Instructions to use deepset/bert-base-german-cased-sentiment-Germeval17 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/bert-base-german-cased-sentiment-Germeval17 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deepset/bert-base-german-cased-sentiment-Germeval17")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deepset/bert-base-german-cased-sentiment-Germeval17") model = AutoModelForSequenceClassification.from_pretrained("deepset/bert-base-german-cased-sentiment-Germeval17") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a0ef4b7cc7656df892ad458f45ac1be11cdd08f9e4fb24f59aeba9db6ebccb21
- Size of remote file:
- 436 MB
- SHA256:
- 6dcf8e865587973c0ca219f50cf9a5e52d33fab1759eacb0e41d41cdde537b2a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.