Create README.md

README.md

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+---
+base_model: microsoft/deberta-v3-base
+datasets:
+- tals/vitaminc
+- allenai/scitail
+- allenai/sciq
+- allenai/qasc
+- sentence-transformers/msmarco-msmarco-distilbert-base-v3
+- sentence-transformers/natural-questions
+- sentence-transformers/trivia-qa
+- sentence-transformers/gooaq
+- google-research-datasets/paws
+language:
+- en
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+- cosine_accuracy
+- cosine_accuracy_threshold
+- cosine_f1
+- cosine_f1_threshold
+- cosine_precision
+- cosine_recall
+- cosine_ap
+- dot_accuracy
+- dot_accuracy_threshold
+- dot_f1
+- dot_f1_threshold
+- dot_precision
+- dot_recall
+- dot_ap
+- manhattan_accuracy
+- manhattan_accuracy_threshold
+- manhattan_f1
+- manhattan_f1_threshold
+- manhattan_precision
+- manhattan_recall
+- manhattan_ap
+- euclidean_accuracy
+- euclidean_accuracy_threshold
+- euclidean_f1
+- euclidean_f1_threshold
+- euclidean_precision
+- euclidean_recall
+- euclidean_ap
+- max_accuracy
+- max_accuracy_threshold
+- max_f1
+- max_f1_threshold
+- max_precision
+- max_recall
+- max_ap
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:123245
+- loss:CachedGISTEmbedLoss
+widget:
+- source_sentence: what type of inheritance does haemochromatosis
+  sentences:
+  - Nestled on the tranquil banks of the Pamlico River, Moss Landing is a vibrant
+    new community of thoughtfully conceived, meticulously crafted single-family homes
+    in Washington, North Carolina. Washington is renowned for its historic architecture
+    and natural beauty.
+  - '1 Microwave on high for 8 to 10 minutes or until tender, turning the yams once.
+    2  To microwave sliced yams: Wash, peel, and cut off the woody portions and ends.
+    3  Cut yams into quarters. 4  Place the yams and 1/2 cup water in a microwave-safe
+    casserole.ake the Yams. 1  Place half the yams in a 1-quart casserole. 2  Layer
+    with half the brown sugar and half the margarine. 3  Repeat the layers. 4  Bake,
+    uncovered, in a 375 degree F oven for 30 to 35 minutes or until the yams are glazed,
+    spooning the liquid over the yams once or twice during cooking.'
+  - Types 1, 2, and 3 hemochromatosis are inherited in an autosomal recessive pattern,
+    which means both copies of the gene in each cell have mutations. Most often, the
+    parents of an individual with an autosomal recessive condition each carry one
+    copy of the mutated gene but do not show signs and symptoms of the condition.Type
+    4 hemochromatosis is distinguished by its autosomal dominant inheritance pattern.With
+    this type of inheritance, one copy of the altered gene in each cell is sufficient
+    to cause the disorder. In most cases, an affected person has one parent with the
+    condition.ype 1, the most common form of the disorder, and type 4 (also called
+    ferroportin disease) begin in adulthood. Men with type 1 or type 4 hemochromatosis
+    typically develop symptoms between the ages of 40 and 60, and women usually develop
+    symptoms after menopause. Type 2 hemochromatosis is a juvenile-onset disorder.
+- source_sentence: More than 273 people have died from the 2019-20 coronavirus outside
+    mainland China .
+  sentences:
+  - 'More than 3,700 people have died : around 3,100 in mainland China and around
+    550 in all other countries combined .'
+  - 'More than 3,200 people have died : almost 3,000 in mainland China and around
+    275 in other countries .'
+  - more than 4,900 deaths have been attributed to COVID-19 .
+- source_sentence: The male reproductive system consists of structures that produce
+    sperm and secrete testosterone.
+  sentences:
+  - What does the male reproductive system consist of?
+  - What facilitates the diffusion of ions across a membrane?
+  - Autoimmunity can develop with time, and its causes may be rooted in this?
