Instructions to use mo374z/theoffice_speaker_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mo374z/theoffice_speaker_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mo374z/theoffice_speaker_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mo374z/theoffice_speaker_classification") model = AutoModelForSequenceClassification.from_pretrained("mo374z/theoffice_speaker_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mo374z/theoffice_speaker_classification: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/mo374z/theoffice_speaker_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mo374z/theoffice_speaker_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mo374z/theoffice_speaker_classification/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 87ad32fb1d4629e458e047c606edb95069dbb22719fc2b259e91b6ea9c8bfee8
- Size of remote file:
- 268 MB
- SHA256:
- 7326f2f150b67a001e186bcd1c8c288e6031a8593172fce3c7aaf9ceb77010f7
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