Instructions to use FINDA-FIT/XLM-ROBERTA_LARGE_TRUE_FP_FALSE_0.3_FINETUNE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINDA-FIT/XLM-ROBERTA_LARGE_TRUE_FP_FALSE_0.3_FINETUNE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FINDA-FIT/XLM-ROBERTA_LARGE_TRUE_FP_FALSE_0.3_FINETUNE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FINDA-FIT/XLM-ROBERTA_LARGE_TRUE_FP_FALSE_0.3_FINETUNE") model = AutoModelForSequenceClassification.from_pretrained("FINDA-FIT/XLM-ROBERTA_LARGE_TRUE_FP_FALSE_0.3_FINETUNE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 467f10d5fe37670313f02487a3d408253e1ddf835c85ffbba9f35eaf9df3863a
- Size of remote file:
- 2.24 GB
- SHA256:
- 23b45ed7ac3cd688197f14d1a81bd2f0030a09d2d824e75b2821cbfffe90be9c
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