checkpoint-55/README.md

---
base_model: microsoft/deberta-v3-small
datasets: []
language: []
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:116445
- loss:CachedGISTEmbedLoss
widget:
- source_sentence: what is the main purpose of the brain
  sentences:
  - Brain Physiologically, the function of the brain is to exert centralized control
    over the other organs of the body. The brain acts on the rest of the body both
    by generating patterns of muscle activity and by driving the secretion of chemicals
    called hormones. This centralized control allows rapid and coordinated responses
    to changes in the environment. Some basic types of responsiveness such as reflexes
    can be mediated by the spinal cord or peripheral ganglia, but sophisticated purposeful
    control of behavior based on complex sensory input requires the information integrating
    capabilities of a centralized brain.
  - How do scientists know that some mountains were once at the bottom of an ocean?
  - The Smiths Wiki | Fandom powered by Wikia Share Ad blocker interference detected!
    Wikia is a free-to-use site that makes money from advertising. We have a modified
    experience for viewers using ad blockers Wikia is not accessible if you’ve made
    further modifications. Remove the custom ad blocker rule(s) and the page will
    load as expected. The Smiths were an English rock band formed in Manchester in
    1982. Based on the songwriting partnership of Morrissey (vocals) and Johnny Marr
    (guitar), the band also included Andy Rourke (bass), Mike Joyce (drums) and for
    a brief time Craig Gannon (rhythm guitar). Critics have called them one of the
    most important alternative rock bands to emerge from the British independent music
    scene of the 1980s,and the group has had major influence on subsequent artists.
    Morrissey's lovelorn tales of alienation found an audience amongst youth culture
    bored by the ubiquitous synthesiser-pop bands of the early 1980s, while Marr's
    complex melodies helped return guitar-based music to popularity. The group were
    signed to the independent record label Rough Trade Records , for whom they released
    four studio albums and several compilations, as well as numerous non-LP singles.
    Although they had limited commercial success outside the UK while they were still
    together, and never released a single that charted higher than number 10 in their
    home country, The Smiths won a growing following, and they remain cult and commercial
    favourites. The band broke up in 1987 amid disagreements between Morrissey and
    Marr and has turned down several offers to reform. Welcome to The Smiths Wiki
- source_sentence: There were 29 Muslims fatalities in the Cave of the Patriarchs
    massacre .
  sentences:
  - In August , after the end of the war in June 1902 , Higgins Southampton left the
    `` SSBavarian '' and returned to Cape Town the following month .
  - Between 29 and 52 Muslims were killed and more than 100 others wounded . [   Settlers
    remember gunman Goldstein ; Hebron riots continue ] .
  - 29 Muslims were killed and more than 100 others wounded . [   Settlers remember
    gunman Goldstein ; Hebron riots continue ] .
- source_sentence: are tabby cats all male?
  sentences:
  - Did you know orange tabby cats are typically male? In fact, up to 80 percent of
    orange tabbies are male, making orange female cats a bit of a rarity. According
    to the BBC's Focus Magazine, the ginger gene in cats works a little differently
    compared to humans; it is on the X chromosome.
  - Shawnee Trails Council was formed from the merger of the Four Rivers Council and
    the Audubon Council .
  - 'A picture of a modern looking kitchen area

