Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Heer/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Heer/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Heer/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Heer/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Heer/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 99e2b56ddcd950333cd34eb19914a411232ecdfeda31d635995d30ad99c43e14
- Size of remote file:
- 268 MB
- SHA256:
- 56e36e95a5e97ecafde7c798795cf65f36c5079538a8e68db11fabed9833befb
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