Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use moonzi/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moonzi/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="moonzi/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("moonzi/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("moonzi/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6b1ddcb5ac982c2ed8483193b0384aa7bbaeb7170c0badc7f32a49f7eedaaca
- Size of remote file:
- 3.31 kB
- SHA256:
- dce714a8fafdc4a56652279f879b91adb3a7640e46e8ffe31c79f1b58802e8c9
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