fine_tuned_model_13 / README.md

srikarvar

Add new SentenceTransformer model.

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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 next move is to acquire the dataset and delineate the divisions.
    sentences:
      - The next step is to download the dataset and define the splits.
      - >-
        The `batch_id` parameter is used to specify a batch specific to the
        recipe code. It is used to update the storage directory when the recipe
        instructions are modified.
      - >-
        The Instructions guide is divided into sections such as Overview,
        Tutorials, How-to guides, Settings, Interface, Hardware, System
        repository, Conceptual guides, and Reference.
  - source_sentence: >-
      The PaperInfo holds the data of a research paper, which may include its
      title, abstract, and reference list.
    sentences:
      - >-
        Parquet is a language-agnostic file format that enables efficient
        storage and querying of data tables.
      - >-
        The purpose of the food processor in the kitchen is to chop and blend
        ingredients quickly and efficiently.
      - >-
        A research paper's information is stored inside PaperInfo and can
        include information such as the paper's title, abstract, and references.
  - source_sentence: This manual is devoted to constructing a personal finance tracker.
    sentences:
      - >-
        The `map()` function in the financial package supports processing large
        amounts of transactions, speeding up data analysis.
      - The manual is about building a personal finance tracker.
      - No, ITEMCODE is not available in version 3.5.0 of the documentation.
  - source_sentence: >-
      The reader may find it more advantageous to not specify a section when
      browsing a collection, as a default section that displays all genres may
      be the most suitable choice if no particular genre is requested.
    sentences:
      - >-
        The PlantCare manual provides guidance on how to plant, water, prune,
        and fertilize different species of plants.
      - >-
        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.
      - >-
        If you want to switch from a ProductList to an InventoryList, you can
        simply create a new InventoryList object from your existing data using
        the appropriate method for your data source.
  - source_sentence: >-
      This framework has a strong connection with cloud platforms, making it
      simple to deploy and share models with the developer community.
    sentences:
      - >-
        Yes, the framework is deeply integrated with cloud-based platforms,
        allowing for easy deployment and sharing with the developer community.
      - UserRole data is properly converted to arrays.
      - >-
        You can find information about creating a research paper card in the
        /docs/papers/v2.10.0/paper_card document.
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: 1
            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: 1
            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: 1
            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: 1
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 1
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 1
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 1
            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: 1
            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: 1
            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: 1
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 1
            name: Dot Mrr@10
          - type: dot_map@100
            value: 1
            name: Dot Map@100
          - type: cosine_accuracy@1
            value: 1
            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: 1
            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: 1
            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: 1
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 1
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 1
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 1
            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: 1
            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: 1
            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: 1
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 1
            name: Dot Mrr@10
          - type: dot_map@100
            value: 1
            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_13")
# Run inference
sentences = [
    'This framework has a strong connection with cloud platforms, making it simple to deploy and share models with the developer community.',
    'Yes, the framework is deeply integrated with cloud-based platforms, allowing for easy deployment and sharing with the developer community.',
    'UserRole data is properly converted to arrays.',
]
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	1.0
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	1.0
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	1.0
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	1.0
cosine_mrr@10	1.0
cosine_map@100	1.0
dot_accuracy@1	1.0
dot_accuracy@3	1.0
dot_accuracy@5	1.0
dot_accuracy@10	1.0
dot_precision@1	1.0
dot_precision@3	0.3333
dot_precision@5	0.2
dot_precision@10	0.1
dot_recall@1	1.0
dot_recall@3	1.0
dot_recall@5	1.0
dot_recall@10	1.0
dot_ndcg@10	1.0
dot_mrr@10	1.0
dot_map@100	1.0

Information Retrieval

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

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

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: 30.18 tokens
max: 98 tokens

min: 8 tokens
mean: 30.0 tokens
max: 98 tokens

	anchor	positive
type	string	string
details	min: 9 tokens mean: 30.18 tokens max: 98 tokens	min: 8 tokens mean: 30.0 tokens max: 98 tokens

Samples:

anchor	positive
`It is not available in v2.10.0.`	`No, it doesn't exist in v2.10.0.`
`You can become a member of the research forum and pose questions to the AI community.`	`You can join and ask questions in the AI research forum.`
`No information regarding initializing a project for PyTorch is included in the guide.`	`The guide does not provide information on how to initialize a project for PyTorch.`

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
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: 3
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	-	0.9911
0.3125	10	0.0088	-
0.625	20	0.001	-
0.9375	30	0.0064	-
1.0	32	-	1.0
1.25	40	0.0	-
1.5625	50	0.0001	-
1.875	60	0.0002	-
2.0	64	-	1.0
2.1875	70	0.0003	-
2.5	80	0.0001	-
2.8125	90	0.0002	-
3.0	96	-	1.0

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}
}