - Current time
      - Guide to starting a small business
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: 0.9212962962962963
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.8385236263275146
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9403508771929825
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.8385236263275146
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9370629370629371
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9436619718309859
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9872231100578164
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.9212962962962963
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 0.8385236263275146
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.9403508771929825
            name: Dot F1
          - type: dot_f1_threshold
            value: 0.8385236263275146
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.9370629370629371
            name: Dot Precision
          - type: dot_recall
            value: 0.9436619718309859
            name: Dot Recall
          - type: dot_ap
            value: 0.9872231100578164
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.9166666666666666
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 8.658426284790039
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.9391891891891893
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 9.594137191772461
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.9025974025974026
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.9788732394366197
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.987218816132896
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.9212962962962963
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 0.568278431892395
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.9403508771929825
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 0.568278431892395
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.9370629370629371
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.9436619718309859
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.9872231100578164
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.9212962962962963
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 8.658426284790039
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.9403508771929825
            name: Max F1
          - type: max_f1_threshold
            value: 9.594137191772461
            name: Max F1 Threshold
          - type: max_precision
            value: 0.9370629370629371
            name: Max Precision
          - type: max_recall
            value: 0.9788732394366197
            name: Max Recall
          - type: max_ap
            value: 0.9872231100578164
            name: Max Ap
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: pair class test
          type: pair-class-test
        metrics:
          - type: cosine_accuracy
            value: 0.9305555555555556
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.8569861650466919
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9484536082474226
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.8531842827796936
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9261744966442953
            name: Cosine Precision
          - type: cosine_recall
            value: 0.971830985915493
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9898045699188958
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.9305555555555556
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 0.8569861650466919
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.9484536082474226
            name: Dot F1
          - type: dot_f1_threshold
            value: 0.8531842231750488
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.9261744966442953
            name: Dot Precision
          - type: dot_recall
            value: 0.971830985915493
            name: Dot Recall
          - type: dot_ap
            value: 0.9898045699188958
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.9351851851851852
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 8.299823760986328
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.9517241379310345
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 8.299823760986328
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.9324324324324325
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.971830985915493
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.9895380844501982
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.9305555555555556
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 0.534814715385437
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.9484536082474226
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 0.5418605804443359
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.9261744966442953
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.971830985915493
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.9898045699188958
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.9351851851851852
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 8.299823760986328
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.9517241379310345
            name: Max F1
          - type: max_f1_threshold
            value: 8.299823760986328
            name: Max F1 Threshold
          - type: max_precision
            value: 0.9324324324324325
            name: Max Precision
          - type: max_recall
            value: 0.971830985915493
            name: Max Recall
          - type: max_ap
            value: 0.9898045699188958
            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_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

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

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",
}