Text Classification
Transformers
PyTorch
Safetensors
English
roberta
emotions
multi-class-classification
multi-label-classification
text-embeddings-inference
Instructions to use Linsad/text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Linsad/text_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Linsad/text_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Linsad/text_classification") model = AutoModelForSequenceClassification.from_pretrained("Linsad/text_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Linsad/text_classification: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/Linsad/text_classification/resolve/main/tokenizer.json
- Command line
-
hf download hf://Linsad/text_classification/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Linsad/text_classification/resolve/main/tokenizer.json
2.11 MB
File too large to display, you can check the raw version instead.