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---
license: mit
language:
- en
- az
base_model:
- FacebookAI/xlm-roberta-base
pipeline_tag: sentence-similarity
---
# XLM-RoBERTa model for English and Azerbaijani
## Usage (Sentence-Transformers)
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences = ['Bu nümunə cümlədir', 'Bu cümlə bir nümunədir']
model = SentenceTransformer('LocalDoc/xlm-roberta-AZ')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
```python
from transformers import AutoTokenizer, AutoModel
import torch
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
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 = ['Bu nümunə cümlədir', 'Bu cümlə bir nümunədir']
tokenizer = AutoTokenizer.from_pretrained('LocalDoc/xlm-roberta-AZ')
model = AutoModel.from_pretrained('LocalDoc/xlm-roberta-AZ')
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
# SentenceTransformer Model Architecture
```python
SentenceTransformer(
(0): Transformer({
'max_seq_length': 8192,
'do_lower_case': False
})
with Transformer model: XLMRobertaModel
(1): Pooling({
'word_embedding_dimension': 1024,
'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
})
)
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