Instructions to use danitamayo/roberta_language_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danitamayo/roberta_language_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="danitamayo/roberta_language_detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("danitamayo/roberta_language_detection") model = AutoModelForSequenceClassification.from_pretrained("danitamayo/roberta_language_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.4c580de
-
Upload config
25e585f -
Upload tokenizer
4fb5a8c -
Upload RobertaForSequenceClassification
900b746 -
Upload tokenizer
10fe31e -
Upload RobertaForSequenceClassification
aeaf71a -
Create README.md
edb1e2b -
Upload tokenizer
39b11a7 -
Upload RobertaForSequenceClassification
d2900fb -
initial commit
e662c3c