# inokufu/bertheo

A sentence-transformers model fine-tuned on course sentences. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

## Details

This model is based on the French flaubert-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 [3]. 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) [4]. 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-fr data) [5]. 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.

## 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 = ["Apprendre le python", "Devenir expert en comptabilité"]

model = SentenceTransformer('inokufu/flaubert-base-uncased-xnli-sts-finetuned-education')
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
token_embeddings = model_output[0] #First element of model_output contains all token embeddings

# Sentences we want sentence embeddings for
sentences = ["Apprendre le python", "Devenir expert en comptabilité"]

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

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.

print("Sentence embeddings:")
print(sentence_embeddings)


## Evaluation Results

STS (fr) score: 83.05%

## Model Architecture

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