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	---
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- feature-extraction
	- sentence-similarity
	- transformers
	- legal
	- french-law
	- droit français
	- tax
	- droit fiscal
	- fiscalité
	license: apache-2.0
	pretty_name: Domain-adapted mBERT for French Tax Practice
	datasets:
	- louisbrulenaudet/lpf
	- louisbrulenaudet/cgi
	- louisbrulenaudet/code-douanes

	language:
	- fr
	library_name: sentence-transformers
	---

	# Domain-adapted mBERT for French Tax Practice

	This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

	Pretrained transformers model on the top 102 languages with the largest Wikipedia using a masked language modeling (MLM) objective, fitted using Transformer-based Sequential Denoising Auto-Encoder for unsupervised sentence embedding learning with one objective : french tax domain adaptation.

	This way, the model learns an inner representation of the french legal language in the training set that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the model as inputs.

	## Usage (Sentence-Transformers)

	Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

	```
	pip install -U sentence-transformers
	```

	Then you can use the model like this:

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

	model = SentenceTransformer("louisbrulenaudet/tsdae-lemone-mbert-tax")
	embeddings = model.encode(sentences)
	print(embeddings)
	```

	## Usage (HuggingFace Transformers)
	Without [sentence-transformers](https://www.SBERT.net), 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.

	```python
	from transformers import AutoTokenizer, AutoModel
	import torch


	def cls_pooling(model_output, attention_mask):
	return model_output[0][:,0]


	# 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("louisbrulenaudet/tsdae-lemone-mbert-tax")
	model = AutoModel.from_pretrained("louisbrulenaudet/tsdae-lemone-mbert-tax")

	# 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, cls pooling.
	sentence_embeddings = cls_pooling(model_output, encoded_input["attention_mask"])

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

	## Training
	The model was trained with the parameters:

	DataLoader:

	`torch.utils.data.dataloader.DataLoader` of length 5507 with parameters:
	```
	{'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
	```

	Loss:

	`sentence_transformers.losses.DenoisingAutoEncoderLoss.DenoisingAutoEncoderLoss`

	Parameters of the fit()-Method:
	```
	{
	"epochs": 1,
	"evaluation_steps": 0,
	"max_grad_norm": 1,
	"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
	"optimizer_params": {
	"lr": 3e-05
	},
	"scheduler": "constantlr",
	"steps_per_epoch": null,
	"warmup_steps": 10000,
	"weight_decay": 0
	}
	```

	## Full Model Architecture
	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
	)
	```

	## Citing & Authors

	If you use this code in your research, please use the following BibTeX entry.

	```BibTeX
	@misc{louisbrulenaudet2023,
	author = {Louis Brulé Naudet},
	title = {Domain-adapted mBERT for French Tax Practice},
	year = {2023}
	howpublished = {\url{https://huggingface.co/louisbrulenaudet/tsdae-lemone-mbert-tax}},
	}
	```

	## Feedback

	If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).