Instructions to use HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary") model = AutoModelForSequenceClassification.from_pretrained("HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6a16ed6a8384b2413c8d37e35b37ee31473c58125ebc2d0d238a360b9e3be1fa
- Size of remote file:
- 652 MB
- SHA256:
- 71bec5426dd42da83db5b06012c0600160121afb5d846f0ed2591293557b7a57
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.