---
language:
- id
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6198
- loss:CoSENTLoss
base_model: intfloat/multilingual-e5-base
datasets:
- Pustekhan-ITB/stsb-indo-edu
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on intfloat/multilingual-e5-base
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb indo edu dev
type: stsb-indo-edu-dev
metrics:
- type: pearson_cosine
value: 0.1930033858243812
name: Pearson Cosine
- type: spearman_cosine
value: 0.17647076252403324
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb indo edu test
type: stsb-indo-edu-test
metrics:
- type: pearson_cosine
value: 0.15065000397563194
name: Pearson Cosine
- type: spearman_cosine
value: 0.1512326380689479
name: Spearman Cosine
---
# SentenceTransformer based on intfloat/multilingual-e5-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on the [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) 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:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu)
- **Language:** id
### 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: XLMRobertaModel
(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})
(2): Normalize()
)
```
## 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("ewideplus/indoedu-e5-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
* Datasets: `stsb-indo-edu-dev` and `stsb-indo-edu-test`
* Evaluated with [EmbeddingSimilarityEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | stsb-indo-edu-dev | stsb-indo-edu-test |
|:--------------------|:------------------|:-------------------|
| pearson_cosine | 0.193 | 0.1507 |
| **spearman_cosine** | **0.1765** | **0.1512** |
## Training Details
### Training Dataset
#### stsb-indo-edu
* Dataset: [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) at [f84d4d6](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu/tree/f84d4d6eaca768507bd0f298aef6f3f1a98ddefc)
* Size: 6,198 training samples
* Columns: sentence1
, sentence2
, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | list | list | float |
| details |
['query: P', 'query: e', 'query: l', 'query: a', 'query: j', ...]
| ['passage: T', 'passage: a', 'passage: r', 'passage: i', 'passage: a', ...]
| 0.76
|
| ['query: S', 'query: e', 'query: b', 'query: e', 'query: l', ...]
| ['passage: U', 'passage: p', 'passage: a', 'passage: y', 'passage: a', ...]
| 0.85
|
| ['query: B', 'query: e', 'query: b', 'query: e', 'query: r', ...]
| ['passage: I', 'passage: n', 'passage: i', 'passage: ', 'passage: m', ...]
| 0.63
|
* Loss: [CoSENTLoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### stsb-indo-edu
* Dataset: [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) at [f84d4d6](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu/tree/f84d4d6eaca768507bd0f298aef6f3f1a98ddefc)
* Size: 1,536 evaluation samples
* Columns: sentence1
, sentence2
, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | list | list | float |
| details | ['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...]
| ['passage: S', 'passage: e', 'passage: o', 'passage: r', 'passage: a', ...]
| 1.0
|
| ['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...]
| ['passage: S', 'passage: e', 'passage: o', 'passage: r', 'passage: a', ...]
| 0.95
|
| ['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...]
| ['passage: P', 'passage: r', 'passage: i', 'passage: a', 'passage: ', ...]
| 1.0
|
* Loss: [CoSENTLoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters