Instructions to use moussaKam/barthez-sentiment-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moussaKam/barthez-sentiment-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="moussaKam/barthez-sentiment-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("moussaKam/barthez-sentiment-classification") model = AutoModelForSequenceClassification.from_pretrained("moussaKam/barthez-sentiment-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 592274d94ceaf54ac56a9f1ae77da64e471f8f5959063715e32faeb42cdad323
- Size of remote file:
- 559 MB
- SHA256:
- fe5965bf92934d02a1ea631b6c091be5c12717778fdef4abe51feca150306db6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.