Text Classification
Transformers
Safetensors
bert
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use kartd/bert-base-reward-tw-ideology with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kartd/bert-base-reward-tw-ideology with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kartd/bert-base-reward-tw-ideology")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kartd/bert-base-reward-tw-ideology") model = AutoModelForSequenceClassification.from_pretrained("kartd/bert-base-reward-tw-ideology") - Notebooks
- Google Colab
- Kaggle
Model Card for bert-base-reward-tw-ideology
This model is a fine-tuned version of google-bert/bert-base-uncased. It has been trained using TRL.
Quick start
from transformers import pipeline
text = "The capital of France is Paris."
rewarder = pipeline(model="kartd/bert-base-reward-tw-ideology", device="cuda")
output = rewarder(text)[0]
print(output["score"])
Training procedure
This model was trained with Reward.
Framework versions
- TRL: 1.3.0
- Transformers: 5.6.2
- Pytorch: 2.5.1+cu121
- Datasets: 4.8.4
- Tokenizers: 0.22.2
Citations
Cite TRL as:
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}
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Model tree for kartd/bert-base-reward-tw-ideology
Base model
google-bert/bert-base-uncased