Instructions to use Joshi-Aryan/Fine_Tuned_HF_Language_Identification_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Joshi-Aryan/Fine_Tuned_HF_Language_Identification_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Joshi-Aryan/Fine_Tuned_HF_Language_Identification_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Joshi-Aryan/Fine_Tuned_HF_Language_Identification_Model") model = AutoModelForSequenceClassification.from_pretrained("Joshi-Aryan/Fine_Tuned_HF_Language_Identification_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b2a434ea7a939cfd6d888a3a0dd259165b4f31b306414b3425d899015925ba6
- Size of remote file:
- 2.22 GB
- SHA256:
- d2cfad20cffe487f12be9be3ff681919ced6737908b77141e20e70034f3d413c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.