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:
- 321dc5d7d9d0c636596e2bc5c9dd32e741a13891326d91757b53f3e48abeb182
- Size of remote file:
- 1.11 GB
- SHA256:
- c2973ea8fd45cba029a2f11b0f1bf1a6fa8c5b09cb16bc8e95ca2637b062dc5f
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