Instructions to use m-aliabbas1/roberta_en_med_merged_classes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m-aliabbas1/roberta_en_med_merged_classes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="m-aliabbas1/roberta_en_med_merged_classes")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("m-aliabbas1/roberta_en_med_merged_classes") model = AutoModelForSequenceClassification.from_pretrained("m-aliabbas1/roberta_en_med_merged_classes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from m-aliabbas1/roberta_en_med_merged_classes: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/m-aliabbas1/roberta_en_med_merged_classes/resolve/main/training_args.bin
- Command line
-
hf download hf://m-aliabbas1/roberta_en_med_merged_classes/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/m-aliabbas1/roberta_en_med_merged_classes/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 339e2b2511f37b8be029f0f3c6d8b71e29897be1b8372574c5dc630d80decafa
- Size of remote file:
- 5.43 kB
- SHA256:
- c07f506c2b58a154838be318101800d8008a652645095fd99b10694d5d666e4c
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