Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use amirbr/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amirbr/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amirbr/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amirbr/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("amirbr/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 930cbb4e6ef002689e6ff310940743dd5ecbe980acfe42446eaa6c977170a281
- Size of remote file:
- 3.12 kB
- SHA256:
- d26eaa1b413e0df0a302d8579ef767ea56f8a94a2fa7e567a2f8112245014593
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