srikarvar/fine_tuned_model_9

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 <!-- at revision fd1525a9fd15316a2d503bf26ab031a61d056e98 -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 384 tokens
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

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_9")
# Run inference
sentences = [
    'Who wrote the book "1984"?',
    'Who wrote the book "To Kill a Mockingbird"?',
    'What is the speed of light?',
]
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]

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Binary Classification

• —Dataset: pair-class-dev
• —Evaluated with <code>BinaryClassificationEvaluator</code>

Binary Classification

• —Dataset: pair-class-test
• —Evaluated with <code>BinaryClassificationEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 2,476 training samples
• —Columns: <code>sentence2</code>, <code>label</code>, and <code>sentence1</code>
• —Approximate statistics based on the first 1000 samples: | | sentence2 | label | sentence1 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | int | string | | details | <ul><li>min: 4 tokens</li><li>mean: 16.06 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>0: ~40.20%</li><li>1: ~59.80%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.35 tokens</li><li>max: 98 tokens</li></ul> |
• —Samples: | sentence2 | label | sentence1 | |:--------------------------------------------------------------------------------------------------------|:---------------|:----------------------------------------------------------------------------------------------------------| | <code>A model is trained using the ImageNet dataset to classify images into distinct categories.</code> | <code>1</code> | <code>The ImageNet dataset is used for training models to classify images into various categories.</code> | | <code>Version 5.3.1 does not contain it.</code> | <code>1</code> | <code>No, it doesn't exist in version 5.3.1.</code> | | <code>Can you do my homework for me?</code> | <code>0</code> | <code>Can you help me with my homework?</code> |
• —Loss: <code>OnlineContrastiveLoss</code>

Evaluation Dataset

Unnamed Dataset

• —Size: 276 evaluation samples
• —Columns: <code>sentence2</code>, <code>label</code>, and <code>sentence1</code>
• —Approximate statistics based on the first 276 samples: | | sentence2 | label | sentence1 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | int | string | | details | <ul><li>min: 5 tokens</li><li>mean: 15.34 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>0: ~41.30%</li><li>1: ~58.70%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.56 tokens</li><li>max: 87 tokens</li></ul> |
• —Samples: | sentence2 | label | sentence1 | |:---------------------------------------------------------------------------------------------------------------------------|:---------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>How is AI used to enhance cybersecurity?</code> | <code>0</code> | <code>What are the challenges of AI in cybersecurity?</code> | | <code>The SYSTEM log documentation can be accessed by clicking on the link which will take you to the main version.</code> | <code>1</code> | <code>You can find the SYSTEM log documentation on the main version. Click on the provided link to redirect to the main version of the documentation.</code> | | <code>Name the capital city of Italy</code> | <code>1</code> | <code>What is the capital of Italy?</code> |
• —Loss: <code>OnlineContrastiveLoss</code>

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: adamwtorchfused
• —batch_sampler: no_duplicates

All Hyperparameters

<details><summary>Click to expand</summary>

• —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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —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

</details>

Training Logs

• —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",
}

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