Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use ThomasLI/roberta-base-finetuned-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThomasLI/roberta-base-finetuned-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ThomasLI/roberta-base-finetuned-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ThomasLI/roberta-base-finetuned-classification") model = AutoModelForSequenceClassification.from_pretrained("ThomasLI/roberta-base-finetuned-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ThomasLI/roberta-base-finetuned-classification: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/ThomasLI/roberta-base-finetuned-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://ThomasLI/roberta-base-finetuned-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ThomasLI/roberta-base-finetuned-classification/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- e3b1e13260f5d5c14af10fdcb262e42c30cdfad54dcc00c08c8682c2a36cc5ab
- Size of remote file:
- 3.52 kB
- SHA256:
- c70980b0f007875dd928ab3a6eeac97dd6e192ae42f44f4cd54f5997acd1a66b
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