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quyenhoang03/trained_all-roberta-large-v1

sourceHugging Faceupdated 7mo agoView on Hugging Face

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SentenceTransformer based on sentence-transformers/all-roberta-large-v1

This is a sentence-transformers model finetuned from sentence-transformers/all-roberta-large-v1. It maps sentences & paragraphs to a 1024-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/all-roberta-large-v1
—Maximum Sequence Length: 256 tokens
—Output Dimensionality: 1024 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': 256, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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:

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'enter congress in the search field',
    'tag: input;classes: ;attributes: {"data-xid":"507","maxlength":"250","name":"q","id":"q","placeholder":"Search InformationWeek","type":"text"};width: 144.99;height: 21.00;top: 211.91;left: 580.63;',
    'tag: a;classes: btn btn-secondary;attributes: {"data-xid":"660","href":"https:\\/\\/mobirise.com\\/mobirise-free-mac.zip","class":"btn btn-secondary"};width: 163.22;height: 18.11;top: 11453.43;left: 170.01;',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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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Training Details

Training Datasets

Unnamed Dataset

—Size: 23,632 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: 3 tokens</li><li>mean: 6.81 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 6.32 tokens</li><li>max: 255 tokens</li></ul> |
—Samples: | sentence0 | sentence1 | |:-------------------------------|:------------------------| | <code>go to world</code> | <code>WORLD</code> | | <code>click "help"</code> | <code>Help</code> | | <code>take me to videos</code> | <code>Video News</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

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

Unnamed Dataset

—Size: 23,632 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: 3 tokens</li><li>mean: 6.79 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 100.9 tokens</li><li>max: 256 tokens</li></ul> |
—Samples: | sentence0 | sentence1 | |:------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>go to the home page</code> | <code><a class="active" data-xid="537" href="/"><br> <img alt="Department of Energy" data-xid="538" src="https://motif.gq/api/archive?uri=https%3A%2F%2Fwww.energy.gov%2Fsites%2Fall%2Fthemes%2Fcleanenergy%2Fimages%2Fxlogofooterwhite.png.pagespeed.ic.wO5tLrl6Iv.webp&accessed=1524216842818"><br> </a></code> | | <code>load my must reads</code> | <code><a class="" data-xid="366" href="/must-read" title="Must Read"><br> Must Read<br> </a></code> | | <code>click to submit query.</code> | <code><input class="searchbutton js-search-button" data-xid="84" id="searchbutton_homepage" tabindex="2" type="submit" value="S"></code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

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

Unnamed Dataset

—Size: 23,632 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: 3 tokens</li><li>mean: 6.76 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 82.16 tokens</li><li>max: 256 tokens</li></ul> |
—Samples: | sentence0 | sentence1 | |:---------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>go to community</code> | <code>tag: a;classes: button button-secondary button-large;attributes: {"data-xid":"101","href":"https:\/\/make.wordpress.org\/community\/meetups-landing-page","class":"button button-secondary button-large"};width: 259.88;height: 35.15;top: 3826.47;left: 36.11;</code> | | <code>change layout.</code> | <code>tag: a;classes: ;attributes: {"data-xid":"20","href":"#","title":"Switch Theme"};width: 78.67;height: 32.00;top: 0.00;left: 732.40;</code> | | <code>go to politics page</code> | <code>tag: a;classes: ;attributes: {"data-xid":"214","href":"https:\/\/globalnews.ca\/politics\/"};width: 49.34;height: 14.49;top: 224.79;left: 201.93;</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

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

Training Hyperparameters

Non-Default Hyperparameters

—per_device_train_batch_size: 64
—per_device_eval_batch_size: 64
—fp16: True
—multi_dataset_batch_sampler: round_robin

All Hyperparameters

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

—overwrite_output_dir: False
—do_predict: False
—eval_strategy: no
—prediction_loss_only: True
—per_device_train_batch_size: 64
—per_device_eval_batch_size: 64
—per_gpu_train_batch_size: None
—per_gpu_eval_batch_size: None
—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: 3
—max_steps: -1
—lr_scheduler_type: linear
—lr_scheduler_kwargs: {}
—warmup_ratio: 0.0
—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: True
—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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—tp_size: 0
—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: 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: None
—hub_always_push: False
—gradient_checkpointing: False
—gradient_checkpointing_kwargs: None
—include_inputs_for_metrics: False
—include_for_metrics: []
—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
—include_tokens_per_second: False
—include_num_input_tokens_seen: False
—neftune_noise_alpha: None
—optim_target_modules: None
—batch_eval_metrics: False
—eval_on_start: False
—use_liger_kernel: False
—eval_use_gather_object: False
—average_tokens_across_devices: False
—prompts: None
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: round_robin

</details>

Training Logs

Epoch	Step	Training Loss
0.4505	500	1.3216
0.9009	1000	1.0186
1.3514	1500	0.8787
1.8018	2000	0.8552
2.2523	2500	0.8545
2.7027	3000	0.8499

Framework Versions

—Python: 3.11.12
—Sentence Transformers: 3.4.1
—Transformers: 4.51.3
—PyTorch: 2.6.0+cu124
—Accelerate: 1.6.0
—Datasets: 3.5.1
—Tokenizers: 0.21.1

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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