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--- |
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language: fr |
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license: mit |
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tags: |
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- zero-shot-classification |
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- sentence-similarity |
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- nli |
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pipeline_tag: zero-shot-classification |
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widget: |
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- text: >- |
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Selon certains physiciens, un univers parallèle, miroir du nôtre ou relevant |
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de ce que l'on appelle la théorie des branes, autoriserait des neutrons à |
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sortir de notre Univers pour y entrer à nouveau. L'idée a été testée une |
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nouvelle fois avec le réacteur nucléaire de l'Institut Laue-Langevin à |
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Grenoble, plus précisément en utilisant le détecteur de l'expérience Stereo |
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initialement conçu pour chasser des particules de matière noire |
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potentielles, les neutrinos stériles. |
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candidate_labels: politique, science, sport, santé |
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hypothesis_template: Ce texte parle de {}. |
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datasets: |
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- flue |
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base_model: |
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- cmarkea/distilcamembert-base |
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--- |
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DistilCamemBERT-NLI |
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=================== |
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We present DistilCamemBERT-NLI, which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine-tuned for the Natural Language Inference (NLI) task for the french language, also known as recognizing textual entailment (RTE). This model is constructed on the XNLI dataset, which determines whether a premise entails, contradicts or neither entails or contradicts a hypothesis. |
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This modelization is close to [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) based on [CamemBERT](https://huggingface.co/camembert-base) 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 especially in the context of cross-encoding like this task. To counteract this effect, we propose this modelization which divides the inference time by 2 with the same consumption power, thanks to DistilCamemBERT. |
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Dataset |
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------- |
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The dataset XNLI from [FLUE](https://huggingface.co/datasets/flue) comprises 392,702 premises with their hypothesis for the train and 5,010 couples for the test. The goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B?) and is a classification task (given two sentences, predict one of three labels). Sentence A is called *premise*, and sentence B is called *hypothesis*, then the goal of modelization is determined as follows: |
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$$P(premise=c\in\{contradiction, entailment, neutral\}\vert hypothesis)$$ |
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Evaluation results |
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------------------ |
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| **class** | **precision (%)** | **f1-score (%)** | **support** | |
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| :----------------: | :---------------: | :--------------: | :---------: | |
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| **global** | 77.70 | 77.45 | 5,010 | |
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| **contradiction** | 78.00 | 79.54 | 1,670 | |
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| **entailment** | 82.90 | 78.87 | 1,670 | |
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| **neutral** | 72.18 | 74.04 | 1,670 | |
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Benchmark |
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--------- |
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We compare the [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) model to 2 other modelizations working on the french language. The first one [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) is based on well named [CamemBERT](https://huggingface.co/camembert-base), the french RoBERTa model and the second one [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) based on [mDeBERTav3](https://huggingface.co/microsoft/mdeberta-v3-base) a multilingual model. To compare the performances, the metrics of accuracy and [MCC (Matthews Correlation Coefficient)](https://en.wikipedia.org/wiki/Phi_coefficient) were used. We used an **AMD Ryzen 5 4500U @ 2.3GHz with 6 cores** for mean inference time measure. |
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| **model** | **time (ms)** | **accuracy (%)** | **MCC (x100)** | |
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| :--------------: | :-----------: | :--------------: | :------------: | |
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| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **51.35** | 77.45 | 66.24 | |
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| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 105.0 | 81.72 | 72.67 | |
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| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 299.18 | **83.43** | **75.15** | |
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Zero-shot classification |
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------------------------ |
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The main advantage of such modelization is to create a zero-shot classifier allowing text classification without training. This task can be summarized by: |
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$$P(hypothesis=i\in\mathcal{C}|premise)=\frac{e^{P(premise=entailment\vert hypothesis=i)}}{\sum_{j\in\mathcal{C}}e^{P(premise=entailment\vert hypothesis=j)}}$$ |
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For this part, we use two datasets, the first one: [allocine](https://huggingface.co/datasets/allocine) used to train the sentiment analysis models. The dataset comprises two classes: "positif" and "négatif" appreciation of movie reviews. Here we use "Ce commentaire est {}." as the hypothesis template and "positif" and "négatif" as candidate labels. |
