# DistilCamemBERT-Sentiment

We present DistilCamemBERT-Sentiment which is DistilCamemBERT fine tuned for the sentiment analysis task for the French language. This model is constructed over 2 datasets: Amazon Reviews and Allociné.fr in order to minimize the bias. Indeed, Amazon reviews are very similar in the messages and relatively shorts, contrary to Allociné critics which are long and rich texts.

This modelization is close to tblard/tf-allocine based on CamemBERT model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase for example. Indeed, inference cost can be a technological issue. To counteract this effect, we propose this modelization which divides the inference time by 2 with the same consumption power thanks to DistilCamemBERT.

## Dataset

The dataset is composed of 204,993 reviews for training and 4,999 reviews for the test coming from Amazon, and respectively 235,516 and 4,729 critics from Allocine website. The dataset is labeled into 5 categories:

• 1 star: represents a very bad appreciation,
• 2 stars: bad appreciation,
• 3 stars: neutral appreciation,
• 4 stars: good appreciation,
• 5 stars: very good appreciation.

## Evaluation results

In addition of accuracy (called here exact accuracy) in order to be robust to +/-1 star estimation errors, we will take the following definition as a performance measure:

$\mathrm{t}\mathrm{o}\mathrm{p}\text{\hspace{0.17em}⁣}-\text{\hspace{0.17em}⁣}2\text{ }\mathrm{a}\mathrm{c}\mathrm{c}=\frac{1}{\mathrm{\mid }\mathcal{O}\mathrm{\mid }}\sum _{i\in \mathcal{O}}\sum _{0\le l<2}\mathbb{1}\left({\stackrel{^}{f}}_{i,l}={y}_{i}\right)\mathrm\left\{top\!-\!2\; acc\right\}=\frac\left\{1\right\}\left\{|\mathcal\left\{O\right\}|\right\}\sum_\left\{i\in\mathcal\left\{O\right\}\right\}\sum_\left\{0\leq l < 2\right\}\mathbb\left\{1\right\}\left(\hat\left\{f\right\}_\left\{i,l\right\}=y_i\right)$

where ${\stackrel{^}{f}}_{l}\hat\left\{f\right\}_l$ is the l-th largest predicted label, $yy$ the true label, $\mathcal{O}\mathcal\left\{O\right\}$ is the test set of the observations and $\mathbb{1}\mathbb\left\{1\right\}$ is the indicator function.

class exact accuracy (%) top-2 acc (%) support
global 61.01 88.80 9,698
1 star 87.21 77.17 1,905
2 stars 79.19 84.75 1,935
3 stars 77.85 78.98 1,974
4 stars 78.61 90.22 1,952
5 stars 85.96 82.92 1,932

## Benchmark

This model is compared to 3 reference models (see below). As each model doesn't have the same definition of targets, we detail the performance measure used for each of them. For the mean inference time measure, an AMD Ryzen 5 4500U @ 2.3GHz with 6 cores was used.

#### bert-base-multilingual-uncased-sentiment

nlptown/bert-base-multilingual-uncased-sentiment is based on BERT model in the multilingual and uncased version. This sentiment analyzer is trained on Amazon reviews similarly to our model, hence the targets and their definitions are the same.

model time (ms) exact accuracy (%) top-2 acc (%)
cmarkea/distilcamembert-base-sentiment 95.56 61.01 88.80
nlptown/bert-base-multilingual-uncased-sentiment 187.70 54.41 82.82

#### tf-allociné and barthez-sentiment-classification

tblard/tf-allocine based on CamemBERT model and moussaKam/barthez-sentiment-classification based on BARThez use the same bi-class definition between them. To bring this back to a two-class problem, we will only consider the "1 star" and "2 stars" labels for the negative sentiments and "4 stars" and "5 stars" for positive sentiments. We exclude the "3 stars" which can be interpreted as a neutral class. In this context, the problem of +/-1 star estimation errors disappears. Then we use only the classical accuracy definition.

model time (ms) exact accuracy (%)
cmarkea/distilcamembert-base-sentiment 95.56 97.52
tblard/tf-allocine 329.74 95.69
moussaKam/barthez-sentiment-classification 197.95 94.29

## How to use DistilCamemBERT-Sentiment

from transformers import pipeline

analyzer = pipeline(
task='text-classification',
model="cmarkea/distilcamembert-base-sentiment",
tokenizer="cmarkea/distilcamembert-base-sentiment"
)
result = analyzer(
"J'aime me promener en forêt même si ça me donne mal aux pieds.",
return_all_scores=True
)

result
[{'label': '1 star',
'score': 0.047529436647892},
{'label': '2 stars',
'score': 0.14150355756282806},
{'label': '3 stars',
'score': 0.3586442470550537},
{'label': '4 stars',
'score': 0.3181498646736145},
{'label': '5 stars',
'score': 0.13417290151119232}]


## Citation

@inproceedings{delestre:hal-03674695,
TITLE = {{DistilCamemBERT : une distillation du mod{\e}le fran{\c c}ais CamemBERT}},
AUTHOR = {Delestre, Cyrile and Amar, Abibatou},
URL = {https://hal.archives-ouvertes.fr/hal-03674695},
BOOKTITLE = {{CAp (Conf{\'e}rence sur l'Apprentissage automatique)}},
ADDRESS = {Vannes, France},
YEAR = {2022},
MONTH = Jul,
KEYWORDS = {NLP ; Transformers ; CamemBERT ; Distillation},
PDF = {https://hal.archives-ouvertes.fr/hal-03674695/file/cap2022.pdf},
HAL_ID = {hal-03674695},
HAL_VERSION = {v1},
}
`
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