    '
- source_sentence: Aamir Khan agreed to act immediately after reading Mehra 's screenplay
    in `` Rang De Basanti '' .
  sentences:
  - Chris Rea —   Free listening, videos, concerts, stats and photos at Last.fm singer-songwriter
    Christopher Anton Rea (pronounced Ree-ah), born 4 March 1951, is a singer, songwriter,
    and guitarist from Middlesbrough, England. Rea's recording career began in 1978.
    Although he almost immediately had a US hit single with "Fool (If You Think It's
    Over)", Rea's initial focus was on continental Europe, releasing eight albums
    in the 1980s. It wasn't until 1985's Shamrock Diaries and the songs "Stainsby
    Girls" and "Josephine," that UK audiences began to take notice of him. Follow
    up albums… read more
  - "Healthy Fast Food Meal No. 1. Grilled Chicken Sandwich and Fruit Cup (Chick-fil-A)\
    \ Several fast food chains offer a grilled chicken sandwich. The trick is ordering\
    \ it without mayo or creamy sauce, and making sure itâ\x80\x99s served with a\
    \ whole grain bun."
  - Aamir Khan agreed to act in `` Rang De Basanti '' immediately after reading Mehra
    's script .
- source_sentence: 'A man wearing a blue bow tie and a fedora hat in a car. '
  sentences:
  - A man takes a photo of himself wearing a bowtie and hat
  - Scientists explain the world based on what?
  - 'County of Angus - definition of County of Angus by The Free Dictionary County
    of Angus - definition of County of Angus by The Free Dictionary http://www.thefreedictionary.com/County+of+Angus
     (ăng′gəs) n. Any of a breed of hornless beef cattle that originated in Scotland
    and are usually black but also occur in a red variety. Also called Black Angus.
    [After Angus, former county of Scotland.] Angus (ˈæŋɡəs) n (Placename) a council
    area of E Scotland on the North Sea: the historical county of Angus became part
    of Tayside region in 1975; reinstated as a unitary authority (excluding City of
    Dundee) in 1996. Administrative centre: Forfar. Pop: 107 520 (2003 est). Area:
    2181 sq km (842 sq miles) An•gus'
model-index:
- name: SentenceTransformer based on microsoft/deberta-v3-small
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.2589065791031549
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.31323211323674593
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.27236487282828553
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.29656486394161036
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.2585939429800171
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.2833925986586202
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.28511212645281553
      name: Pearson Dot
    - type: spearman_dot
      value: 0.2967423026930272
      name: Spearman Dot
    - type: pearson_max
      value: 0.28511212645281553
      name: Pearson Max
    - type: spearman_max
      value: 0.31323211323674593
      name: Spearman Max
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: allNLI dev
      type: allNLI-dev
    metrics:
    - type: cosine_accuracy
      value: 0.66796875
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.9721465110778809
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.5343511450381679
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.85741126537323
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.39886039886039887
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8092485549132948
      name: Cosine Recall
    - type: cosine_ap
      value: 0.4140638596370657
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.666015625
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 518.88671875
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.514018691588785
      name: Dot F1
    - type: dot_f1_threshold
      value: 323.9651184082031
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.35181236673773986
      name: Dot Precision
    - type: dot_recall
      value: 0.953757225433526
      name: Dot Recall
    - type: dot_ap
      value: 0.3781233337023534
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.671875
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 114.41839599609375
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.5384615384615384
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 226.82566833496094
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.3941018766756032
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.8497109826589595
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.4272864144491257
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.671875
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 5.084325790405273
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.5404339250493098
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 11.333902359008789
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.4101796407185629
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.791907514450867
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.41769294415599645
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.671875
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 518.88671875
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.5404339250493098
      name: Max F1
    - type: max_f1_threshold
      value: 323.9651184082031
      name: Max F1 Threshold
    - type: max_precision
      value: 0.4101796407185629
      name: Max Precision
    - type: max_recall
      value: 0.953757225433526
      name: Max Recall
    - type: max_ap
      value: 0.4272864144491257
      name: Max Ap
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Qnli dev
      type: Qnli-dev
    metrics:
    - type: cosine_accuracy
      value: 0.640625
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.8695281744003296
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.6578512396694215
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7936367988586426
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.5392953929539296
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8432203389830508
      name: Cosine Recall
    - type: cosine_ap
      value: 0.6314640856589909
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.609375
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 351.17626953125
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.6501650165016502
      name: Dot F1
    - type: dot_f1_threshold
      value: 316.48046875
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.5324324324324324
      name: Dot Precision
    - type: dot_recall
      value: 0.8347457627118644
      name: Dot Recall
    - type: dot_ap
      value: 0.5366456296706419
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.658203125
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 206.32894897460938
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.652373660030628
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 261.3590393066406
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.5107913669064749
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.902542372881356
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.6679289689394285
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.65234375
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 10.764808654785156
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.6393210749646393
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 15.096710205078125
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.47983014861995754
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9576271186440678
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.6460602994393339
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.658203125
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 351.17626953125
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.6578512396694215
      name: Max F1
    - type: max_f1_threshold
      value: 316.48046875
      name: Max F1 Threshold
    - type: max_precision
      value: 0.5392953929539296
      name: Max Precision
    - type: max_recall
      value: 0.9576271186440678
      name: Max Recall
    - type: max_ap
      value: 0.6679289689394285
      name: Max Ap
---

