Instructions to use rithwik-db/finetuning-sentiment-model-300-gpu-15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rithwik-db/finetuning-sentiment-model-300-gpu-15 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rithwik-db/finetuning-sentiment-model-300-gpu-15")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rithwik-db/finetuning-sentiment-model-300-gpu-15") model = AutoModelForSequenceClassification.from_pretrained("rithwik-db/finetuning-sentiment-model-300-gpu-15") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 448b2348e474e74ca906e30d18d2e2a70d4c793f666e3ebee194c0179cce999c
- Size of remote file:
- 268 MB
- SHA256:
- 371e3422654d08c02dc33da1c3029458e65ebce8dddb793cb523d44d7eba8878
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.