Turemb 512

🚀 turemb_512

This is a sentence-transformers model that maps sentences and paragraphs to a 512-dimensional dense vector space. It can be used for tasks such as clustering or semantic search.

🚀 Quick Start

✨ Features

• Maps sentences and paragraphs to a 512-dimensional dense vector space.
• Suitable for clustering and semantic search tasks.

📦 Installation

Using this model becomes easy when you have sentence-transformers installed. You can install it with the following command:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

If you have sentence-transformers installed, you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)

Advanced Usage

Without sentence-transformers, you can use the model by passing your input through the transformer model and then applying the right pooling operation on top of the contextualized word embeddings.

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 = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# 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)

📚 Documentation

Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

Training

The model was trained with the following parameters:

DataLoader: torch.utils.data.dataloader.DataLoader of length 14435 with parameters:

{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss: sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss with parameters:

{'scale': 20.0, 'similarity_fct': 'cos_sim'}

Parameters of the fit()-Method:

{
    "epochs": 12,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 0.0001
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 866,
    "weight_decay": 0.005
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': None, 'do_lower_case': False}) with Transformer model: T5EncoderModel 
  (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})
)

📄 License

The citations for this model are as follows:

@article{,
  title={Translation Aligned Sentence Embeddings for Turkish Language},
  author={Unlu, Eren and Ciftci, Unver},
  journal={arXiv preprint arXiv:2311.09748},
  year={2023}
}

@article{chung2022scaling,
  title={Scaling instruction-finetuned language models},
  author={Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret and Tay, Yi and Fedus, William and Li, Yunxuan and Wang, Xuezhi and Dehghani, Mostafa and Brahma, Siddhartha and others},
  journal={arXiv preprint arXiv:2210.11416},
  year={2022}
}

@article{budur2020data,
  title={Data and representation for turkish natural language inference},
  author={Budur, Emrah and {\"O}z{\c{c}}elik, R{\i}za and G{\"u}ng{\"o}r, Tunga and Potts, Christopher},
  journal={arXiv preprint arXiv:2004.14963},
  year={2020}
}

@article{tiedemann2020tatoeba,
  title={The Tatoeba Translation Challenge--Realistic Data Sets for Low Resource and Multilingual MT},
  author={Tiedemann, J{\"o}rg},
  journal={arXiv preprint arXiv:2010.06354},
  year={2020}
}

@article{unal2016tasviret,
  title={Tasviret: G{\"o}r{\"u}nt{\"u}lerden otomatik t{\"u}rk{\c{c}}e a{\c{c}}{\i}klama olusturma I{\c{c}}in bir denekta{\c{c}}{\i} veri k{\"u}mesi (TasvirEt: A benchmark dataset for automatic Turkish description generation from images)},
  author={Unal, Mesut Erhan and Citamak, Begum and Yagcioglu, Semih and Erdem, Aykut and Erdem, Erkut and Cinbis, Nazli Ikizler and Cakici, Ruket},
  journal={IEEE Sinyal Isleme ve Iletisim Uygulamalar{\i} Kurultay{\i} (SIU 2016)},
  year={2016}
}