metadata
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 model finetuned from 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
- Maximum Sequence Length: 1024 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
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:
pip install -U sentence-transformers
Then you can load this model and run inference.
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]
Evaluation
Metrics
Semantic Similarity
- Evaluated with
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 |
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