Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use AlphaZetta/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlphaZetta/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AlphaZetta/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AlphaZetta/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("AlphaZetta/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f01769de965da166d47f9d0e08430ee0d82609c67279b090e7ba9cb7108396e1
- Size of remote file:
- 3.25 kB
- SHA256:
- cd75f714c2581e38e894b8204eeb1f6ca174578ff724edced45fb55f5954fe88
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