Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use rianders/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rianders/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rianders/finetuning-sentiment-model-3000-samples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rianders/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("rianders/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rianders/finetuning-sentiment-model-3000-samples: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/rianders/finetuning-sentiment-model-3000-samples/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rianders/finetuning-sentiment-model-3000-samples/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rianders/finetuning-sentiment-model-3000-samples/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- f8bf0a843989a428901dd26d455659911e52b4b353aba21284b631b470a2b6e9
- Size of remote file:
- 268 MB
- SHA256:
- a73b4f569dd97b912c45e6fe43173f3ae8ec21930ce258ef8dd7f5ce2207c31d
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