Update README.md

README.md

@@ -17,8 +17,8 @@ base_model:
 
 [Paper](https://arxiv.org/abs/2505.20161) [Project Page](https://nvlabs.github.io/prismatic-synthesis/)
 
-PrismNLI-0.4B is a compact yet powerful expert model, purpose-built for natural language inference (NLI) and zero-shot classification. 
-**Despite its small size, it delivers state-of-the-art performance on 8 NLI benchmarks**, making it a go-to solution for high-accuracy, low-latency NLI applications.
+PrismNLI-0.4B is a compact yet powerful model, purpose-built for natural language inference (NLI) and zero-shot classification. 
+**Despite its small size, it delivers state-of-the-art performance on 8 NLI benchmarks**, making it a go-to solution for high-accuracy, low-latency applications.
 
 PrismNLI-0.4B is fine-tuned from [deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) 
 on our high-quality dataset [PrismNLI](https://huggingface.co/datasets/Jaehun/PrismNLI), curated specifically to improve generalization of the trained model.
@@ -29,37 +29,40 @@ The enhancement includes:
 - Instead of starting from scratch, we start from [deberta-v3-large-zeroshot-v2.0](https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v2.0), a checkpoint of
 deberta-v3-lage trained on diverse classification data.
 - Following prior works on entailment models, we reformulate the traditional 3-way NLI classification—`entailment`, `neutral`, and `contradiction`—into a binary setup:
- `entailment` vs. `not-entailment`. This simplification enables the model to act as a **universal classifier**, by asking a single, intuitive question: *Is this hypothesis true, given the premise?*
+ `entailment` vs. `not-entailment`. This simplification helps the model to better act as a **universal classifier** by simply asking: *Is this hypothesis true, given the premise?*
 
+| **Model**                      | **Average** | **HANS** | **WNLI** | **ANLI-r1** | **ANLI-r2** | **ANLI-r3** | **Diagnostics** | **BigBench** | **Control** |
+|--------------------------------|-------------|----------|----------|-------------|-------------|-------------|-----------------|--------------|-------------|
+| deberta-v3-large-zeroshot-v2.0 | 79.47       | 81.28    | 70.68    | 86.40       | 77.60       | 77.50       | 83.59           | 87.03        | 71.68       |
+| modernBERT-large-zeroshot-v2.0 | 74.78       | 80.30    | 66.00    | 81.20       | 71.50       | 71.67       | 82.05           | 73.18        | 72.30       |
+| deberta-v3-large-mfalw         | 80.62       | 81.10    | **74.08**| 86.30       | **79.90**   | 78.33       | 85.22           | 85.61        | 74.40       |
+| PrismNLI-0.4B                  | **82.88**   | **90.68**| 72.95    | **87.70**   | 78.80       | **79.58**   | **86.22**       | **90.59**    | **76.52**   |
 
-# Training Data
+
+## Training Data
 The model has been fine-tuned on 515K NLI datapoints from [PrismNLI](https://huggingface.co/datasets/Jaehun/PrismNLI), a synthetic dataset to improve generalization of
 NLI models. The dataset has been generated by [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) via our algorithm, Prismatic Synthesis that scales synthetic data while improving the diversity of generated samples.
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
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-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
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-
+## Model Usage
+The model can be used as a standard NLI (entailment detection) classifier. Label `0` denotes `entailment`, and Label `l` denotes `not-entailment`.
+Beyond NLI, the model can serve as a zero-shot classifier:
+```python
+from transformers import pipeline
+
+text = "It was baked in a wood-fired oven and topped with San Marzano tomatoes and buffalo mozzarella."
+hypothesis_template = "This text is about {}"
+classes_verbalized = ['pizza', 'pasta', 'salad', 'sushi']
+zeroshot_classifier = pipeline("zero-shot-classification", model="Jaehun/PrismNLI-0.4B")
+output = zeroshot_classifier(text, classes_verbalized, hypothesis_template=hypothesis_template, multi_label=False)
+```
+The output will be like:
+```python
+{
+  'sequence': 'It was baked in a wood-fired oven and topped with San Marzano tomatoes and buffalo mozzarella.',
+  'labels': ['pizza', 'pasta', 'salad', 'sushi'],
+  'scores': [0.9982, 0.0014, 0.0002, 0.0002],
+}
+```
 
 ## Citation
 If you find this model useful, please consider citing us!