Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use azert99/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use azert99/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="azert99/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("azert99/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("azert99/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81d091cfabfbe8021a9b2de19bcc5529dc23b489d0a31a954e5fde2cf6f7c91f
- Size of remote file:
- 268 MB
- SHA256:
- 981251e757feb726f13d0958f5fd63e37f6d7ed151877cc5030fbf3c9a0c9e16
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