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| **model** | **time (ms)** | **accuracy (%)** | **MCC (x100)** | |
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| :--------------: | :-----------: | :--------------: | :------------: | |
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| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **195.54** | 80.59 | 63.71 | |
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| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 378.39 | **86.37** | **73.74** | |
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| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 520.58 | 84.97 | 70.05 | |
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The second one: [mlsum](https://huggingface.co/datasets/mlsum) used to train the summarization models. In this aim, we aggregate sub-topics and select a few of them. We use the articles summary part to predict their topics. In this case, the hypothesis template used is "C'est un article traitant de {}." and the candidate labels are: "économie", "politique", "sport" and "science". |
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| **model** | **time (ms)** | **accuracy (%)** | **MCC (x100)** | |
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| :--------------: | :-----------: | :--------------: | :------------: | |
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| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **217.77** | **79.30** | **70.55** | |
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| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 448.27 | 70.7 | 64.10 | |
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| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 591.34 | 64.45 | 58.67 | |
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How to use DistilCamemBERT-NLI |
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------------------------------ |
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```python |
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from transformers import pipeline |
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classifier = pipeline( |
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task='zero-shot-classification', |
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model="cmarkea/distilcamembert-base-nli", |
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tokenizer="cmarkea/distilcamembert-base-nli" |
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) |
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result = classifier ( |
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sequences="Le style très cinéphile de Quentin Tarantino " |
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"se reconnaît entre autres par sa narration postmoderne " |
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"et non linéaire, ses dialogues travaillés souvent " |
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"émaillés de références à la culture populaire, et ses " |
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"scènes hautement esthétiques mais d'une violence " |
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"extrême, inspirées de films d'exploitation, d'arts " |
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"martiaux ou de western spaghetti.", |
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candidate_labels="cinéma, technologie, littérature, politique", |
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hypothesis_template="Ce texte parle de {}." |
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) |
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result |
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{"labels": ["cinéma", |
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"littérature", |
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"technologie", |
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"politique"], |
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"scores": [0.7164115309715271, |
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0.12878799438476562, |
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0.1092301607131958, |
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0.0455702543258667]} |
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``` |
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### Optimum + ONNX |
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```python |
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from optimum.onnxruntime import ORTModelForSequenceClassification |
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from transformers import AutoTokenizer, pipeline |
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HUB_MODEL = "cmarkea/distilcamembert-base-nli" |
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tokenizer = AutoTokenizer.from_pretrained(HUB_MODEL) |
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model = ORTModelForSequenceClassification.from_pretrained(HUB_MODEL) |
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onnx_qa = pipeline("zero-shot-classification", model=model, tokenizer=tokenizer) |
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# Quantized onnx model |
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quantized_model = ORTModelForSequenceClassification.from_pretrained( |
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HUB_MODEL, file_name="model_quantized.onnx" |
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) |
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``` |
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Citation |
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-------- |
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```bibtex |
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@inproceedings{delestre:hal-03674695, |
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TITLE = {{DistilCamemBERT : une distillation du mod{\`e}le fran{\c c}ais CamemBERT}}, |
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AUTHOR = {Delestre, Cyrile and Amar, Abibatou}, |
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URL = {https://hal.archives-ouvertes.fr/hal-03674695}, |
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BOOKTITLE = {{CAp (Conf{\'e}rence sur l'Apprentissage automatique)}}, |
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ADDRESS = {Vannes, France}, |
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YEAR = {2022}, |
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MONTH = Jul, |
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KEYWORDS = {NLP ; Transformers ; CamemBERT ; Distillation}, |
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PDF = {https://hal.archives-ouvertes.fr/hal-03674695/file/cap2022.pdf}, |
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HAL_ID = {hal-03674695}, |
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HAL_VERSION = {v1}, |
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} |
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``` |
