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 training_args.bin from rianders/finetuning-sentiment-model-3000-samples: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/rianders/finetuning-sentiment-model-3000-samples/resolve/main/training_args.bin
- Command line
-
hf download hf://rianders/finetuning-sentiment-model-3000-samples/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rianders/finetuning-sentiment-model-3000-samples/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 089394b728770e24fa1f9358cc819b1f6eff4f0f78a8fb3625b034e7dd57b2b9
- Size of remote file:
- 4.54 kB
- SHA256:
- 8c4e550a032d8a0e30ed2cc7b18d6e559bb5211ac4ddf768c3f61cc7b3564480
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