distilcamembert base sentiment EU AI Act Compliance Profile

DistilCamemBERT-Sentiment

We present DistilCamemBERT-Sentiment, which is DistilCamemBERT fine-tuned for the sentiment analysis task for the French language. This model is built using two datasets: Amazon Reviews and Allociné.fr to minimize the bias. Indeed, Amazon reviews are similar in messages and relatively shorts, contrary to Allociné critics, who 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 two with the same consumption power thanks to DistilCamemBERT.

Dataset

The dataset comprises 204,993 reviews for training and 4,999 reviews for the test from Amazon, and 235,516 and 4,729 critics from Allocine website. The dataset is labeled into five categories:

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

$$\mathrm{top!-!2; acc}=\frac{1}{|\mathcal{O}|}\sum_{i\in\mathcal{O}}\sum_{0\leq l < 2}\mathbb{1}(\hat{f}_{i,l}=y_i)$$

where \(\hat{f}_l\) is the l-th largest predicted label, \(y\) the true label, \(\mathcal{O}\) is the test set of the observations and

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Library

Base Model