Bert Base Uncased Xnli Sts Finetuned Education

🚀 inokufu/bertheo-en

A sentence-transformers model fine-tuned on course sentences, mapping sentences & paragraphs to a 768-dimensional dense vector space for tasks like clustering or semantic search.

🚀 Quick Start

This model is a sentence-transformers model fine-tuned on course sentences. It can map sentences and paragraphs to a 768-dimensional dense vector space, which is useful for tasks such as clustering or semantic search.

✨ Features

• Based on the English bert-base-uncased pre-trained model.
• Fine-tuned on a learning object (LO) sentences dataset, a natural language inference task (XNLI), and a text semantic similarity task (on STS data).
• Capable of providing a better semantic representation of words compared to the base model.

📦 Installation

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

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 = ["Learn to code in python", "Become an expert in accounting"]

model = SentenceTransformer('inokufu/bert-base-uncased-xnli-sts-finetuned-education')
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 = ["Learn to code in python", "Become an expert in accounting"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('inokufu/bert-base-uncased-xnli-sts-finetuned-education')
model = AutoModel.from_pretrained('inokufu/bert-base-uncased-xnli-sts-finetuned-education')

# 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

STS (en) score: 84.61%

Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (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})
)

References

[1] https://huggingface.co/bert-base-uncased
[2] https://arxiv.org/abs/1810.04805
[3] https://arxiv.org/abs/1809.05053
[4] https://huggingface.co/datasets/stsb_multi_mt

🔧 Technical Details

This model is based on the English bert-base-uncased pre-trained model [1, 2]. It was first fine-tuned on our learning object (LO) sentences dataset. This dataset consists of a sample of 500k sentences of course descriptions. We used standard parameter settings for fine-tuning as mentioned in the original BERT paper [2]. This allows the model to improve its performance on the target task (Masked Language Model) for domain-specific sentences. It was then fine-tuned on a natural language inference task (XNLI) [3]. This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication). It was then fine-tuned on a text semantic similarity task (on STS data) [4]. This task consists in training the model to estimate the similarity between two sentences. This fine-tuning process allows our model to have a semantic representation of words that is much better than the one proposed by the base model.

📄 License

No license information provided in the original document.