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:
- aaea5f529737ad4e9f451a008bc8db8b65e002e384786c52d6f919967768291a
- Size of remote file:
- 2.22 GB
- SHA256:
- e0a8bd39f2f520684fc33954112f75b65e6c58ef4f3233a10f5a8db9cc82450e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.