Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use cb2-kai/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cb2-kai/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cb2-kai/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cb2-kai/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("cb2-kai/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd5aeb421c79a819a6916e7173e83f99e90d8d97ecc71dff3cbfdb2e962827fc
- Size of remote file:
- 268 MB
- SHA256:
- 3787542fb78470b27433b3c272ab51608d06db54883ae25dacf70b2dda3c59e1
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