Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use vanja98/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vanja98/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vanja98/finetuning-sentiment-model-3000-samples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vanja98/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("vanja98/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from vanja98/finetuning-sentiment-model-3000-samples: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/vanja98/finetuning-sentiment-model-3000-samples/resolve/main/training_args.bin
- Command line
-
hf download hf://vanja98/finetuning-sentiment-model-3000-samples/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vanja98/finetuning-sentiment-model-3000-samples/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- 4b2d50ab2d0a51fd3fec01a201a1e55b7fc721ad405436a5990f3fbc4057a3ec
- Size of remote file:
- 3.45 kB
- SHA256:
- d1642d3a81616b8e0f84271e1cecff9965eb625bb46ebeedbc80f6c65c61da6f
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