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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

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

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

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
  • 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}
}
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