Text Classification
Transformers
PyTorch
English
roberta
sentiment-analysis
sentiment-classification
targeted-sentiment-classification
target-depentent-sentiment-classification
Instructions to use fhamborg/roberta-targeted-sentiment-classification-newsarticles with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fhamborg/roberta-targeted-sentiment-classification-newsarticles with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fhamborg/roberta-targeted-sentiment-classification-newsarticles")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, PretrainedWrapper tokenizer = AutoTokenizer.from_pretrained("fhamborg/roberta-targeted-sentiment-classification-newsarticles") model = PretrainedWrapper.from_pretrained("fhamborg/roberta-targeted-sentiment-classification-newsarticles", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
7451331
1
Parent(s): ca1bf21
Adding `safetensors` variant of this model
Browse filesThis 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.
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