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--- |
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license: bigscience-bloom-rail-1.0 |
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datasets: |
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- xnli |
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language: |
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- fr |
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- en |
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pipeline_tag: zero-shot-classification |
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--- |
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## Presentation |
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We introduce the Bloomz-560m-NLI model, fine-tuned on the [Bloomz-560m-chat-dpo](https://huggingface.co/cmarkea/bloomz-560m-dpo-chat) foundation model. This model is trained on a Natural Language Inference (NLI) task in a language-agnostic manner. The NLI task involves determining the semantic relationship between a hypothesis and a set of premises, often expressed as pairs of sentences. It should be noted that hypotheses and premises are randomly chosen between English and French, with each language combination representing a probability of 25%. |
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## Zero-shot Classification |
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The primary appeal of training such models lies in their zero-shot classification performance. This means the model is capable of classifying any text with any label without specific training. What sets the Bloomz-560m-NLI LLMs apart in this realm is their ability to model and extract information from significantly more complex and lengthy test structures compared to models like BERT, RoBERTa, or CamemBERT. |
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The zero-shot classification 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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With *i* representing a hypothesis composed of a template (for example, "This text is about {}.") and *#C* candidate labels ("cinema", "politics", etc.), the set of hypotheses comprises {"This text is about cinema.", "This text is about politics.", ...}. It is these hypotheses that we will measure against the premise, which is the sentence we aim to classify. |
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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/bloomz-3b-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": [0.6797838807106018, |
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0.1440986692905426, |
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0.09773541986942291, |
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0.07838203758001328]} |
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# Resilience in cross-language French/English context |
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result = classifier ( |
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sequences="Quentin Tarantino's very cinephile style is " |
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"recognized, among other things, by his postmodern and " |
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"non-linear narration, his elaborate dialogues often " |
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"peppered with references to popular culture, and his " |
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"highly aesthetic but extremely violent scenes, inspired by " |
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"exploitation films, martial arts or spaghetti western.", |
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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": [0.6970456838607788, |
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0.17720822989940643, |
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0.06449680775403976, |
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0.0612492673099041]} |
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``` |