Model Card for krishnagarg09/stance-detection-semeval2016

Model Description

The goal is to identify the stance (AGAINST, NONE, FAVOR) of a user towards a given target.

Sample:

Input: Lord, You are my Hope! In You I will always trust.
Target: Atheism
Stance: AGAINST

The model is pretrained on SemEval2016-Task6 stance detection dataset. The dataset is available at https://huggingface.co/datasets/krishnagarg09/SemEval2016Task6.

Ref: https://aclanthology.org/S16-1003/ for more details about the dataset

• Developed by: Krishna Garg
• Shared by [Optional]: Krishna Garg
• Model type: Language model
• Language(s) (NLP): en
• License: mit
• Resources for more information:
  • Associated Paper

Direct Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from datasets import load_dataset

# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("krishnagarg09/stance-detection-semeval2016")
model = AutoModelForSequenceClassification.from_pretrained("krishnagarg09/stance-detection-semeval2016")

# load dataset
dataset = load_dataset("krishnagarg09/SemEval2016Task6")

# prepare input
text = dataset['test']['Tweet']
encoded_input = tokenizer(text, return_tensors='pt', add_special_tokens = True, max_length=128, padding=True, truncation=True)

# forward pass
output = model(**encoded_input)

Dataset

The dataset is available at https://huggingface.co/datasets/krishnagarg09/SemEval2016Task6.

dataset = load_dataset("krishnagarg09/SemEval2016Task6")

Training Details

optimizer: Adam
lr: 2e-5
loss: crossentropy
epochs: 5 (best weights chosen over validation)
batch_size: 32

Preprocessing

Text lowercased, #semst tags removed, p.OPT.URL,p.OPT.EMOJI,p.OPT.RESERVED removed using tweet-preprocessor package, normalization done using emnlp_dict.txt and noslang_data.json

Evaluation

Evaluation for Stance Detection is done only for 2/3 labels, i.e., FAVOR and AGAINST.

Precision: 62.69
Recall: 69.43
F1: 65.56

Hardware

Nvidia RTX A5000 24GB

Model Card Contact

[email protected]