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:
- 9b01948dbe140275edb8cff0a0d77eb53a84488ccd2fe14a1bb51c2b7ca9698c
- Size of remote file:
- 268 MB
- SHA256:
- ffbcec830c830be02d0c95d0e320649f8b454c42829f99b59a7355ea45f68b84
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