Instructions to use vikp/instruct_rater with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikp/instruct_rater with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vikp/instruct_rater")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vikp/instruct_rater") model = AutoModelForSequenceClassification.from_pretrained("vikp/instruct_rater", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4880544ec92fc0c81670652d0b819a3bd3e1cbd0d1356fd991758b23a5a0ab0
- Size of remote file:
- 1.42 GB
- SHA256:
- 65dbc730144d9d14bf494d95a27c21b679c4cdc12766fea42db49e57bf671508
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