---
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)
- **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
### 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]
```
## Evaluation
### Metrics
#### Binary Classification
* Dataset: `quora-duplicates-dev`
* Evaluated with [BinaryClassificationEvaluator
](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** |
## 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: sentence1
, sentence2
, and label
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details |
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
](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: sentence1
, sentence2
, and label
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | 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
](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