Klue Sroberta Base Continue Learning By Mnr

🚀 bespin-global/klue-sroberta-base-continue-learning-by-mnr

This model utilizes the KLUE/NLI and KLUE/STS datasets. It was trained through the continue-learning method introduced in the official documentation of sentence-transformers as follows:

1. After negative sampling using the NLI dataset, perform the first round of NLI training with MultipleNegativeRankingLoss.
2. For the model trained in step 1, perform the second round of STS training using the STS dataset with CosineSimilarityLoss.

For more details about the training, please refer to the Blog and Colab practice code.

This is a sentence-transformers model: It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for tasks like clustering or semantic search.

🚀 Quick Start

✨ Features

• Utilizes KLUE/NLI and KLUE/STS datasets.
• Trained through a two - step process with different loss functions.
• Maps text to a 768 - dimensional dense vector space for various NLP tasks.

📦 Installation

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

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

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

model = SentenceTransformer("bespin-global/klue-sroberta-base-continue-learning-by-mnr")
embeddings = model.encode(sentences)
print(embeddings)

Advanced Usage

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("bespin-global/klue-sroberta-base-continue-learning-by-mnr")
model = AutoModel.from_pretrained("bespin-global/klue-sroberta-base-continue-learning-by-mnr")

# 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

EmbeddingSimilarityEvaluator: Evaluating the model on sts - test dataset:

• Cosine - Similarity :
  • Pearson: 0.8901 Spearman: 0.8893
• Manhattan - Distance:
  • Pearson: 0.8867 Spearman: 0.8818
• Euclidean - Distance:
  • Pearson: 0.8875 Spearman: 0.8827
• Dot - Product - Similarity:
  • Pearson: 0.8786 Spearman: 0.8735
• Average : 0.8892573547643868

Training

The model was trained with the parameters: DataLoader: torch.utils.data.dataloader.DataLoader of length 329 with parameters:

{'batch_size': 32, '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": 32,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 132,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

📄 License

This model is licensed under the CC - BY - 4.0 license.

Citing & Authors

JaeHyeong AN at Bespin Global

Information Table