Modelpublic

Sayyam-1/crislens-mono-mpnet

sourceHugging Faceupdated 4mo agoView on Hugging Face

0likes75downloads

Model Card

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2. It maps sentences & paragraphs to a 768-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: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
—Maximum Sequence Length: 128 tokens
—Output Dimensionality: 768 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': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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("Sayyam-1/crislens-mono-mpnet")
# Run inference
sentences = [
    'No one voting and they threw tremendous quiet! Not even the reds are saved.',
    'Robbery of Chavistas in the legislative elections of Venezuela This robbery was not recorded on the day of the legislative elections in Venezuela, but in Colombia last November',
    'Picture of Earth as captured on Mars This image taken on Mars shows one of the planet’s two moons and not Earth',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.4909,  0.0480],
#         [ 0.4909,  1.0000, -0.0013],
#         [ 0.0480, -0.0013,  1.0000]])

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Training Details

Training Dataset

Unnamed Dataset

—Size: 51,436 training samples
—Columns: <code>sentence0</code> and <code>sentence1</code>
—Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 80.35 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 40.44 tokens</li><li>max: 128 tokens</li></ul> |
—Samples: | sentence0 | sentence1 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>थूकने की प्रथा बस यहां ही नहीं बल्कि, लंदन जैसी जगहों पर भी है.. ये बिचारा एक अल्जीरिया से आया हुआ पीसफुल शरणार्थी है.. सीधी सी बात है.. या तो इनका थूका हुआ खाओ.. या इनका बहिष्कार करो.. मर्जी आपकी..</code> | <code>Video shows Muslim man from Algeria spitting on food at a London restaurant Old Video of Man Spitting on Pizza Revived With Communal Twist</code> | | <code>Vaccinations make them magnetic, either through liquid or worse through microchips. GTFOH! This is my Dad's TikTok [USER]</code> | <code>The fact that magnets stuck to the arm after the Covid 19 vaccination proves that the vaccine would contain magnetic substances or a chip. Why magnets can stick to the arm and that has nothing to do with the Covid 19 vaccination</code> | | <code>Kristen Stewart: "Hace dos años un palestino me regalo esta Kufeyah hoy murió por un bombarde israelí PARA MATAR A LOS PALESTINOS BASTA" Kristen Stewart 4h-O Two years ago, I got this scarf as a gift from one of my Palestinian fans Now I shocked when I knew that he died by Israilian attack Stop killing Palestinian people, Enough Is Enough #GazaUnderAttack #FreePalestine ...</code> | <code>Foto de la actriz Kristen Stewart usando un pañuelo o kufiyya regalada por palestino muerto en bombardeo israelí La foto de la actriz Kristen Stewart con un pañuelo palestino, conocido como kufiyya, es un montaje</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

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

Training Hyperparameters

Non-Default Hyperparameters

—per_device_train_batch_size: 32
—per_device_eval_batch_size: 32
—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: 32
—per_device_eval_batch_size: 32
—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
0.3109	500	0.2226
0.6219	1000	0.1364
0.9328	1500	0.1058

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: 4.8.3
—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",
}

MultipleNegativesRankingLoss

bibtex

@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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