Text Classification
Transformers
PyTorch
Enawené-Nawé
roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use davis901/roberta-frame-CP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davis901/roberta-frame-CP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="davis901/roberta-frame-CP")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("davis901/roberta-frame-CP") model = AutoModelForSequenceClassification.from_pretrained("davis901/roberta-frame-CP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from davis901/roberta-frame-CP: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/davis901/roberta-frame-CP/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://davis901/roberta-frame-CP@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/davis901/roberta-frame-CP/resolve/refs%2Fpr%2F1/tokenizer.json
2.11 MB
- Xet hash:
- 92c80957e6e1c67aef0502d2103aca0cb1e6549c1e84d58d00dfdfb89107dcbb
- Size of remote file:
- 2.11 MB
- SHA256:
- 0dd145b365bb0290901f396fd0d1d7cf040b0886a151b804d833f074ad323be9
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