Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use BaxterAI/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaxterAI/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaxterAI/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaxterAI/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("BaxterAI/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5836550d4cd875c5e33afd7c8430e35b9e479ceda2f8a76843842cba3362cfb1
- Size of remote file:
- 268 MB
- SHA256:
- 4e9574cbbdfc7c63f4383c5658c51a30a09f75222ceed3fd7856814b82c5ce96
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