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:
- 77ca1ff5fa246accdea32603aada06197870561084389898dd4501351cb60352
- Size of remote file:
- 3.12 kB
- SHA256:
- 46040db7ef5522aad90b4f5858d03b0fba3115841f6a3c369139e7b292d5ada8
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