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---
base_model: sileod/deberta-v3-large-tasksource-nli
datasets:
- PiC/phrase_similarity
language:
- en
library_name: sentence-transformers
metrics:
- 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:7004
- loss:SoftmaxLoss
widget:
- source_sentence: The valve will open 100% when the set point is reached and will
remain open until a certain blow down factor is reached.
sentences:
- Having raised $17,000,000 in a standard matter, one of the first speculative IPOs,
Tucker needed more money to continue development of the car.
- The valve will open 100% when the tennis scoring protocol is reached and will
remain open until a certain blow down factor is reached.
- But the government of PML (N) gave it the complete exponential of a Tehsil.
- source_sentence: Java BluePrints was the first source to promote Model View Controller
(MVC) and Data Access Object (DAO) for Java EE application development.
sentences:
- Java BluePrints was the pioneer authority to promote Model View Controller (MVC)
and Data Access Object (DAO) for Java EE application development.
- One of the primary job of IIUG is to publish news through a monthly newsletter
("The Insider").
- Opera Dragonfly must be downloaded on original practice, and functions offline
thereafter.
- source_sentence: It also appears immediately after the first shower of the monsoon.
sentences:
- The latter can be minimised by meticulous precision to the wheel bearings, tyre
sizes and pressures, and brakes (to avoid parasitic brake drag).
- It also appears immediately after the initial rain of the monsoon.
- McCullough filed a second appeal that could not be denied without a hearing from
the State Attorney's Office.
- source_sentence: This type places the shifters closer to the hand positions, but
still offer a simple reliable system, especially for touring cyclist.
sentences:
- This type places the shifters closer to the palm placement, but still offer a
simple reliable system, especially for touring cyclist.
- All square dancers learn standard "definitions" of calls, which they recall and
use when the caller issues a certain directive.
- Mainos-TV operated by leasing atmospheric duration from Yleisradio, broadcasting
in reserved blocks between Yleisradio's own programming on its two channels.
- source_sentence: He also played with the Turkish 2nd Division team Pertevniyal,
which was at the time the farm team of Efes, via a dual license.
sentences:
- The group is still active, producing a monthly action points on the women, peace,
and authentication blocks affecting countries on Council's agenda.
- 'Storage/centre tracks are found in the vicinity of the following stations:
Other song highlights.'
- He also played with the Turkish 2nd Division team Pertevniyal, which was at the
time the farm team of Efes, via a two-part authorization.
model-index:
- name: SentenceTransformer based on sileod/deberta-v3-large-tasksource-nli
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: quora duplicates dev
type: quora-duplicates-dev
metrics:
- type: cosine_accuracy
value: 0.753
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8562747240066528
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.7734303912647863
name: Cosine F1
- type: cosine_f1_threshold
value: 0.827180027961731
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.7095158597662772
name: Cosine Precision
- type: cosine_recall
value: 0.85
name: Cosine Recall
- type: cosine_ap
value: 0.7593865167351814
name: Cosine Ap
- type: dot_accuracy
value: 0.716
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 472.6572265625
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.7501982553528945
name: Dot F1
- type: dot_f1_threshold
value: 343.77313232421875
name: Dot F1 Threshold
- type: dot_precision
value: 0.621550591327201
name: Dot Precision
- type: dot_recall
value: 0.946
name: Dot Recall
- type: dot_ap
value: 0.6945003367753116
name: Dot Ap
- type: manhattan_accuracy
value: 0.754
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 320.8356018066406
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.7716105550500454
name: Manhattan F1
- type: manhattan_f1_threshold
value: 356.869140625
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.7078464106844741
name: Manhattan Precision
- type: manhattan_recall
value: 0.848
name: Manhattan Recall
- type: manhattan_ap
value: 0.75919098072954
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.751
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 13.484582901000977
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.7697777777777778
name: Euclidean F1
- type: euclidean_f1_threshold
value: 15.105815887451172
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.6928
name: Euclidean Precision
- type: euclidean_recall
value: 0.866
name: Euclidean Recall
- type: euclidean_ap
value: 0.7572975810714628
name: Euclidean Ap
- type: max_accuracy
value: 0.754
name: Max Accuracy
- type: max_accuracy_threshold
value: 472.6572265625
name: Max Accuracy Threshold
- type: max_f1
value: 0.7734303912647863
name: Max F1
- type: max_f1_threshold
value: 356.869140625
name: Max F1 Threshold
- type: max_precision
value: 0.7095158597662772
name: Max Precision
- type: max_recall
value: 0.946
name: Max Recall
- type: max_ap
value: 0.7593865167351814
name: Max Ap
---
# SentenceTransformer based on sileod/deberta-v3-large-tasksource-nli
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sileod/deberta-v3-large-tasksource-nli](https://huggingface.co/sileod/deberta-v3-large-tasksource-nli) on the [PiC/phrase_similarity](https://huggingface.co/datasets/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](https://huggingface.co/sileod/deberta-v3-large-tasksource-nli) <!-- at revision 212de447184bda8fb9415a2e5697846864ddf304 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```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("Deehan1866/finetuned-valloss-sileod-deberta-v3-large-tasksource-nli")
