Instructions to use NaeemCSECUET18/banglabert-MLTC-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NaeemCSECUET18/banglabert-MLTC-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NaeemCSECUET18/banglabert-MLTC-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NaeemCSECUET18/banglabert-MLTC-1") model = AutoModelForSequenceClassification.from_pretrained("NaeemCSECUET18/banglabert-MLTC-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e48a5b91c09d65a3b732ea03e5a5ca0841c5ac5cb483657a7e85ef1a4e554e8
- Size of remote file:
- 5.11 kB
- SHA256:
- ab076a28a02109904e6dee5658e2202004bccc80d8d349a06a2a12d9952758d0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.