Instructions to use SCM-LAB/fluency-phobert-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SCM-LAB/fluency-phobert-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SCM-LAB/fluency-phobert-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SCM-LAB/fluency-phobert-v2") model = AutoModelForSequenceClassification.from_pretrained("SCM-LAB/fluency-phobert-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 99f885a1a734e48e385f427f29b3ce9227247a9c72917fc5b67104848d2d1a96
- Size of remote file:
- 3.7 kB
- SHA256:
- a0aecee4943db273e5509fa2af8b49a5c4d1c0d116c371f34b7e16b4d0b74518
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.