Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Abonia/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abonia/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Abonia/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Abonia/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Abonia/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a3755f209065b7ced3d40ff65213b83a48de91786bc26a169f0b1a68a537f28
- Size of remote file:
- 3.31 kB
- SHA256:
- 677588c1d81c8930ec942042397486d32b8226ca90a1b6ad13263601ad21bd60
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