---
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](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%.

## 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.

```python
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]}
```