turkish-medium-bert-uncased-mean-nli-stsb-tr
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.
This model was adapted from ytu-ce-cosmos/turkish-medium-bert-uncased and fine-tuned on these datasets:
:warning: All texts were manually lowercased, as stated by the model's authors:
text.replace("I", "ı").lower()
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 = ["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, 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 = ["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 test split are given below:
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})
)
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Model tree for atasoglu/turkish-medium-bert-uncased-mean-nli-stsb-tr
Base model
ytu-ce-cosmos/turkish-medium-bert-uncased