Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JS21/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JS21/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JS21/finetuning-sentiment-model-3000-samples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JS21/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("JS21/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from JS21/finetuning-sentiment-model-3000-samples: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/JS21/finetuning-sentiment-model-3000-samples/resolve/main/tokenizer.json
- Command line
-
hf download hf://JS21/finetuning-sentiment-model-3000-samples/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/JS21/finetuning-sentiment-model-3000-samples/resolve/main/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.