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 pytorch_model.bin from ThomasLI/roberta-base-finetuned-classification: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/ThomasLI/roberta-base-finetuned-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ThomasLI/roberta-base-finetuned-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ThomasLI/roberta-base-finetuned-classification/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 787a6b3d5b7af4eb07e4cfb87e26077162adeadafed323dc29c90cd3b699d28d
- Size of remote file:
- 499 MB
- SHA256:
- 46033980bfcfd7ce0fca1f4fa8476c08c313f219e273e780464dcb080c607c90
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