Instructions to use skypro1111/mbart-large-50-verbalization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skypro1111/mbart-large-50-verbalization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("skypro1111/mbart-large-50-verbalization") model = AutoModelForSeq2SeqLM.from_pretrained("skypro1111/mbart-large-50-verbalization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -6,13 +6,14 @@ tags: []
|
|
| 6 |
# Model Card for mbart-large-50-verbalization
|
| 7 |
|
| 8 |
## Model Description
|
| 9 |
-
`mbart-large-50-verbalization` is a fine-tuned version of the
|
| 10 |
|
| 11 |
## Architecture
|
| 12 |
-
This model is based on the
|
| 13 |
|
| 14 |
## Training Data
|
| 15 |
The model was fine-tuned on a subset of 96,780 sentences from the Ubertext dataset, focusing on news content. The verbalized equivalents were created using Google Gemini Pro, providing a rich basis for learning text transformation tasks.
|
|
|
|
| 16 |
|
| 17 |
## Training Procedure
|
| 18 |
The model underwent nearly 70,000 training steps, amounting to almost 2 epochs, to ensure thorough learning from the training dataset.
|
|
|
|
| 6 |
# Model Card for mbart-large-50-verbalization
|
| 7 |
|
| 8 |
## Model Description
|
| 9 |
+
`mbart-large-50-verbalization` is a fine-tuned version of the [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) model, specifically designed for the task of verbalizing Ukrainian text to prepare it for Text-to-Speech (TTS) systems. This model aims to transform structured data like numbers and dates into their fully expanded textual representations in Ukrainian.
|
| 10 |
|
| 11 |
## Architecture
|
| 12 |
+
This model is based on the [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) architecture, renowned for its effectiveness in translation and text generation tasks across numerous languages.
|
| 13 |
|
| 14 |
## Training Data
|
| 15 |
The model was fine-tuned on a subset of 96,780 sentences from the Ubertext dataset, focusing on news content. The verbalized equivalents were created using Google Gemini Pro, providing a rich basis for learning text transformation tasks.
|
| 16 |
+
Dataset [skypro1111/ubertext-2-news-verbalized](https://huggingface.co/datasets/skypro1111/ubertext-2-news-verbalized)
|
| 17 |
|
| 18 |
## Training Procedure
|
| 19 |
The model underwent nearly 70,000 training steps, amounting to almost 2 epochs, to ensure thorough learning from the training dataset.
|