Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use olivia371/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use olivia371/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="olivia371/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("olivia371/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("olivia371/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56513abfbf17cab915ee660525bad335b63b865f05303a0dcceb360510d85763
- Size of remote file:
- 268 MB
- SHA256:
- aab3eabb4ec4c77a93b7c1763e9c625755000dd99fcf099f74ff1b4ebd3e1a32
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