+- source_sentence: Nitrogen gas comprises about three-fourths of earth's atmosphere.
+  sentences:
+  - What do all cells have in common?
+  - What gas comprises about three-fourths of earth's atmosphere?
+  - What do you call an animal in which the embryo, often termed a joey, is born immature
+    and must complete its development outside the mother's body?
+- source_sentence: What device is used to regulate a person's heart rate?
+  sentences:
+  - 'Marie Antoinette and the French Revolution    .   Famous Faces    .   Mad Max:
+    Maximilien Robespierre   |   PBS Extended Interviews > Resources > For Educators
+    > Mad Max: Maximilien Robespierre Maximilien Robespierre was born May 6, 1758
+    in Arras, France. Educated at the Lycée Louis-le-Grand in Paris as a lawyer, Robespierre
+    became a disciple of philosopher Jean-Jacques Rousseau and a passionate advocate
+    for the poor. Called "the Incorruptible" because of his unwavering dedication
+    to the Revolution, Robespierre joined the Jacobin Club and earned a loyal following.
+    In contrast to the more republican Girondins and Marie Antoinette, Robespierre
+    fiercely opposed declaring war on Austria, feeling it would distract from revolutionary
+    progress in France. Robespierre''s exemplary oratory skills influenced the National
+    Convention in 1792 to avoid seeking public opinion about the Convention’s decision
+    to execute King Louis XVI.  In 1793, the Convention elected Robespierre to the
+    Committee of Public Defense. He was a highly controversial member, developing
+    radical policies, warning of conspiracies, and suggesting restructuring the Convention.
+    This behavior eventually led to his downfall, and he was guillotined without trial
+    on 10th Thermidor An II (July 28, 1794), marking the end of the Reign of Terror.
+    Famous Faces'
+  - Devices for Arrhythmia Devices for Arrhythmia Updated:Dec 21,2016 In a medical
+    emergency, life-threatening arrhythmias may be stopped by giving the heart an
+    electric shock (as with a defibrillator ). For people with recurrent arrhythmias,
+    medical devices such as a pacemaker and implantable cardioverter defibrillator
+    (ICD) can help by continuously monitoring the heart's electrical system and providing
+    automatic correction when an arrhythmia starts to occur. This section covers everything
+    you need to know about these devices. Implantable Cardioverter Defibrillator (ICD)
+  - 'vintage cleats | eBay vintage cleats: 1 2 3 4 5 eBay determines this price through
+    a machine learned model of the product''s sale prices within the last 90 days.
+    eBay determines trending price through a machine learned model of the product’s
+    sale prices within the last 90 days. "New" refers to a brand-new, unused, unopened,
+    undamaged item, and "Used" refers to an item that has been used previously. Top
+    Rated Plus Sellers with highest buyer ratings Returns, money back Sellers with
+    highest buyer ratings Returns, money back'
+model-index:
+- name: SentenceTransformer based on microsoft/deberta-v3-base
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test
+      type: sts-test
+    metrics:
+    - type: pearson_cosine
+      value: 0.8253431554642914
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.870857890879963
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8653068915625914
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8667110599943904
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8671346646296434
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8681442638917114
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.7826717704847901
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.7685403521338614
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8671346646296434
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.870857890879963
+      name: Spearman Max
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: allNLI dev
+      type: allNLI-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.71875
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.8745474815368652
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.617169373549884
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.7519949674606323
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.5155038759689923
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.7687861271676301
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.6116004689391709
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.693359375
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 401.3755187988281
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.566735112936345
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 295.2575988769531
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.4394904458598726
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.7976878612716763
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.5243551756921989
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.724609375
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 228.3092498779297
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.6267281105990783
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 266.0207824707031
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.5210727969348659
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.7861271676300579
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.6101425904568746
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.720703125
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 9.726119041442871
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.6303854875283447
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 14.837699890136719
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.5186567164179104
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.8034682080924855
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.6172110045723997
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.724609375
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 401.3755187988281
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.6303854875283447
+      name: Max F1
+    - type: max_f1_threshold
