Instructions to use Amdalotaibi/fully_supervised_sentiment_model-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amdalotaibi/fully_supervised_sentiment_model-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Amdalotaibi/fully_supervised_sentiment_model-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Amdalotaibi/fully_supervised_sentiment_model-2") model = AutoModelForSequenceClassification.from_pretrained("Amdalotaibi/fully_supervised_sentiment_model-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f77b4c16743c5d795e61f1afc19e2b8d715ac7e00385b3db79ea60be550f6d15
- Size of remote file:
- 5.43 kB
- SHA256:
- eff9cf032b0791e9dc65508ad572046e827d07a2f5f8ecaf11790f88e981d4b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.