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.
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 was monitored using TensorBoard, and the following metrics were observed:
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:
To use this model, you can load it using the Hugging Face Transformers library as follows:
1from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
2
3tokenizer = AutoTokenizer.from_pretrained("SchnabelTim/t5-catify-de-en")
4model = AutoModelForSeq2SeqLM.from_pretrained("SchnabelTim/t5-catify-de-en")
5
6def catify_text(input_text):
7 inputs = tokenizer(input_text, return_tensors="pt")
8 outputs = model.generate(**inputs)
9 return tokenizer.decode(outputs[0], skip_special_tokens=True)
10
11input_text = "What is a Human?"
12print(catify_text(input_text)) # Output: "What is a cat?"
