Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use zboxi7/finetuning-sentiment-model-3000-samples_fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zboxi7/finetuning-sentiment-model-3000-samples_fr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zboxi7/finetuning-sentiment-model-3000-samples_fr")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zboxi7/finetuning-sentiment-model-3000-samples_fr") model = AutoModelForSequenceClassification.from_pretrained("zboxi7/finetuning-sentiment-model-3000-samples_fr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77105d2b0ff808bb6c90c11b550a9fdddc697c97c011119ab187e70210151301
- Size of remote file:
- 268 MB
- SHA256:
- 355718fbf2121ebba0409bfc612a611937828dbb2f242f9cfbabc346f0c9abde
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