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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: mit | |
| ## 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** |