Text Classification
Transformers
Safetensors
English
roberta
speech-acts
peer-review
sage
text-embeddings-inference
Instructions to use Arys02/roberta-speech-act-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arys02/roberta-speech-act-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Arys02/roberta-speech-act-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Arys02/roberta-speech-act-classifier") model = AutoModelForSequenceClassification.from_pretrained("Arys02/roberta-speech-act-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Arys02/roberta-speech-act-classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.16 kB
-
https://huggingface.co/Arys02/roberta-speech-act-classifier/resolve/main/README.md
- Command line
-
hf download hf://Arys02/roberta-speech-act-classifier/README.md
-
curl -L -o README.md https://huggingface.co/Arys02/roberta-speech-act-classifier/resolve/main/README.md
1.16 kB
metadata
library_name: transformers
pipeline_tag: text-classification
tags:
- roberta
- speech-acts
- peer-review
- sage
language: en
roberta-speech-act-classifier
Speech-act classifier used by SAGE (Subjectivity-Aware Generation Estimator) of generated_text_detection_hub to label each sentence of a review.
- Architecture:
RobertaForSequenceClassification(RoBERTa-base size: 12 layers, hidden size 768, 514 positions), 5 classes. - Labels:
0 Assertion,1 Expressive,2 Neither,3 Question,4 Suggestion. - Input: one sentence (the hub splits reviews with pysbd). Max length 512 tokens.
Provenance and limits
The checkpoint this model was fine-tuned from, the training data, the training procedure and the evaluation metrics are not
documented: config.json has no _name_or_path and the training code is not in the repository. No licence is stated for the
weights. Use it as a component of the hub, not as a validated standalone classifier.
Use
python -m scripts.fetch_sage_models # from the hub repository: downloads and checks both SAGE models into models/