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turkish-medium-bert-uncased-mean-nli-stsb-tr

Sentence Similarity
sentence-transformers
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bert
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turkish-medium-bert-uncased-mean-nli-stsb-tr
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---
language:
- tr
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- nli_tr
- emrecan/stsb-mt-turkish
license: mit
---

# turkish-medium-bert-uncased-mean-nli-stsb-tr

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.

This model was adapted from [ytu-ce-cosmos/turkish-medium-bert-uncased](https://huggingface.co/ytu-ce-cosmos/turkish-medium-bert-uncased) and fine-tuned on these datasets:
- [nli_tr](https://huggingface.co/datasets/nli_tr)
- [emrecan/stsb-mt-turkish](https://huggingface.co/datasets/emrecan/stsb-mt-turkish)

:warning: **All texts were manually lowercased,** [as stated](https://huggingface.co/ytu-ce-cosmos/turkish-medium-bert-uncased#%E2%9A%A0-uncased-use-requires-manual-lowercase-conversion) by the model's authors:

 ```python
text.replace("I", "ı").lower()
```

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["Bu örnek bir cümle", "Her cümle dönüştürülür"]

model = SentenceTransformer('atasoglu/turkish-medium-bert-uncased-mean-nli-stsb-tr')
embeddings = model.encode(sentences)
print(embeddings)
```



## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch


#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 = ["Bu örnek bir cümle", "Her cümle dönüştürülür"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('atasoglu/turkish-medium-bert-uncased-mean-nli-stsb-tr')
model = AutoModel.from_pretrained('atasoglu/turkish-medium-bert-uncased-mean-nli-stsb-tr')

# 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:")
print(sentence_embeddings)
```



## Evaluation Results

Achieved results on the [STS-b](https://huggingface.co/datasets/emrecan/stsb-mt-turkish) test split are given below:

```txt
Cosine-Similarity :	    Pearson: 0.8329	Spearman: 0.8336
Manhattan-Distance:	    Pearson: 0.8193	Spearman: 0.8188
Euclidean-Distance:	    Pearson: 0.8198	Spearman: 0.8195
Dot-Product-Similarity:	Pearson: 0.7888	Spearman: 0.7822
```


## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 90 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 

Parameters of the fit()-Method:
```
{
    "epochs": 4,
    "evaluation_steps": 9,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 36,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 512, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors

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