README.md

metadata

base_model: srikarvar/fine_tuned_model_5
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
  - dot_accuracy@1
  - dot_accuracy@3
  - dot_accuracy@5
  - dot_accuracy@10
  - dot_precision@1
  - dot_precision@3
  - dot_precision@5
  - dot_precision@10
  - dot_recall@1
  - dot_recall@3
  - dot_recall@5
  - dot_recall@10
  - dot_ndcg@10
  - dot_mrr@10
  - dot_map@100
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:560
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      The main objective of the System Logs documentation is to demonstrate how
      to utilize the 📋 Logs system to access and manipulate logs of any format
      or type.
    sentences:
      - >-
        The purpose of the System Logs documentation is to provide information
        on how to use the 📋 Logs system to store and work with logs of any
        format or type.
      - >-
        The main difference between a ProductList and an InventoryList is that a
        ProductList provides random access to the items, while an InventoryList
        updates progressively as you browse the list.
      - >-
        The most recommended way to clean kitchen surfaces is with a microfiber
        cloth.
  - source_sentence: The main repository page can be accessed by clicking on the link.
    sentences:
      - >-
        The `to_absolute` function translates a `TaskInstruction` instance into
        a list of absolute instructions, which are then combined together.
      - No, ACTIVATE_X doesn't exist in version 3.0.
      - >-
        It exists in the main repository. You can click on the provided link to
        redirect to the main repository page.
  - source_sentence: >-
      The documentation does not specify what type of value is returned by the
      `fetch_data` function.
    sentences:
      - >-
        The purpose of this document is to provide documentation for the Plugin
        library.
      - >-
        The return type of the `fetch_data` function is not specified in the
        current API documentation.
      - >-
        The `from_dictionary` function takes the following parameters:

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

        - `schema` (Schema, optional): If not passed, will be inferred from the
        Mapping values.

        - `metadata` (Union[dict, Mapping], optional): Optional metadata for the
        schema (if inferred).
  - source_sentence: >-
      The aim of the Gardening.Fertilization class is to carry out the
      application of fertilizers in the garden.
    sentences:
      - The `iterate_folder` function iterates over files within a folder.
      - >-
        The purpose of the Gardening.Fertilization class is to apply fertilizers
        in the garden.
      - >-
        It may be more convenient for the reader to not specify a section when
        browsing a collection because a suitable default may be an aggregated
        section that displays all genres if the reader doesn’t request a
        particular one.
  - source_sentence: Two kinds of cooking methods exist, baking and frying.
    sentences:
      - There are two types of cooking methods, baking and frying.
      - >-
        The purpose of the given recipe is to provide instructions for making
        lasagna.
      - >-
        To get the full path to the locally extracted file, we need to join the
        path of the directory where the archive is extracted to and the relative
        image file path.
model-index:
  - name: SentenceTransformer based on srikarvar/fine_tuned_model_5
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: e5 cogcache small refined
          type: e5-cogcache-small-refined
        metrics:
          - type: cosine_accuracy@1
            value: 0.9642857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.9642857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19999999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09999999999999999
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.9642857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9844808884566332
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9791666666666666
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9791666666666667
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 0.9642857142857143
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 1
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 1
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 1
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.9642857142857143
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.3333333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.19999999999999998
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09999999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.9642857142857143
            name: Dot Recall@1
          - type: dot_recall@3
            value: 1
            name: Dot Recall@3
          - type: dot_recall@5
            value: 1
            name: Dot Recall@5
          - type: dot_recall@10
            value: 1
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.9844808884566332
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.9791666666666666
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.9791666666666667
            name: Dot Map@100
          - type: cosine_accuracy@1
            value: 0.9642857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.9642857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19999999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09999999999999999
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.9642857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9844808884566332
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9791666666666666
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9791666666666667
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 0.9642857142857143
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 1
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 1
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 1
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.9642857142857143
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.3333333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.19999999999999998
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09999999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.9642857142857143
            name: Dot Recall@1
          - type: dot_recall@3
            value: 1
            name: Dot Recall@3
          - type: dot_recall@5
            value: 1
            name: Dot Recall@5
          - type: dot_recall@10
            value: 1
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.9844808884566332
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.9791666666666666
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.9791666666666667
            name: Dot Map@100

SentenceTransformer based on srikarvar/fine_tuned_model_5

This is a sentence-transformers model finetuned from srikarvar/fine_tuned_model_5 on the json dataset. 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: srikarvar/fine_tuned_model_5
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 tokens
• Similarity Function: Cosine Similarity
• Training Dataset:
  • json

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_16")
# Run inference
sentences = [
    'Two kinds of cooking methods exist, baking and frying.',
    'There are two types of cooking methods, baking and frying.',
    'The purpose of the given recipe is to provide instructions for making lasagna.',
]
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

Information Retrieval

• Dataset: e5-cogcache-small-refined
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9643
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.9643
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.9643
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9845
cosine_mrr@10	0.9792
cosine_map@100	0.9792
dot_accuracy@1	0.9643
dot_accuracy@3	1.0
dot_accuracy@5	1.0
dot_accuracy@10	1.0
dot_precision@1	0.9643
dot_precision@3	0.3333
dot_precision@5	0.2
dot_precision@10	0.1
dot_recall@1	0.9643
dot_recall@3	1.0
dot_recall@5	1.0
dot_recall@10	1.0
dot_ndcg@10	0.9845
dot_mrr@10	0.9792
dot_map@100	0.9792

Information Retrieval

• Dataset: e5-cogcache-small-refined
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9643
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.9643
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.9643
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9845
cosine_mrr@10	0.9792
cosine_map@100	0.9792
dot_accuracy@1	0.9643
dot_accuracy@3	1.0
dot_accuracy@5	1.0
dot_accuracy@10	1.0
dot_precision@1	0.9643
dot_precision@3	0.3333
dot_precision@5	0.2
dot_precision@10	0.1
dot_recall@1	0.9643
dot_recall@3	1.0
dot_recall@5	1.0
dot_recall@10	1.0
dot_ndcg@10	0.9845
dot_mrr@10	0.9792
dot_map@100	0.9792

Training Details

Training Dataset

json

• Dataset: json
• Size: 560 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 560 samples:

  	anchor	positive
  type	string	string
  details	min: 9 tokens mean: 30.72 tokens max: 98 tokens	min: 8 tokens mean: 30.52 tokens max: 98 tokens

• Samples:

  anchor	positive
  The function assists in the preprocessing of the whole module in one go.	The function helps preprocess your entire module at once.
  The num_threads parameter determines the quantity of threads used when downloading and processing the data locally.	The num_threads parameter specifies the number of threads when downloading and processing the data locally.
  The map() function can be used to apply transformations to all elements of a model.	The map() function can apply transforms over an entire model.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 5
• warmup_ratio: 0.1
• 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: 16
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• 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: 5
• 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: False
• 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
• 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	e5-cogcache-small-refined_cosine_map@100
0	0	-	0.9702
0.3125	10	0.0171	-
0.625	20	0.0042	-
0.9375	30	0.0011	-
1.0	32	-	0.9792
1.25	40	0.0062	-
1.5625	50	0.0001	-
1.875	60	0.0002	-
2.0	64	-	0.9792
2.1875	70	0.0001	-
2.5	80	0.0005	-
2.8125	90	0.0001	-
3.0	96	-	0.9792
3.125	100	0.0001	-
3.4375	110	0.0002	-
3.75	120	0.0001	-
4.0	128	-	0.9792
4.0625	130	0.0001	-
4.375	140	0.0	-
4.6875	150	0.0001	-
5.0	160	0.0001	0.9792

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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}