Instructions to use chkla/roberta-argument with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chkla/roberta-argument with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chkla/roberta-argument")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chkla/roberta-argument") model = AutoModelForSequenceClassification.from_pretrained("chkla/roberta-argument", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Joao GanteModel converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=1.192e-06; Maximum crossload hidden layer difference=1.717e-05; Maximum conversion output difference=1.192e-06; Maximum conversion hidden layer difference=1.717e-05;27e1976
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