Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use federicopascual/finetuning-sentiment-analysis-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use federicopascual/finetuning-sentiment-analysis-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="federicopascual/finetuning-sentiment-analysis-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("federicopascual/finetuning-sentiment-analysis-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("federicopascual/finetuning-sentiment-analysis-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ced7f15abdf0a31b688e1820714f231bb417f928d9e44471662c17e58183d29
- Size of remote file:
- 2.99 kB
- SHA256:
- eff4b9c4fa75a7901d9aa646fd8c666b39a4f19c415226e049327f7a0301651d
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