Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Christabel/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Christabel/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Christabel/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Christabel/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Christabel/finetuning-sentiment-model-3000-samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c264eff748bcb6a64366047fb543e0a3d8ef53444df92ffee2c5bec3375ee460
- Size of remote file:
- 3.38 kB
- SHA256:
- d3bab2cab06fe4eb7f8a8426597ae73a48a6b19fe645bb90779f43f170c77aeb
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