Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use daniel780/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daniel780/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="daniel780/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("daniel780/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("daniel780/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98727dff03ac70003b1db83208b906719a257352aeafa9088c9641401ac2549e
- Size of remote file:
- 3.25 kB
- SHA256:
- 374745f958d46b0cf1e2794d09eb8b82f01600d64765d630a622bf2e1169b0e7
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