Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use sgraf202/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgraf202/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sgraf202/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sgraf202/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("sgraf202/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65b525933306dd5c20b388adee0c02c1ddaad3ef38f710fb14cefdbb0b85c5c4
- Size of remote file:
- 3.38 kB
- SHA256:
- 662390c1b47263d53aec8ece0e7dbe40fe83f65b21286b3a648cc49b26d3d782
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