Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use dev-ninja/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dev-ninja/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dev-ninja/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dev-ninja/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("dev-ninja/finetuning-sentiment-model-3000-samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6e97fe03a83ac9f5a994638e784663ee21d3ad2f404e9e3357cab3619abfa99
- Size of remote file:
- 268 MB
- SHA256:
- 7a04952d908b49645c960b1512c8192be02e09678f7332bb70027f7318d98f43
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