Token Classification
Transformers
PyTorch
Catalan
roberta
catalan
part of speech tagging
pos
CaText
Catalan Textual Corpus
Eval Results (legacy)
Instructions to use projecte-aina/roberta-base-ca-cased-pos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use projecte-aina/roberta-base-ca-cased-pos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="projecte-aina/roberta-base-ca-cased-pos")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("projecte-aina/roberta-base-ca-cased-pos") model = AutoModelForTokenClassification.from_pretrained("projecte-aina/roberta-base-ca-cased-pos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from projecte-aina/roberta-base-ca-cased-pos: direct link, hf CLI and curl.
- Browser
- Download file 2.48 kB
-
https://huggingface.co/projecte-aina/roberta-base-ca-cased-pos/resolve/main/training_args.bin
- Command line
-
hf download hf://projecte-aina/roberta-base-ca-cased-pos/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/projecte-aina/roberta-base-ca-cased-pos/resolve/main/training_args.bin
2.48 kB
- Xet hash:
- 1b8bf82e83e68e57fb82cf3b83fb7cb17c3ee592717c0de30999ddd6b60f06b0
- Size of remote file:
- 2.48 kB
- SHA256:
- 4c4ca07a4cfb95ccbc90161bc8bd5268a9e427fc9d3086dcd7ed5949b6eb26c5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.