Deehan1866/finetuned-sileod-deberta-v3-large-tasksource-nli

SentenceTransformer based on sileod/deberta-v3-large-tasksource-nli

This is a sentence-transformers model finetuned from sileod/deberta-v3-large-tasksource-nli on the PiC/phrase_similarity dataset. 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: sileod/deberta-v3-large-tasksource-nli
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 tokens
• Similarity Function: Cosine Similarity
• Training Dataset:
  • PiC/phrase_similarity
• Language: en

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: DebertaV2Model 
  (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})
)

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("Deehan1866/finetuned-sileod-deberta-v3-large-tasksource-nli")
# Run inference
sentences = [
    "She wants to write about Keima but suffers a major case of writer's block.",
    "She wants to write about Keima but suffers a huge occurrence of writer's block.",
    'specific medical status of movement and the general condition of movement both are conditions under which contradictions can move.',
]
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]

Evaluation

Metrics

Binary Classification

• Dataset: quora-duplicates-dev
• Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.727
cosine_accuracy_threshold	0.8827
cosine_f1	0.7556
cosine_f1_threshold	0.8339
cosine_precision	0.68
cosine_recall	0.85
cosine_ap	0.7491
dot_accuracy	0.702
dot_accuracy_threshold	494.5388
dot_f1	0.7455
dot_f1_threshold	445.2775
dot_precision	0.6352
dot_recall	0.902
dot_ap	0.6487
manhattan_accuracy	0.732
manhattan_accuracy_threshold	335.8038
manhattan_f1	0.7566
manhattan_f1_threshold	378.6906
manhattan_precision	0.6767
manhattan_recall	0.858
manhattan_ap	0.7516
euclidean_accuracy	0.732
euclidean_accuracy_threshold	13.306
euclidean_f1	0.7571
euclidean_f1_threshold	14.6011
euclidean_precision	0.6839
euclidean_recall	0.848
euclidean_ap	0.7524
max_accuracy	0.732
max_accuracy_threshold	494.5388
max_f1	0.7571
max_f1_threshold	445.2775
max_precision	0.6839
max_recall	0.902
max_ap	0.7524

Training Details

Training Dataset

PiC/phrase_similarity

• Dataset: PiC/phrase_similarity at fc67ce7
• Size: 7,004 training samples
• Columns: sentence1, sentence2, and label
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	min: 12 tokens mean: 25.5 tokens max: 57 tokens	min: 12 tokens mean: 25.9 tokens max: 58 tokens	0: ~48.80% 1: ~51.20%

• Samples:

  sentence1	sentence2	label
  newly formed camp is released from the membrane and diffuses across the intracellular space where it serves to activate pka.	recently made encampment is released from the membrane and diffuses across the intracellular space where it serves to activate pka.	0
  According to one data, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.	According to a particular statistic, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.	1
  Note that Fact 1 does not assume any particular structure on the set formula_65.	Note that Fact 1 does not assume any specific edifice on the set formula_65.	0

• Loss: SoftmaxLoss

Evaluation Dataset

PiC/phrase_similarity

• Dataset: PiC/phrase_similarity at fc67ce7
• Size: 1,000 evaluation samples
• Columns: sentence1, sentence2, and label
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	min: 10 tokens mean: 25.46 tokens max: 58 tokens	min: 11 tokens mean: 25.84 tokens max: 59 tokens	0: ~50.00% 1: ~50.00%

• Samples:

  sentence1	sentence2	label
  after theo's apparent death, she decides to leave first colony and ends up traveling with the apostles.	after theo's apparent death, she decides to leave original settlement and ends up traveling with the apostles.	0
  The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's network.	The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's locations.	0
  Two days later Louis XVI banished Necker by a "lettre de cachet" for his very public exchange of pamphlets.	Two days later Louis XVI banished Necker by a "lettre de cachet" for his very free forum of pamphlets.	0

• Loss: SoftmaxLoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• learning_rate: 2e-05
• num_train_epochs: 5
• warmup_ratio: 0.1
• load_best_model_at_end: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• 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: 2e-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: 5
• 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: {'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
• eval_on_start: False
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	quora-duplicates-dev_max_ap
0	0	-	-	0.6829
0.2283	100	-	0.6162	0.7157
0.4566	200	-	0.5267	0.7524
0.6849	300	-	0.5304	0.7595
0.9132	400	-	0.5595	0.7407
1.1416	500	0.5298	0.6625	0.7640
1.3699	600	-	0.5963	0.7437
1.5982	700	-	0.6927	0.7396
1.8265	800	-	0.6797	0.7599
2.0548	900	-	0.8071	0.7630
2.2831	1000	0.2748	1.0068	0.7805
2.5114	1100	-	1.0143	0.7668
2.7397	1200	-	1.1706	0.7662
2.9680	1300	-	1.0500	0.7587
3.1963	1400	-	1.2401	0.7647
3.4247	1500	0.0983	1.3123	0.7618
3.6530	1600	-	1.3971	0.7710
3.8813	1700	-	1.3807	0.7602
4.1096	1800	-	1.3983	0.7564
4.3379	1900	-	1.4812	0.7593
4.5662	2000	0.0494	1.4806	0.7595
4.7945	2100	-	1.4959	0.7601
5.0	2190	-	-	0.7524

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.10
• Sentence Transformers: 3.0.1
• Transformers: 4.42.3
• PyTorch: 2.2.1+cu121
• Accelerate: 0.32.1
• Datasets: 2.20.0
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

@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",
}