Instructions to use SCM-LAB/fluency-phobert-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SCM-LAB/fluency-phobert-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SCM-LAB/fluency-phobert-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SCM-LAB/fluency-phobert-v1") model = AutoModelForSequenceClassification.from_pretrained("SCM-LAB/fluency-phobert-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 10c90400967bc3b45dc17e23e8f65ec93d59e5fdb2e65699ef6193847f0c5b2b
- Size of remote file:
- 1.08 GB
- SHA256:
- 0da444623471617b33ac7ec01b36268080217ee0dcc7fcdc7e7d8ae64ac19579
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