Instructions to use mahaamami/distilroberta-base-model-transcript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mahaamami/distilroberta-base-model-transcript with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mahaamami/distilroberta-base-model-transcript")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mahaamami/distilroberta-base-model-transcript") model = AutoModelForMaskedLM.from_pretrained("mahaamami/distilroberta-base-model-transcript", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mahaamami/distilroberta-base-model-transcript: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/mahaamami/distilroberta-base-model-transcript/resolve/main/training_args.bin
- Command line
-
hf download hf://mahaamami/distilroberta-base-model-transcript/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mahaamami/distilroberta-base-model-transcript/resolve/main/training_args.bin
2.99 kB
- Xet hash:
- 4a3e911a9888116c8d923254b502e40d22343ebd6e98a7ae13232b8bd40038e9
- Size of remote file:
- 2.99 kB
- SHA256:
- bf97b7334048948d28c6398c69ac723601ef4c824254f4ee87820ce1e43cc845
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.