# Run inference
sentences = [
'He also played with the Turkish 2nd Division team Pertevniyal, which was at the time the farm team of Efes, via a dual license.',
'He also played with the Turkish 2nd Division team Pertevniyal, which was at the time the farm team of Efes, via a two-part authorization.',
'Storage/centre tracks are found in the vicinity of the following stations:\nOther song highlights.',
]
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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</details>
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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Binary Classification
* Dataset: `quora-duplicates-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.753 |
| cosine_accuracy_threshold | 0.8563 |
| cosine_f1 | 0.7734 |
| cosine_f1_threshold | 0.8272 |
| cosine_precision | 0.7095 |
| cosine_recall | 0.85 |
| cosine_ap | 0.7594 |
| dot_accuracy | 0.716 |
| dot_accuracy_threshold | 472.6572 |
| dot_f1 | 0.7502 |
| dot_f1_threshold | 343.7731 |
| dot_precision | 0.6216 |
| dot_recall | 0.946 |
| dot_ap | 0.6945 |
| manhattan_accuracy | 0.754 |
| manhattan_accuracy_threshold | 320.8356 |
| manhattan_f1 | 0.7716 |
| manhattan_f1_threshold | 356.8691 |
| manhattan_precision | 0.7078 |
| manhattan_recall | 0.848 |
| manhattan_ap | 0.7592 |
| euclidean_accuracy | 0.751 |
| euclidean_accuracy_threshold | 13.4846 |
| euclidean_f1 | 0.7698 |
| euclidean_f1_threshold | 15.1058 |
| euclidean_precision | 0.6928 |
| euclidean_recall | 0.866 |
| euclidean_ap | 0.7573 |
| max_accuracy | 0.754 |
| max_accuracy_threshold | 472.6572 |
| max_f1 | 0.7734 |
| max_f1_threshold | 356.8691 |
| max_precision | 0.7095 |
| max_recall | 0.946 |
| **max_ap** | **0.7594** |
<!--
## 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.*
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<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### PiC/phrase_similarity
* Dataset: [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity) at [fc67ce7](https://huggingface.co/datasets/PiC/phrase_similarity/tree/fc67ce7c1e69e360e42dc6f31ddf97bb32f1923d)
* Size: 7,004 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 12 tokens</li><li>mean: 25.5 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 25.9 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>0: ~48.80%</li><li>1: ~51.20%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>newly formed camp is released from the membrane and diffuses across the intracellular space where it serves to activate pka.</code> | <code>recently made encampment is released from the membrane and diffuses across the intracellular space where it serves to activate pka.</code> | <code>0</code> |
| <code>According to one data, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.</code> | <code>According to a particular statistic, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.</code> | <code>1</code> |
| <code>Note that Fact 1 does not assume any particular structure on the set formula_65.</code> | <code>Note that Fact 1 does not assume any specific edifice on the set formula_65.</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
### Evaluation Dataset
#### PiC/phrase_similarity
* Dataset: [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity) at [fc67ce7](https://huggingface.co/datasets/PiC/phrase_similarity/tree/fc67ce7c1e69e360e42dc6f31ddf97bb32f1923d)
* Size: 1,000 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 10 tokens</li><li>mean: 25.46 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 25.84 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:----------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>after theo's apparent death, she decides to leave first colony and ends up traveling with the apostles.</code> | <code>after theo's apparent death, she decides to leave original settlement and ends up traveling with the apostles.</code> | <code>0</code> |
| <code>The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's network.</code> | <code>The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's locations.</code> | <code>0</code> |
| <code>Two days later Louis XVI banished Necker by a "lettre de cachet" for his very public exchange of pamphlets.</code> | <code>Two days later Louis XVI banished Necker by a "lettre de cachet" for his very free forum of pamphlets.</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#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`: 100
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
#### 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`: 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`: 100
- `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
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | quora-duplicates-dev_max_ap |
|:----------:|:-------:|:-------------:|:----------:|:---------------------------:|
| 0 | 0 | - | - | 0.6829 |
| 0.2283 | 100 | - | 0.6795 | 0.6829 |
| 0.4566 | 200 | - | 0.6664 | 0.6873 |
| 0.6849 | 300 | - | 0.6426 | 0.7011 |
| 0.9132 | 400 | - | 0.5995 | 0.7190 |
| 1.1416 | 500 | 0.6452 | 0.5537 | 0.7410 |
| 1.3699 | 600 | - | 0.5262 | 0.7525 |
| **1.5982** | **700** | **-** | **0.5199** | **0.7594** |
| 1.8265 | 800 | - | 0.5206 | 0.7655 |
| 2.0548 | 900 | - | 0.5340 | 0.7745 |
| 2.2831 | 1000 | 0.4654 | 0.5433 | 0.7790 |
| 2.5114 | 1100 | - | 0.5683 | 0.7728 |
| 2.7397 | 1200 | - | 0.5629 | 0.7774 |
| 2.9680 | 1300 | - | 0.5715 | 0.7732 |
| 3.1963 | 1400 | - | 0.6772 | 0.7777 |
| 3.4247 | 1500 | 0.3219 | 0.6834 | 0.7844 |
| 3.6530 | 1600 | - | 0.7428 | 0.7792 |
| 3.8813 | 1700 | - | 0.7353 | 0.7594 |
* 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
```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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