
# hugorosen/flaubert_base_uncased-xnli-sts

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.

<!--- Describe your model here -->

## 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 = ["Ceci est une phrase d'exemple", "Chaque phrase est convertie"]

model = SentenceTransformer('hugorosen/flaubert_base_uncased-xnli-sts')
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


#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 = ["Un avion est en train de décoller.",
          "Un homme joue d'une grande flûte.",
          "Un homme étale du fromage râpé sur une pizza.",
          "Une personne jette un chat au plafond.",
          "Une personne est en train de plier un morceau de papier.",
          ]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('hugorosen/flaubert_base_uncased-xnli-sts')
model = AutoModel.from_pretrained('hugorosen/flaubert_base_uncased-xnli-sts')

# 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

<!--- Describe how your model was evaluated -->

This model scores 76.9% on STS test (french)


## Training
### Pre-training

We use the pre-trained [flaubert/flaubert_base_uncased](https://huggingface.co/flaubert/flaubert_base_cased). Please refer to the model card for more detailed information about the pre-training procedure.

### Fine-tuning

we fine-tune the model using a `CosineSimilarityLoss` on XNLI and STS dataset (french).

Parameters of the fit()-Method:
```
{
    "epochs": 4,
    "evaluation_steps": 1000,
    "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": 144,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) 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})
)
```

## Citing & Authors

Fine-tuned for semantic similarity by Hugo Rosenkranz-costa.

Based on FlauBERT:

```
@InProceedings{le2020flaubert,
  author    = {Le, Hang  and  Vial, Lo\"{i}c  and  Frej, Jibril  and  Segonne, Vincent  and  Coavoux, Maximin  and  Lecouteux, Benjamin  and  Allauzen, Alexandre  and  Crabb\'{e}, Beno\^{i}t  and  Besacier, Laurent  and  Schwab, Didier},
  title     = {FlauBERT: Unsupervised Language Model Pre-training for French},
  booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
  month     = {May},
  year      = {2020},
  address   = {Marseille, France},
  publisher = {European Language Resources Association},
  pages     = {2479--2490},
  url       = {https://www.aclweb.org/anthology/2020.lrec-1.302}
}
```
{"language": "fr", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "fr"], "datasets": ["xnli", "stsb_multi_mt"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch flaubert feature-extraction sentence-similarity transformers fr dataset:xnli dataset:stsb_multi_mt endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #flaubert #feature-extraction #sentence-similarity #transformers #fr #dataset-xnli #dataset-stsb_multi_mt #endpoints_compatible #region-us 


# hugorosen/flaubert_base_uncased-xnli-sts

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.



## Usage (Sentence-Transformers)

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



Then you can use the model like this:





## 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.





## Evaluation Results



This model scores 76.9% on STS test (french)


## Training
### Pre-training

We use the pre-trained flaubert/flaubert_base_uncased. Please refer to the model card for more detailed information about the pre-training procedure.

### Fine-tuning

we fine-tune the model using a 'CosineSimilarityLoss' on XNLI and STS dataset (french).

Parameters of the fit()-Method:



## Full Model Architecture


## Citing & Authors

Fine-tuned for semantic similarity by Hugo Rosenkranz-costa.

Based on FlauBERT:


# hugorosen/flaubert_base_uncased-xnli-sts

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. ## Usage (Sentence-Transformers)

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



Then you can use the model like this: ## 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. ## Evaluation Results



This model scores 76.9% on STS test (french) ## Training ### Pre-training

We use the pre-trained flaubert/flaubert_base_uncased. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning

we fine-tune the model using a 'CosineSimilarityLoss' on XNLI and STS dataset (french).

Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors

Fine-tuned for semantic similarity by Hugo Rosenkranz-costa.

Based on FlauBERT:
TAGS
#sentence-transformers #pytorch #flaubert #feature-extraction #sentence-similarity #transformers #fr #dataset-xnli #dataset-stsb_multi_mt #endpoints_compatible #region-us 
 # hugorosen/flaubert_base_uncased-xnli-sts

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. ## Usage (Sentence-Transformers)

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



Then you can use the model like this: ## 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. ## Evaluation Results



This model scores 76.9% on STS test (french) ## Training ### Pre-training

We use the pre-trained flaubert/flaubert_base_uncased. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning

we fine-tune the model using a 'CosineSimilarityLoss' on XNLI and STS dataset (french).

Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors

Fine-tuned for semantic similarity by Hugo Rosenkranz-costa.

Based on FlauBERT:
passage: TAGS
#sentence-transformers #pytorch #flaubert #feature-extraction #sentence-similarity #transformers #fr #dataset-xnli #dataset-stsb_multi_mt #endpoints_compatible #region-us 
# hugorosen/flaubert_base_uncased-xnli-sts

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.## Usage (Sentence-Transformers)

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



Then you can use the model like this:## 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.## Evaluation Results



This model scores 76.9% on STS test (french)## Training### Pre-training

We use the pre-trained flaubert/flaubert_base_uncased. Please refer to the model card for more detailed information about the pre-training procedure.### Fine-tuning

we fine-tune the model using a 'CosineSimilarityLoss' on XNLI and STS dataset (french).

Parameters of the fit()-Method:## Full Model Architecture## Citing & Authors

Fine-tuned for semantic similarity by Hugo Rosenkranz-costa.

Based on FlauBERT: