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")# pip install -U transformers accelerate # 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
Download training_args.bin from PabloAMC/sentiment-model-100-samples: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/PabloAMC/sentiment-model-100-samples/resolve/main/training_args.bin
- Command line
-
hf download hf://PabloAMC/sentiment-model-100-samples/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/PabloAMC/sentiment-model-100-samples/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- df8aaddff5c7172b1641618e4014ff73ca8e3f086c057dd246ea562bba1c55ed
- Size of remote file:
- 3.45 kB
- SHA256:
- 8a120700ffdae29e18d5829b9540559796c68d1d31577b5981b14aecf1b7bb50
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