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---
license: apache-2.0
language: ti
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
widget:
- text: "ግራፋይት ኣብ መላእ ዓለም ዳርጋ ብምዕሩይ ዝርጋሐ’ዩ ዝርከብ"
---
# TiRoBERTa BiEncoder Model
This is a [sentence-transformers](https://www.SBERT.net) model for the Tigrinya language based on [TiRoBERTa-base](https://huggingface.co/fgaim/tiroberta-base).
The maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Using Sentence-Transformers
Using this model becomes easy when you have sentence-transformersinstalled:
```shell
pip install -U sentence-transformers
```
Then use the model as follows:
```python
from sentence_transformers import SentenceTransformer
sentences = ["ሓደ ሰብኣይ ፈረስ ይጋልብ ኣሎ።", "ሓንቲ ጓል ክራር ትጻወት ኣላ።"]
model = SentenceTransformer('fgaim/tiroberta-bi-encoder')
embeddings = model.encode(sentences)
print(embeddings)
```
## Using 🤗 Transformers
Use the transformers library as follows:
Pass the input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
import torch
from transformers import AutoModel, AutoTokenizer
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ["ሓደ ሰብኣይ ፈረስ ይጋልብ ኣሎ።", "ሓንቲ ጓል ክራር ትጻወት ኣላ።"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("fgaim/tiroberta-bi-encoder")
model = AutoModel.from_pretrained("fgaim/tiroberta-bi-encoder")
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input["attention_mask"])
print("Sentence embeddings:", sentence_embeddings)
```
## Architecture
### Base Model
The model properties:
| Model Size | Layers | Attn. Heads | Hidden Size | FFN | Parameters | Max. Seq |
|------------|----|----|-----|------|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M | 512 |
### BiEncoder Model
- Max Seq Length: `512`
- Word embedding dimension: `768`
```
SentenceTransformer(
Transformer(
{
'max_seq_length': 512,
'do_lower_case': False
}
) # with Transformer model: RobertaModel
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,
}
)
)
```
## Cite
If you use this model in your product or research, you can cite it as follows:
```bibtex
@article{Fitsum2021TiPLMs,
author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
title={Monolingual Pre-trained Language Models for Tigrinya},
year=2021,
publisher={WiNLP 2021 co-located EMNLP 2021}
}
```
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