Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use aiscientist/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aiscientist/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aiscientist/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aiscientist/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("aiscientist/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0971a7e4e1a4e93816408390fd698ec88dd129ce9e8b4ff101f2c74f491a89c3
- Size of remote file:
- 3.45 kB
- SHA256:
- b0a6886f906292a6e30fd47ac10ad0385f7d9cf5d85ff0fdd607cbf6a921b663
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