README.md

metadata

license: bigscience-bloom-rail-1.0
datasets:
  - xnli
language:
  - fr
  - en
pipeline_tag: zero-shot-classification

Presentation

We introduce the Bloomz-560m-NLI model, fine-tuned on the Bloomz-560m-chat-dpo 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%.

Zero-shot Classification

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.

The zero-shot classification task can be summarized by: $$P(hypothesis=i\in \mathcal{C}\mathrm{\mid }premise)=\frac{e^{P(premise=entailment\mathrm{\mid }hypothesis=i)}}{\sum _{j\in \mathcal{C}}{e}^{P(premise=entailment\mathrm{\mid }hypothesis=j)}}P(hypothesis=i\backslash in\backslash mathcal\{C\}|premise)=\backslash frac\{e^\{P(premise=entailment\backslash vert\; hypothesis=i)\}\}\{\backslash sum\_\{j\backslash in\backslash mathcal\{C\}\}e^\{P(premise=entailment\backslash vert\; hypothesis=j)\}\}$$ 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.

from transformers import pipeline

classifier = pipeline(
    task='zero-shot-classification',
    model="cmarkea/bloomz-3b-nli"
)
result = classifier (
    sequences="Le style très cinéphile de Quentin Tarantino "
    "se reconnaît entre autres par sa narration postmoderne "
    "et non linéaire, ses dialogues travaillés souvent "
    "émaillés de références à la culture populaire, et ses "
    "scènes hautement esthétiques mais d'une violence "
    "extrême, inspirées de films d'exploitation, d'arts "
    "martiaux ou de western spaghetti.",
    candidate_labels="cinéma, technologie, littérature, politique",
    hypothesis_template="Ce texte parle de {}."
)

result
{"labels": ["cinéma",
            "littérature",
            "technologie",
            "politique"],
 "scores": [0.6797838807106018,
            0.1440986692905426,
            0.09773541986942291,
            0.07838203758001328]}

# Resilience in cross-language French/English context
result = classifier (
    sequences="Quentin Tarantino's very cinephile style is "
    "recognized, among other things, by his postmodern and "
    "non-linear narration, his elaborate dialogues often "
    "peppered with references to popular culture, and his "
    "highly aesthetic but extremely violent scenes, inspired by "
    "exploitation films, martial arts or spaghetti western.",
    candidate_labels="cinéma, technologie, littérature, politique",
    hypothesis_template="Ce texte parle de {}."
)

result
{"labels": ["cinéma",
            "littérature",
            "technologie",
            "politique"],
 "scores": [0.6970456838607788,
            0.17720822989940643,
            0.06449680775403976,
            0.0612492673099041]}