Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use mnavas/roberta-finetuned-WebClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mnavas/roberta-finetuned-WebClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mnavas/roberta-finetuned-WebClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mnavas/roberta-finetuned-WebClassification") model = AutoModelForSequenceClassification.from_pretrained("mnavas/roberta-finetuned-WebClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mnavas/roberta-finetuned-WebClassification: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/mnavas/roberta-finetuned-WebClassification/resolve/main/training_args.bin
- Command line
-
hf download hf://mnavas/roberta-finetuned-WebClassification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mnavas/roberta-finetuned-WebClassification/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- 5ac0b3ea5214d92fd785fa2bd8f1843a71e20c04b70cc867212e81ecb8407785
- Size of remote file:
- 3.06 kB
- SHA256:
- aabed5638d5621e19ed381afa13d60c8be0953b896b8cdca2df7ccc6a33c5390
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