---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
base_model: yano0/my_rope_bert_v2
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget: []
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on yano0/my_rope_bert_v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: pearson_cosine
      value: 0.8363388345473755
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7829140815230603
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8169134821588451
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7806182228552376
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8176194153920942
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7812646926795144
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.790584312051173
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7341313863604967
      name: Spearman Dot
    - type: pearson_max
      value: 0.8363388345473755
      name: Pearson Max
    - type: spearman_max
      value: 0.7829140815230603
      name: Spearman Max
---

# SentenceTransformer based on yano0/my_rope_bert_v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [yano0/my_rope_bert_v2](https://huggingface.co/yano0/my_rope_bert_v2). 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:** [yano0/my_rope_bert_v2](https://huggingface.co/yano0/my_rope_bert_v2) <!-- at revision a392086c08b3bf3a9b9030267a8965af0552d7fb -->
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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': 1024, 'do_lower_case': False}) with Transformer model: RetrievaBertModel 
  (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("pkshatech/RoSEtta-base")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
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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## Evaluation

### Metrics

#### Semantic Similarity

* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8363     |
| **spearman_cosine** | **0.7829** |
| pearson_manhattan   | 0.8169     |
| spearman_manhattan  | 0.7806     |
| pearson_euclidean   | 0.8176     |
| spearman_euclidean  | 0.7813     |
| pearson_dot         | 0.7906     |
| spearman_dot        | 0.7341     |
| pearson_max         | 0.8363     |
| spearman_max        | 0.7829     |

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## Training Details

### Training Logs
| Epoch | Step | spearman_cosine |
|:-----:|:----:|:---------------:|
| 0     | 0    | 0.7829          |


### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.0
- Transformers: 4.44.0
- PyTorch: 2.3.1+cu118
- Accelerate: 0.30.1
- Datasets: 2.19.2
- Tokenizers: 0.19.1

## Citation

### BibTeX

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