Instructions to use EducativeCS2023/roberta-similarity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EducativeCS2023/roberta-similarity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EducativeCS2023/roberta-similarity")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EducativeCS2023/roberta-similarity") model = AutoModelForSequenceClassification.from_pretrained("EducativeCS2023/roberta-similarity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from EducativeCS2023/roberta-similarity: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/EducativeCS2023/roberta-similarity/resolve/main/training_args.bin
- Command line
-
hf download hf://EducativeCS2023/roberta-similarity/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/EducativeCS2023/roberta-similarity/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 4397012ed5cc69c921eefd566fadd9d87739dee735dda3795ea3e873344d51da
- Size of remote file:
- 3.58 kB
- SHA256:
- 3aeaff2df2c43fef6dbb8b8381a7ee2d53ac9607d43ee09f7426aa3094ef3239
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