Instructions to use dfafdsaf/bert_sentiment_5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dfafdsaf/bert_sentiment_5000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dfafdsaf/bert_sentiment_5000")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dfafdsaf/bert_sentiment_5000") model = AutoModelForSequenceClassification.from_pretrained("dfafdsaf/bert_sentiment_5000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf4221dee0834a39227b839273fb514829a7065040f4a5744d9c834eb563b382
- Size of remote file:
- 438 MB
- SHA256:
- bb97cf82d9944fe39833b35c0e144109f2e0f6a3fe3177ac7d62123e39a7ad31
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.