Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use PabloAMC/sentiment-model-100-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PabloAMC/sentiment-model-100-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PabloAMC/sentiment-model-100-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PabloAMC/sentiment-model-100-samples") model = AutoModelForSequenceClassification.from_pretrained("PabloAMC/sentiment-model-100-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01194f5fefb1930b59c71b77e6bab59d0c360811479e73704e5bed94b289709b
- Size of remote file:
- 268 MB
- SHA256:
- 658741743f8a450e4aef26bd8dc91f8d5f1d4ef568d16c791760be0f1c8bf25e
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