Text Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Bittar/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bittar/outputs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Bittar/outputs")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Bittar/outputs") model = AutoModelForSequenceClassification.from_pretrained("Bittar/outputs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Bittar/outputs: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Bittar/outputs/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://Bittar/outputs@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Bittar/outputs/resolve/refs%2Fpr%2F1/tokenizer.json
17.1 MB
- Xet hash:
- 420c59e648f53358330b731e433ac701304910c0e36298ab68d54642bc96aa68
- Size of remote file:
- 17.1 MB
- SHA256:
- a26e35f912c57d138bc85d5260014aafe3fcecd697c654fbbcda018708c73996
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