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:
- 39fe78e4ed2166111d4085bb72250ac396145aa811e29cdde391ddcd5de08409
- Size of remote file:
- 268 MB
- SHA256:
- 12c69444e525ef471e1f888f459355b01a2d9b76ddd48776eb16c438ac3fd50c
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