# SentenceTransformer based on microsoft/deberta-v3-small

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the bobox/enhanced_nli-50_k dataset. 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-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - bobox/enhanced_nli-50_k
<!-- - **Language:** Unknown -->
<!-- - **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': 768, '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("bobox/DeBERTa-small-ST-UnifiedDatasets-baseline-checkpoints-tmp")
# Run inference
sentences = [
    'A man wearing a blue bow tie and a fedora hat in a car. ',
    'A man takes a photo of himself wearing a bowtie and hat',
    'County of Angus - definition of County of Angus by The Free Dictionary County of Angus - definition of County of Angus by The Free Dictionary http://www.thefreedictionary.com/County+of+Angus \xa0(ăng′gəs) n. Any of a breed of hornless beef cattle that originated in Scotland and are usually black but also occur in a red variety. Also called Black Angus. [After Angus, former county of Scotland.] Angus (ˈæŋɡəs) n (Placename) a council area of E Scotland on the North Sea: the historical county of Angus became part of Tayside region in 1975; reinstated as a unitary authority (excluding City of Dundee) in 1996. Administrative centre: Forfar. Pop: 107 520 (2003 est). Area: 2181 sq km (842 sq miles) An•gus',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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### Direct Usage (Transformers)

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</details>
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
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### Out-of-Scope Use

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## 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      | 0.2589     |
| **spearman_cosine** | **0.3132** |
| pearson_manhattan   | 0.2724     |
| spearman_manhattan  | 0.2966     |
| pearson_euclidean   | 0.2586     |
| spearman_euclidean  | 0.2834     |
| pearson_dot         | 0.2851     |
| spearman_dot        | 0.2967     |
| pearson_max         | 0.2851     |
| spearman_max        | 0.3132     |

#### Binary Classification
* Dataset: `allNLI-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.668      |
| cosine_accuracy_threshold    | 0.9721     |
| cosine_f1                    | 0.5344     |
| cosine_f1_threshold          | 0.8574     |
| cosine_precision             | 0.3989     |
| cosine_recall                | 0.8092     |
| cosine_ap                    | 0.4141     |
| dot_accuracy                 | 0.666      |
| dot_accuracy_threshold       | 518.8867   |
| dot_f1                       | 0.514      |
| dot_f1_threshold             | 323.9651   |
| dot_precision                | 0.3518     |
| dot_recall                   | 0.9538     |
| dot_ap                       | 0.3781     |
| manhattan_accuracy           | 0.6719     |
| manhattan_accuracy_threshold | 114.4184   |
| manhattan_f1                 | 0.5385     |
| manhattan_f1_threshold       | 226.8257   |
| manhattan_precision          | 0.3941     |
| manhattan_recall             | 0.8497     |
| manhattan_ap                 | 0.4273     |
| euclidean_accuracy           | 0.6719     |
| euclidean_accuracy_threshold | 5.0843     |
| euclidean_f1                 | 0.5404     |
| euclidean_f1_threshold       | 11.3339    |
| euclidean_precision          | 0.4102     |
| euclidean_recall             | 0.7919     |
| euclidean_ap                 | 0.4177     |
| max_accuracy                 | 0.6719     |
| max_accuracy_threshold       | 518.8867   |
| max_f1                       | 0.5404     |
| max_f1_threshold             | 323.9651   |
| max_precision                | 0.4102     |
| max_recall                   | 0.9538     |
| **max_ap**                   | **0.4273** |

#### Binary Classification
* Dataset: `Qnli-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.6406     |
| cosine_accuracy_threshold    | 0.8695     |
| cosine_f1                    | 0.6579     |
| cosine_f1_threshold          | 0.7936     |
| cosine_precision             | 0.5393     |
| cosine_recall                | 0.8432     |
| cosine_ap                    | 0.6315     |
| dot_accuracy                 | 0.6094     |
| dot_accuracy_threshold       | 351.1763   |
| dot_f1                       | 0.6502     |
| dot_f1_threshold             | 316.4805   |
| dot_precision                | 0.5324     |
| dot_recall                   | 0.8347     |
| dot_ap                       | 0.5366     |
| manhattan_accuracy           | 0.6582     |
| manhattan_accuracy_threshold | 206.3289   |
| manhattan_f1                 | 0.6524     |
| manhattan_f1_threshold       | 261.359    |
| manhattan_precision          | 0.5108     |
| manhattan_recall             | 0.9025     |
| manhattan_ap                 | 0.6679     |
| euclidean_accuracy           | 0.6523     |
| euclidean_accuracy_threshold | 10.7648    |
| euclidean_f1                 | 0.6393     |
| euclidean_f1_threshold       | 15.0967    |
| euclidean_precision          | 0.4798     |
| euclidean_recall             | 0.9576     |
| euclidean_ap                 | 0.6461     |
| max_accuracy                 | 0.6582     |
| max_accuracy_threshold       | 351.1763   |
| max_f1                       | 0.6579     |
| max_f1_threshold             | 316.4805   |
| max_precision                | 0.5393     |
| max_recall                   | 0.9576     |
| **max_ap**                   | **0.6679** |

