Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use zunicd/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zunicd/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zunicd/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zunicd/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("zunicd/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 076ae54136b86d9d4b73e7a3d267c3e1b05177eff1804617a713fea8cbb98f6b
- Size of remote file:
- 3.31 kB
- SHA256:
- 8319ba109558f9bf5386df48d2649094cb8dcf7dd2c3c12e9382362e7a214b2f
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