turemb_512

turemb_512

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

<!--- Describe your model here -->

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then 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)

Usage (HuggingFace Transformers)

Without sentence-transformers, 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.

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)

Evaluation Results

<!--- Describe how your model was evaluated -->

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

Training

The model was trained with the 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})
)

Citing & Authors

@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}
}