Instructions to use GateNLP/stance-bertweet-target-oblivious with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GateNLP/stance-bertweet-target-oblivious with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GateNLP/stance-bertweet-target-oblivious")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GateNLP/stance-bertweet-target-oblivious") model = AutoModelForSequenceClassification.from_pretrained("GateNLP/stance-bertweet-target-oblivious", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| datasets: | |
| - strombergnlp/rumoureval_2019 | |
| language: | |
| - en | |
| base_model: | |
| - vinai/bertweet-base | |
| # Model Card for Model ID | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| One of a pair of models intended to classify the stance of a "reply" post with respect to the post to which it is replying. This is a _target oblivious_ model, i.e. it considers only the reply itself, not the original target post. [GateNLP/stance-bertweet-target-aware](https://huggingface.co/GateNLP/stance-bertweet-target-aware) is the complementary "target-aware" model, the two models are used in combination in the [GATE Cloud English Stance Classifier](https://cloud.gate.ac.uk/shopfront/displayItem/stance-classification). | |
| For background, see [this paper](https://arxiv.org/pdf/2303.12665). | |