Instructions to use Youssef320/LSTM-finetuned-50label-2epoch-Reproduced-code-maybe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Youssef320/LSTM-finetuned-50label-2epoch-Reproduced-code-maybe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Youssef320/LSTM-finetuned-50label-2epoch-Reproduced-code-maybe")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Youssef320/LSTM-finetuned-50label-2epoch-Reproduced-code-maybe") model = AutoModelForSequenceClassification.from_pretrained("Youssef320/LSTM-finetuned-50label-2epoch-Reproduced-code-maybe") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4b22b3b7ccc4f270abcd8c0a99d7a3d0518ad7a7bd44c3f9e948c5037925d5e
- Size of remote file:
- 46.5 MB
- SHA256:
- 69c32893a98784d681e69310efdcacac5cf002f33806feb49fa20f83f891a0b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.