Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JS21/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JS21/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JS21/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JS21/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("JS21/finetuning-sentiment-model-3000-samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ac62f0edfc7902eb49cc9591ef3672a6e9db2a9241e04e2e52d6d624ba75203
- Size of remote file:
- 268 MB
- SHA256:
- 0287ea2293182f4d6e013bc9d1cee9ca46127a6e4553683acd38a13ad92d7bba
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