---
library_name: transformers
language:
- en
metrics:
- accuracy: 62.3 % accuracy on the 2-label liar test set.
pipeline_tag: text-classification
---

# Model Card for Model ID

This model classifies news statements as true or false.


## Model Details

### Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Finetuned from model:** https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

model = AutoPeftModelForCausalLM.from_pretrained("baris-yazici/liar_stabilityai_stablelm-2-zephyr-1_6b_PROMPT_TUNING_CAUSAL_LM").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-zephyr-1_6b")

```

## Training Details

### Training Data
The liar dataset can be accessed from: https://huggingface.co/datasets/liar.

### Training Procedure 
Prompt tuning was used: https://huggingface.co/docs/peft/task_guides/prompt_based_methods).
Trained on 2 epochs due to computational limitations.