Model Card for SchnabelTim/t5-catify-de-en
Model Details
- Model Name: SchnabelTim/t5-catify-de-en
- Model Architecture: T5
- Base Model: Einmalumdiewelt/T5-Base_GNAD
- Language(s): German, English
- License: MIT
- Author: SchnabelTim
Model Description
SchnabelTim/t5-catify-de-en is a Transformer-based model fine-tuned from the T5 architecture. It is designed to transform person-related data into cat-related data, functioning effectively in both German and English. The model can take input sentences that are about people and convert them to be about cats, maintaining the original context and meaning as much as possible.
Training Data
The model was trained on a self-created dataset. The dataset includes sentences related to people and their corresponding cat-related transformations. This dataset was curated to ensure diverse and contextually rich examples for robust performance across various scenarios.
Training Procedure
- Number of Epochs: 5
- Batch Size: 12
- Optimizer: AdamW
- Learning Rate: 5e-5
Training was monitored using TensorBoard, and the following metrics were observed:
- Training Loss: 0.098
- Validation Loss: 0.096
Evaluation
The model was evaluated on a held-out test set from the same distribution as the training data. The following metrics were used to assess model performance:
- Test Loss: 0.096
Usage
To use this model, you can load it using the Hugging Face Transformers library as follows:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("SchnabelTim/t5-catify-de-en")
model = AutoModelForSeq2SeqLM.from_pretrained("SchnabelTim/t5-catify-de-en")
def catify_text(input_text):
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
input_text = "What is a Human?"
print(catify_text(input_text)) # Output: "What is a cat?"
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