Update README.md

README.md

@@ -8,15 +8,37 @@ language:
 pipeline_tag: zero-shot-classification
 ---
 
-## Presentation
-We introduce the Bloomz-3b-NLI model, fine-tuned on the [Bloomz-3b-dpo-chat](https://huggingface.co/cmarkea/bloomz-3b-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%.
+# Presentation
+We introduce the Bloomz-3b-NLI model, fine-tuned on the [Bloomz-3b-chat-dpo](https://huggingface.co/cmarkea/bloomz-3b-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.
 
-## 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-3b-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 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:
+$$P(premise=c\in\{contradiction, entailment, neutral\}\vert hypothesis)$$
+
+### Language-agnostic approach
+It should be noted that hypotheses and premises are randomly chosen between English and French, with each language combination representing a probability of 25%.
+
+### Detaset
+
+### Performance
+
+# 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-3b-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:
 $$P(hypothesis=i\in\mathcal{C}|premise)=\frac{e^{P(premise=entailment\vert hypothesis=i)}}{\sum_{j\in\mathcal{C}}e^{P(premise=entailment\vert hypothesis=j)}}$$
-With *i* representing a hypothesis composed of a template (for example, "This text is about {}.") and 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.
+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.
+
+### Performance
+
+# How to use Bloomz-560m-NLI
 
 ```python
 from transformers import pipeline