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AntoineGourru/FT_phi_cos

sourceHugging Faceupdated 7mo agoView on Hugging Face

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Model Card

SentenceTransformer based on microsoft/Phi-3-mini-4k-instruct

This is a sentence-transformers model finetuned from microsoft/Phi-3-mini-4k-instruct. It maps sentences & paragraphs to a 3072-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: microsoft/Phi-3-mini-4k-instruct
—Maximum Sequence Length: 128 tokens
—Output Dimensionality: 3072 dimensions
—Similarity Function: Cosine Similarity

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': 128, 'do_lower_case': False, 'architecture': 'Phi3Model'})
  (1): Pooling({'word_embedding_dimension': 3072, '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})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

bash

pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("AntoineGourru/FT_phi_cos")
# Run inference
sentences = [
    'N Korea warns of retaliation for South Korea drill',
    "North warns of retaliation for Seoul's naval drill plan",
    'Bangladeshi Islamists rally to demand action against atheist bloggers',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 3072]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7161, 0.1516],
#         [0.7161, 1.0000, 0.1610],
#         [0.1516, 0.1610, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Semantic Similarity

—Dataset: stsb-dev
—Evaluated with <code>EmbeddingSimilarityEvaluator</code>

Metric	Value
pearson_cosine	0.873
spearman_cosine	0.8732

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Training Details

Training Dataset

Unnamed Dataset

—Size: 5,749 training samples
—Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>label</code>
—Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 4 tokens</li><li>mean: 15.28 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.04 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.53</li><li>max: 1.0</li></ul> |
—Samples: | sentence0 | sentence1 | label | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------| | <code>The results were released at Tuesday's meeting in Seattle of the American Thoracic Society and will be published in Thursday's New England Journal of Medicine.</code> | <code>The study results were released at a meeting in Seattle of the American Thoracic Society and also will be published in tomorrow's issue of The New England Journal of Medicine.</code> | <code>0.8727999687194824</code> | | <code>Put a Little Love in your Heart We are all vessels filled with many wonders.</code> | <code>Landon And So This is Christmas We are all vessels filled with many wonders.</code> | <code>0.5599999904632569</code> | | <code>Wall Street analysts had expected 22 cents a share, according to Thomson First Call.</code> | <code>The results were 3 cents a share lower than the forecast of analysts surveyed by Thomson First Call.</code> | <code>0.4400000095367432</code> |
—Loss: <code>CosineSimilarityLoss</code> with these parameters:

json

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }

Training Hyperparameters

Non-Default Hyperparameters

—per_device_train_batch_size: 4
—per_device_eval_batch_size: 4
—num_train_epochs: 1
—multi_dataset_batch_sampler: round_robin

All Hyperparameters

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

—do_predict: False
—eval_strategy: no
—prediction_loss_only: True
—per_device_train_batch_size: 4
—per_device_eval_batch_size: 4
—gradient_accumulation_steps: 1
—eval_accumulation_steps: None
—torch_empty_cache_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
—num_train_epochs: 1
—max_steps: -1
—lr_scheduler_type: linear
—lr_scheduler_kwargs: None
—warmup_ratio: None
—warmup_steps: 0
—log_level: passive
—log_level_replica: warning
—log_on_each_node: True
—logging_nan_inf_filter: True
—enable_jit_checkpoint: False
—save_on_each_node: False
—save_only_model: False
—restore_callback_states_from_checkpoint: False
—use_cpu: False
—seed: 42
—data_seed: None
—bf16: False
—fp16: False
—bf16_full_eval: False
—fp16_full_eval: False
—tf32: None
—local_rank: -1
—ddp_backend: None
—debug: []
—dataloader_drop_last: False
—dataloader_num_workers: 0
—dataloader_prefetch_factor: None
—disable_tqdm: False
—remove_unused_columns: True
—label_names: None
—load_best_model_at_end: False
—ignore_data_skip: False
—fsdp: []
—fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
—parallelism_config: None
—deepspeed: None
—label_smoothing_factor: 0.0
—optim: adamwtorchfused
—optim_args: None
—group_by_length: False
—length_column_name: length
—project: huggingface
—trackio_space_id: trackio
—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
—push_to_hub: False
—resume_from_checkpoint: None
—hub_model_id: None
—hub_strategy: every_save
—hub_private_repo: None
—hub_always_push: False
—hub_revision: None
—gradient_checkpointing: False
—gradient_checkpointing_kwargs: None
—include_for_metrics: []
—eval_do_concat_batches: True
—auto_find_batch_size: False
—full_determinism: False
—ddp_timeout: 1800
—torch_compile: False
—torch_compile_backend: None
—torch_compile_mode: None
—include_num_input_tokens_seen: no
—neftune_noise_alpha: None
—optim_target_modules: None
—batch_eval_metrics: False
—eval_on_start: False
—use_liger_kernel: False
—liger_kernel_config: None
—eval_use_gather_object: False
—average_tokens_across_devices: True
—use_cache: False
—prompts: None
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: round_robin
—router_mapping: {}
—learning_rate_mapping: {}

</details>

Training Logs

Epoch	Step	Training Loss	stsb-dev_spearman_cosine
0.3477	500	0.0533	-
0.6954	1000	0.0263	-
1.0	1438	-	0.8732

Framework Versions

—Python: 3.12.12
—Sentence Transformers: 5.2.3
—Transformers: 5.0.0
—PyTorch: 2.10.0+cu128
—Accelerate: 1.12.0
—Datasets: 4.0.0
—Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

bibtex

@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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