Instructions to use AleksandrAlgazinov/ModCon-Task-Identifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AleksandrAlgazinov/ModCon-Task-Identifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AleksandrAlgazinov/ModCon-Task-Identifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AleksandrAlgazinov/ModCon-Task-Identifier") model = AutoModelForSequenceClassification.from_pretrained("AleksandrAlgazinov/ModCon-Task-Identifier") - Notebooks
- Google Colab
- Kaggle
Model Description
We introduce ModCon-Task-Identifier, a fine-tuned BERT model that is capable of identifying the modality conversion task type based on the user’s prompt. For instance, if the user’s prompt is ‘read this text’, the model will output ‘TTS’ (Text-to-Speech). The model was developed as a part of the Multi-Agent MATE project, the goal of which is to develop a universal multi-agent modality conversion framework. Based on the user’s query, the system will convert the input file to the desired format by changing the modality (for instance, a text can be converted to an image, or a video can be converted to an audio)
The official project repository and the full project code are available at https://github.com/AlgazinovAleksandr/Multi-Agent-MATE
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