Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use daniel780/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daniel780/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="daniel780/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("daniel780/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("daniel780/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9780bd4e8d76b05407648589c4fcc14718969e3ffe6790a991fc2ccadee212fb
- Size of remote file:
- 268 MB
- SHA256:
- 8acd5d0d8b016c923f0b96e4f7cd1d4a790647ae1d8a93973eaab46eacd17554
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