+      value: 295.2575988769531
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.5210727969348659
+      name: Max Precision
+    - type: max_recall
+      value: 0.8034682080924855
+      name: Max Recall
+    - type: max_ap
+      value: 0.6172110045723997
+      name: Max Ap
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: Qnli dev
+      type: Qnli-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.673828125
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.7472400069236755
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.6863468634686347
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.7334084510803223
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.6078431372549019
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.788135593220339
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.7293502303398447
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.6484375
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 392.88726806640625
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.6634920634920635
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 310.97833251953125
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.5304568527918782
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.885593220338983
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.6331200610041253
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.671875
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 277.69342041015625
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.6830122591943958
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 301.36639404296875
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.582089552238806
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.826271186440678
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.7276384343706648
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.68359375
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 15.343950271606445
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.6895238095238095
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 15.738676071166992
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.6262975778546713
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.7669491525423728
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.7307379367367225
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.68359375
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 392.88726806640625
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.6895238095238095
+      name: Max F1
+    - type: max_f1_threshold
+      value: 310.97833251953125
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.6262975778546713
+      name: Max Precision
+    - type: max_recall
+      value: 0.885593220338983
+      name: Max Recall
+    - type: max_ap
+      value: 0.7307379367367225
+      name: Max Ap
+---
+
+# SentenceTransformer based on microsoft/deberta-v3-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the negation-triplets, [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), xsum-pairs, [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), openbookqa_pairs, [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq), [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) and global_dataset datasets. 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:** [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) <!-- at revision 8ccc9b6f36199bec6961081d44eb72fb3f7353f3 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Datasets:**
+    - negation-triplets
+    - [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc)
+    - [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail)
+    - [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail)
+    - xsum-pairs
+    - [sciq_pairs](https://huggingface.co/datasets/allenai/sciq)
+    - [qasc_pairs](https://huggingface.co/datasets/allenai/qasc)
+    - openbookqa_pairs
+    - [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3)
+    - [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions)
+    - [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa)
+    - [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq)
+    - [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws)
+    - global_dataset
+- **Language:** en
+<!-- - **License:** Unknown -->
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+* Dataset: `sts-test`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8253     |
+| **spearman_cosine** | **0.8709** |
+| pearson_manhattan   | 0.8653     |
+| spearman_manhattan  | 0.8667     |
+| pearson_euclidean   | 0.8671     |
+| spearman_euclidean  | 0.8681     |
+| pearson_dot         | 0.7827     |
+| spearman_dot        | 0.7685     |
+| pearson_max         | 0.8671     |
+| spearman_max        | 0.8709     |
+
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 96
+- `per_device_eval_batch_size`: 68
+- `learning_rate`: 3.5e-05
+- `weight_decay`: 0.0005
+- `num_train_epochs`: 2
+- `lr_scheduler_type`: cosine_with_min_lr
+- `lr_scheduler_kwargs`: {'num_cycles': 3.5, 'min_lr': 1.5e-05}
+- `warmup_ratio`: 0.33
+- `save_safetensors`: False
+- `fp16`: True
+- `push_to_hub`: True
+- `hub_model_id`: bobox/DeBERTa3-base-STr-CosineWaves-checkpoints-tmp
+- `hub_strategy`: all_checkpoints
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 96
+- `per_device_eval_batch_size`: 68
+- `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`: 3.5e-05
+- `weight_decay`: 0.0005
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine_with_min_lr
+- `lr_scheduler_kwargs`: {'num_cycles': 3.5, 'min_lr': 1.5e-05}
+- `warmup_ratio`: 0.33
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: False
+- `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`: {'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`: True
+- `resume_from_checkpoint`: None
+- `hub_model_id`: bobox/DeBERTa3-base-STr-CosineWaves-checkpoints-tmp
+- `hub_strategy`: all_checkpoints
+- `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
+- `eval_use_gather_object`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.0.1
+- Transformers: 4.44.0
+- PyTorch: 2.4.0
+- Accelerate: 0.33.0
+- Datasets: 2.21.0
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```