Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Jherb/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jherb/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jherb/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jherb/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Jherb/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 379202fffac45e654e9e3080ea24f128de84ad7df3c821a2d53c600de7b5341d
- Size of remote file:
- 3.25 kB
- SHA256:
- 123e7642afc237aea4a3533da001f71e620b4aedd304dd434453ec3b035163e2
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