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### Recommendations

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## Training Details

### Training Dataset

#### bobox/enhanced_nli-50_k

* Dataset: bobox/enhanced_nli-50_k
* Size: 116,445 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 33.67 tokens</li><li>max: 338 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 51.48 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | sentence1                                                            | sentence2                                                                                                                                                                                                                                  |
  |:---------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>who is darnell from my name is earl</code>                     | <code>Eddie Steeples Eddie Steeples (born November 25, 1973)[1] is an American actor known for his roles as the "Rubberband Man" in an advertising campaign for OfficeMax, and as Darnell Turner on the NBC sitcom My Name Is Earl.</code> |
  | <code>Ferrell and the Chili Peppers toured together in 2013 .</code> | <code>Ferrell and the Chili Peppers wrapped up I 'm With You World Tour in April 2013 .</code>                                                                                                                                             |
  | <code>Cells have four cycles.</code>                                 | <code>How many cycles do cells have?</code>                                                                                                                                                                                                |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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()
  ), 'temperature': 0.025}
  ```

### Evaluation Dataset

#### bobox/enhanced_nli-50_k

* Dataset: bobox/enhanced_nli-50_k
* Size: 1,506 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 3 tokens</li><li>mean: 32.36 tokens</li><li>max: 341 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 61.99 tokens</li><li>max: 431 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                                     | sentence2                                                                                                                                                                                 |
  |:----------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Interestingly, snakes use their forked tongues to smell.</code>                                                                         | <code>Snakes use their tongue to smell things.</code>                                                                                                                                     |
  | <code>Soil is a renewable resource that can take thousand of years to form.</code>                                                            | <code>What is a renewable resource that can take thousand of years to form?</code>                                                                                                        |
  | <code>As of March 22 , there were more than 321,000 cases with over 13,600 deaths and more than 96,000 recoveries reported worldwide .</code> | <code>As of 22 March , more than 321,000 cases of COVID-19 have been reported in over 180 countries and territories , resulting in more than 13,600 deaths and 96,000 recoveries .</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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()
  ), 'temperature': 0.025}
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 640
- `per_device_eval_batch_size`: 128
- `learning_rate`: 3.75e-05
- `weight_decay`: 0.0005
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 7.499999999999999e-06}
- `warmup_ratio`: 0.33
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTa-small-ST-UnifiedDatasets-baseline-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`: 640
- `per_device_eval_batch_size`: 128
- `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`: 3.75e-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`: 3
- `max_steps`: -1
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 7.499999999999999e-06}
- `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/DeBERTa-small-ST-UnifiedDatasets-baseline-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>

### Training Logs
| Epoch  | Step | Training Loss | loss   | Qnli-dev_max_ap | allNLI-dev_max_ap | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:---------------:|:-----------------:|:------------------------:|
| 0.0055 | 1    | 8.8159        | -      | -               | -                 | -                        |
| 0.0110 | 2    | 9.1259        | -      | -               | -                 | -                        |
| 0.0165 | 3    | 8.9017        | -      | -               | -                 | -                        |
| 0.0220 | 4    | 9.1969        | -      | -               | -                 | -                        |
| 0.0275 | 5    | 9.3716        | 1.3746 | 0.6067          | 0.3706            | 0.1943                   |
| 0.0330 | 6    | 9.0425        | -      | -               | -                 | -                        |
