Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use cb2-kai/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cb2-kai/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cb2-kai/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cb2-kai/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("cb2-kai/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea506a1196848b5839f599900e9f6ba1e38f61a2ff70d97c0b7ccfa4b208c971
- Size of remote file:
- 3.06 kB
- SHA256:
- f17efcbab5ea4364f6e24fd78fd4b3d5f7d62b19213e4facb6b47bb2ccff03ac
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