Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use yiftach/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yiftach/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yiftach/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yiftach/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("yiftach/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb62edda1f71ac4ecc8d23b45b8446b573b842daf9c9914b419d0ea3aa559cb3
- Size of remote file:
- 268 MB
- SHA256:
- f42b6dc34e9f3b1e67c560adf54f8463bc04132ecef71a970e0a585b5aa15507
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