| 0.0385 | 7    | 8.7309        | -      | -               | -                 | -                        |
| 0.0440 | 8    | 9.0123        | -      | -               | -                 | -                        |
| 0.0495 | 9    | 8.8095        | -      | -               | -                 | -                        |
| 0.0549 | 10   | 9.3194        | 1.3227 | 0.6089          | 0.3721            | 0.1976                   |
| 0.0604 | 11   | 8.9873        | -      | -               | -                 | -                        |
| 0.0659 | 12   | 8.5575        | -      | -               | -                 | -                        |
| 0.0714 | 13   | 8.8096        | -      | -               | -                 | -                        |
| 0.0769 | 14   | 8.0996        | -      | -               | -                 | -                        |
| 0.0824 | 15   | 8.1942        | 1.2244 | 0.6140          | 0.3743            | 0.2085                   |
| 0.0879 | 16   | 8.1654        | -      | -               | -                 | -                        |
| 0.0934 | 17   | 7.7336        | -      | -               | -                 | -                        |
| 0.0989 | 18   | 7.9535        | -      | -               | -                 | -                        |
| 0.1044 | 19   | 7.9322        | -      | -               | -                 | -                        |
| 0.1099 | 20   | 7.6812        | 1.1301 | 0.6199          | 0.3790            | 0.2233                   |
| 0.1154 | 21   | 7.551         | -      | -               | -                 | -                        |
| 0.1209 | 22   | 7.3788        | -      | -               | -                 | -                        |
| 0.1264 | 23   | 7.1746        | -      | -               | -                 | -                        |
| 0.1319 | 24   | 7.1849        | -      | -               | -                 | -                        |
| 0.1374 | 25   | 7.1085        | 1.0723 | 0.6195          | 0.3852            | 0.2357                   |
| 0.1429 | 26   | 7.3926        | -      | -               | -                 | -                        |
| 0.1484 | 27   | 7.1817        | -      | -               | -                 | -                        |
| 0.1538 | 28   | 7.239         | -      | -               | -                 | -                        |
| 0.1593 | 29   | 7.0023        | -      | -               | -                 | -                        |
| 0.1648 | 30   | 6.9898        | 1.0282 | 0.6215          | 0.3898            | 0.2477                   |
| 0.1703 | 31   | 6.9776        | -      | -               | -                 | -                        |
| 0.1758 | 32   | 6.8088        | -      | -               | -                 | -                        |
| 0.1813 | 33   | 6.8916        | -      | -               | -                 | -                        |
| 0.1868 | 34   | 6.6931        | -      | -               | -                 | -                        |
| 0.1923 | 35   | 6.5707        | 0.9846 | 0.6253          | 0.3952            | 0.2608                   |
| 0.1978 | 36   | 6.6231        | -      | -               | -                 | -                        |
| 0.2033 | 37   | 6.4951        | -      | -               | -                 | -                        |
| 0.2088 | 38   | 6.4607        | -      | -               | -                 | -                        |
| 0.2143 | 39   | 6.4504        | -      | -               | -                 | -                        |
| 0.2198 | 40   | 6.3649        | 0.9314 | 0.6299          | 0.4041            | 0.2738                   |
| 0.2253 | 41   | 6.2244        | -      | -               | -                 | -                        |
| 0.2308 | 42   | 6.007         | -      | -               | -                 | -                        |
| 0.2363 | 43   | 5.977         | -      | -               | -                 | -                        |
| 0.2418 | 44   | 6.0748        | -      | -               | -                 | -                        |
| 0.2473 | 45   | 5.7946        | 0.8549 | 0.6404          | 0.4116            | 0.2847                   |
| 0.2527 | 46   | 5.8751        | -      | -               | -                 | -                        |
| 0.2582 | 47   | 5.543         | -      | -               | -                 | -                        |
| 0.2637 | 48   | 5.5511        | -      | -               | -                 | -                        |
| 0.2692 | 49   | 5.411         | -      | -               | -                 | -                        |
| 0.2747 | 50   | 5.378         | 0.7943 | 0.6557          | 0.4159            | 0.2866                   |
| 0.2802 | 51   | 5.3831        | -      | -               | -                 | -                        |
| 0.2857 | 52   | 4.9729        | -      | -               | -                 | -                        |
| 0.2912 | 53   | 5.0425        | -      | -               | -                 | -                        |
| 0.2967 | 54   | 4.9446        | -      | -               | -                 | -                        |
| 0.3022 | 55   | 4.9288        | 0.7178 | 0.6679          | 0.4273            | 0.3132                   |


### 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",
}
```

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