Instructions to use dipit099/segmentation-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dipit099/segmentation-fine-tuned with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dipit099/segmentation-fine-tuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from dipit099/segmentation-fine-tuned: direct link, hf CLI and curl.
- Browser
- Download file 540 Bytes
-
https://huggingface.co/dipit099/segmentation-fine-tuned/resolve/main/README.md
- Command line
-
hf download hf://dipit099/segmentation-fine-tuned/README.md
-
curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/dipit099/segmentation-fine-tuned/resolve/main/README.md
540 Bytes
metadata
library_name: transformers
language:
- bn
license: mit
base_model: pyannote/segmentation-3.0
tags:
- generated_from_trainer
datasets:
- bengali-speaker-diarization
model-index:
- name: speaker-segmentation-fine-tuned-bn-v2
results: []
Framework versions
- Transformers 4.48.3
- Pytorch 2.9.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.4